diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000000..45232b80ed --- /dev/null +++ b/.clang-format @@ -0,0 +1,161 @@ +--- +Language: Cpp +AlignAfterOpenBracket: Align +AlignArrayOfStructures: Left +AlignConsecutiveAssignments: AcrossComments +AlignConsecutiveBitFields: AcrossComments +AlignConsecutiveDeclarations: AcrossComments +AlignConsecutiveMacros: AcrossComments +# AlignConsecutiveShortCaseStatements: AcrossComments +AlignEscapedNewlines: Left # LeftWithLastLine +AlignOperands: Align +AlignTrailingComments: + Kind: Always + OverEmptyLines: 1 +AllowAllArgumentsOnNextLine: true +AllowAllParametersOfDeclarationOnNextLine: false +# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen +AllowShortBlocksOnASingleLine: Never +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Inline +AllowShortIfStatementsOnASingleLine: Never +AllowShortLambdasOnASingleLine: Inline +AllowShortLoopsOnASingleLine: false +AlwaysBreakBeforeMultilineStrings: true +BinPackArguments: true +BinPackParameters: true # OnePerLine +BitFieldColonSpacing: Both +BreakBeforeBraces: Custom # Attach +BraceWrapping: + AfterCaseLabel: true + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + BeforeLambdaBody: false + BeforeWhile: false + IndentBraces: false + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +# BreakAdjacentStringLiterals: true +BreakAfterAttributes: Never +BreakBeforeBinaryOperators: None +BreakBeforeInlineASMColon: OnlyMultiline +BreakBeforeTernaryOperators: false +# BreakBinaryOperations: Never +BreakConstructorInitializers: AfterColon +# BreakFunctionDefinitionParameters: false +BreakInheritanceList: AfterComma +BreakStringLiterals: true +# BreakTemplateDeclarations: Yes +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: false +DerivePointerAlignment: false +DisableFormat: false +EmptyLineBeforeAccessModifier: Leave +EmptyLineAfterAccessModifier: Never +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +IncludeBlocks: Regroup +IncludeCategories: + - Regex: '^<.*\.h>' + Priority: 1 + SortPriority: 0 + - Regex: '^<.*' + Priority: 2 + SortPriority: 0 + - Regex: '.*' + Priority: 3 + SortPriority: 0 +IncludeIsMainRegex: '([-_](test|unittest))?$' +IncludeIsMainSourceRegex: '' +IndentAccessModifiers: false +IndentCaseBlocks: true +IndentCaseLabels: true +IndentExternBlock: NoIndent +IndentGotoLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 4 +IndentWrappedFunctionNames: false +InsertBraces: true # NOTE: may lead to incorrect formatting +InsertNewlineAtEOF: true +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +LambdaBodyIndentation: Signature +LineEnding: LF +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Auto +ObjCBlockIndentWidth: 4 +ObjCSpaceAfterProperty: true +ObjCSpaceBeforeProtocolList: true +PPIndentWidth: -1 +PackConstructorInitializers: CurrentLine +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Middle +QualifierAlignment: Left +#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict'] +RawStringFormats: + - Language: Cpp + Delimiters: + - cc + - CC + - cpp + - Cpp + - CPP + - 'c++' + - 'C++' + CanonicalDelimiter: '' +ReferenceAlignment: Middle +ReflowComments: false # IndentOnly +SeparateDefinitionBlocks: Always +SortIncludes: CaseInsensitive +SortUsingDeclarations: LexicographicNumeric +SpaceAfterCStyleCast: true +SpaceAfterLogicalNot: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyBlock: false +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 2 +SpacesInAngles: Never +SpacesInContainerLiterals: true +SpacesInLineCommentPrefix: + Minimum: 1 + Maximum: -1 +SpacesInParentheses: false +SpacesInSquareBrackets: false +SpaceBeforeSquareBrackets: false +Standard: c++17 +TabWidth: 4 +UseTab: Never +WhitespaceSensitiveMacros: ['STRINGIZE'] +... + diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index d5acd35e20..05bff1bdf6 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -26,7 +26,7 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) && \ cp build/bin/* . diff --git a/.devops/full-musa.Dockerfile b/.devops/full-musa.Dockerfile index 34ba856d3d..575e81b486 100644 --- a/.devops/full-musa.Dockerfile +++ b/.devops/full-musa.Dockerfile @@ -19,7 +19,7 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) && \ cp build/bin/* . diff --git a/.devops/llama-cli-cann.Dockerfile b/.devops/llama-cli-cann.Dockerfile index db5ba2f25e..02dce501ce 100644 --- a/.devops/llama-cli-cann.Dockerfile +++ b/.devops/llama-cli-cann.Dockerfile @@ -1,6 +1,6 @@ ARG ASCEND_VERSION=8.0.rc2.alpha003-910b-openeuler22.03-py3.8 -FROM cosdt/cann:$ASCEND_VERSION AS build +FROM ascendai/cann:$ASCEND_VERSION AS build WORKDIR /app @@ -22,11 +22,11 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH RUN echo "Building with static libs" && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \ - cmake -B build -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ cmake --build build --config Release --target llama-cli # TODO: use image with NNRT -FROM cosdt/cann:$ASCEND_VERSION AS runtime +FROM ascendai/cann:$ASCEND_VERSION AS runtime COPY --from=build /app/build/bin/llama-cli /llama-cli ENV LC_ALL=C.utf8 diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile index b75163b944..7796891d5b 100644 --- a/.devops/llama-cli-cuda.Dockerfile +++ b/.devops/llama-cli-cuda.Dockerfile @@ -22,16 +22,17 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-cli -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libgomp1 -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so -COPY --from=build /app/build/bin/llama-cli /llama-cli +COPY --from=build /app/lib/ / +COPY --from=build /app/build/bin/llama-cli / ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile index 79dba06a77..0706f732a9 100644 --- a/.devops/llama-cli-intel.Dockerfile +++ b/.devops/llama-cli-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build @@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with static libs" && \ - cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \ cmake --build build --config Release --target llama-cli diff --git a/.devops/llama-cli-musa.Dockerfile b/.devops/llama-cli-musa.Dockerfile index b5696794f1..3372749bee 100644 --- a/.devops/llama-cli-musa.Dockerfile +++ b/.devops/llama-cli-musa.Dockerfile @@ -15,16 +15,17 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-cli -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libgomp1 -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-cli /llama-cli ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile index 9b0dad8bf7..92a6e04793 100644 --- a/.devops/llama-cli-vulkan.Dockerfile +++ b/.devops/llama-cli-vulkan.Dockerfile @@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key # Build it WORKDIR /app COPY . . -RUN cmake -B build -DGGML_VULKAN=1 && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \ cmake --build build --config Release --target llama-cli # Clean up diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile index a40e242057..bf8a198f99 100644 --- a/.devops/llama-server-cuda.Dockerfile +++ b/.devops/llama-server-cuda.Dockerfile @@ -22,16 +22,17 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-server -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev libgomp1 curl -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-server /llama-server # Must be set to 0.0.0.0 so it can listen to requests from host machine diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile index 9c355b664f..b503b8cfe1 100644 --- a/.devops/llama-server-intel.Dockerfile +++ b/.devops/llama-server-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build @@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with dynamic libs" && \ - cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ cmake --build build --config Release --target llama-server FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime diff --git a/.devops/llama-server-musa.Dockerfile b/.devops/llama-server-musa.Dockerfile index 193a6d77cb..eb67201c18 100644 --- a/.devops/llama-server-musa.Dockerfile +++ b/.devops/llama-server-musa.Dockerfile @@ -15,16 +15,17 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-server -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev libgomp1 curl -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-server /llama-server # Must be set to 0.0.0.0 so it can listen to requests from host machine diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile index 93c5e0c26e..6aa7867791 100644 --- a/.devops/llama-server-vulkan.Dockerfile +++ b/.devops/llama-server-vulkan.Dockerfile @@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key # Build it WORKDIR /app COPY . . -RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ cmake --build build --config Release --target llama-server # Clean up diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 5d7d7ea5ae..b88e6ca809 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -126,9 +126,9 @@ effectiveStdenv.mkDerivation (finalAttrs: { }; postPatch = '' - substituteInPlace ./ggml/src/ggml-metal.m \ + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" - substituteInPlace ./ggml/src/ggml-metal.m \ + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";" ''; @@ -173,7 +173,7 @@ effectiveStdenv.mkDerivation (finalAttrs: { (cmakeBool "GGML_NATIVE" false) (cmakeBool "GGML_BLAS" useBlas) (cmakeBool "GGML_CUDA" useCuda) - (cmakeBool "GGML_HIPBLAS" useRocm) + (cmakeBool "GGML_HIP" useRocm) (cmakeBool "GGML_METAL" useMetalKit) (cmakeBool "GGML_VULKAN" useVulkan) (cmakeBool "GGML_STATIC" enableStatic) diff --git a/.editorconfig b/.editorconfig index f88f8da67c..eac38a15f1 100644 --- a/.editorconfig +++ b/.editorconfig @@ -24,6 +24,16 @@ insert_final_newline = unset [examples/server/public/*] indent_size = 2 +[examples/server/public/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + +[examples/server/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + [examples/llama.swiftui/llama.swiftui.xcodeproj/*] indent_style = tab diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml deleted file mode 100644 index 54785854f7..0000000000 --- a/.github/ISSUE_TEMPLATE/01-bug-low.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Low Severity Bugs -description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches) -title: "Bug: " -labels: ["bug-unconfirmed", "low severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml new file mode 100644 index 0000000000..550ee1b498 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml @@ -0,0 +1,73 @@ +name: Bug (compilation) +description: Something goes wrong when trying to compile llama.cpp. +title: "Compile bug: " +labels: ["bug-unconfirmed", "compilation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the compilation of llama.cpp fails. + Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`. + If the compilation succeeds with ccache disabled you should be able to permanently fix the issue + by clearing `~/.cache/ccache` (on Linux). + - type: textarea + id: commit + attributes: + label: Git commit + description: Which commit are you trying to compile? + placeholder: | + $git rev-parse HEAD + 84a07a17b1b08cf2b9747c633a2372782848a27f + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] + multiple: true + - type: textarea + id: steps_to_reproduce + attributes: + label: Steps to Reproduce + description: > + Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us. + placeholder: > + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml new file mode 100644 index 0000000000..1adb162b79 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -0,0 +1,98 @@ +name: Bug (model use) +description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module). +title: "Eval bug: " +labels: ["bug-unconfirmed", "model evaluation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the model evaluation results + (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + The `llama-cli` binary can be used for simple and reproducible model inference. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] + multiple: true + - type: textarea + id: hardware + attributes: + label: Hardware + description: Which CPUs/GPUs are you using? + placeholder: > + e.g. Ryzen 5950X + 2x RTX 4090 + validations: + required: true + - type: textarea + id: model + attributes: + label: Model + description: > + Which model at which quantization were you using when encountering the bug? + If you downloaded a GGUF file off of Huggingface, please provide a link. + placeholder: > + e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M + validations: + required: false + - type: textarea + id: steps_to_reproduce + attributes: + label: Steps to Reproduce + description: > + Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + If you can narrow down the bug to specific hardware, compile flags, or command line arguments, + that information would be very much appreciated by us. + placeholder: > + e.g. when I run llama-cli with -ngl 99 I get garbled outputs. + When I use -ngl 0 it works correctly. + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/.github/ISSUE_TEMPLATE/019-bug-misc.yml new file mode 100644 index 0000000000..124cdee919 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml @@ -0,0 +1,78 @@ +name: Bug (misc.) +description: Something is not working the way it should (and it's not covered by any of the above cases). +title: "Misc. bug: " +labels: ["bug-unconfirmed"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for miscellaneous bugs that don't fit into any other category. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: module + attributes: + label: Which llama.cpp modules do you know to be affected? + multiple: true + options: + - libllama (core library) + - llama-cli + - llama-server + - llama-bench + - llama-quantize + - Python/Bash scripts + - Other (Please specify in the next section) + validations: + required: true + - type: textarea + id: steps_to_reproduce + attributes: + label: Steps to Reproduce + description: > + Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml deleted file mode 100644 index a6285c6f05..0000000000 --- a/.github/ISSUE_TEMPLATE/02-bug-medium.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Medium Severity Bug -description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable) -title: "Bug: " -labels: ["bug-unconfirmed", "medium severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/020-enhancement.yml similarity index 97% rename from .github/ISSUE_TEMPLATE/05-enhancement.yml rename to .github/ISSUE_TEMPLATE/020-enhancement.yml index 58fca73183..02dd4f575a 100644 --- a/.github/ISSUE_TEMPLATE/05-enhancement.yml +++ b/.github/ISSUE_TEMPLATE/020-enhancement.yml @@ -1,5 +1,5 @@ name: Enhancement -description: Used to request enhancements for llama.cpp +description: Used to request enhancements for llama.cpp. title: "Feature Request: " labels: ["enhancement"] body: diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml deleted file mode 100644 index ff816b9376..0000000000 --- a/.github/ISSUE_TEMPLATE/03-bug-high.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: High Severity Bug -description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow) -title: "Bug: " -labels: ["bug-unconfirmed", "high severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/030-research.yml similarity index 97% rename from .github/ISSUE_TEMPLATE/06-research.yml rename to .github/ISSUE_TEMPLATE/030-research.yml index 3ae4e9f8ca..18975dbbfd 100644 --- a/.github/ISSUE_TEMPLATE/06-research.yml +++ b/.github/ISSUE_TEMPLATE/030-research.yml @@ -1,5 +1,5 @@ name: Research -description: Track new technical research area +description: Track new technical research area. title: "Research: " labels: ["research 🔬"] body: diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml deleted file mode 100644 index 7af42a80b3..0000000000 --- a/.github/ISSUE_TEMPLATE/04-bug-critical.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Critical Severity Bug -description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss) -title: "Bug: " -labels: ["bug-unconfirmed", "critical severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/040-refactor.yml similarity index 95% rename from .github/ISSUE_TEMPLATE/07-refactor.yml rename to .github/ISSUE_TEMPLATE/040-refactor.yml index 3a68d3d535..b6e6ab36de 100644 --- a/.github/ISSUE_TEMPLATE/07-refactor.yml +++ b/.github/ISSUE_TEMPLATE/040-refactor.yml @@ -1,5 +1,5 @@ name: Refactor (Maintainers) -description: Used to track refactoring opportunities +description: Used to track refactoring opportunities. title: "Refactor: " labels: ["refactor"] body: diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 423173b975..572f91643f 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -55,7 +55,13 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF .. + cmake .. \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DGGML_RPC=ON \ + -DBUILD_SHARED_LIBS=OFF cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -92,7 +98,7 @@ jobs: name: llama-bin-macos-arm64.zip macOS-latest-cmake-x64: - runs-on: macos-12 + runs-on: macos-13 steps: - name: Clone @@ -113,7 +119,12 @@ jobs: sysctl -a # Metal is disabled due to intermittent failures with Github runners not having a GPU: # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 - cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL=OFF \ + -DGGML_RPC=ON \ + -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -394,15 +405,36 @@ jobs: - name: Build with native CMake HIP support id: cmake_build run: | - cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIPBLAS=ON + cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON cmake --build build --config Release -j $(nproc) - name: Build with legacy HIP support id: cmake_build_legacy_hip run: | - cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIPBLAS=ON + cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON cmake --build build2 --config Release -j $(nproc) + ubuntu-22-cmake-musa: + runs-on: ubuntu-22.04 + container: mthreads/musa:rc3.1.0-devel-ubuntu22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + apt-get update + apt-get install -y build-essential git cmake libcurl4-openssl-dev + + - name: Build with native CMake MUSA support + id: cmake_build + run: | + cmake -B build -S . -DGGML_MUSA=ON + cmake --build build --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 @@ -569,6 +601,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ @@ -599,6 +632,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ @@ -734,7 +768,7 @@ jobs: id: clone_kompute if: ${{ matrix.build == 'kompute-x64' }} run: | - git submodule update --init ggml/src/kompute + git submodule update --init ggml/src/ggml-kompute/kompute - name: Download OpenBLAS id: get_openblas @@ -917,7 +951,7 @@ jobs: shell: bash env: - WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7dff44ba-e3af-4448-841c-0d616c8da6e7/w_BaseKit_p_2024.1.0.595_offline.exe + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI" steps: @@ -952,13 +986,14 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin" - cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.4.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_win_proxy_loader.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_level_zero.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin @@ -1001,7 +1036,7 @@ jobs: run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} windows-latest-cmake-hip-release: @@ -1037,7 +1072,7 @@ jobs: run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} md "build\bin\rocblas\library\" cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" diff --git a/.gitignore b/.gitignore index 1092d097a7..307c065f79 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ *.a *.bat *.bin +*.d *.dll *.dot *.etag @@ -133,3 +134,7 @@ poetry.toml # Test models for lora adapters /lora-tests + +# Local scripts +/run-vim.sh +/run-chat.sh diff --git a/.gitmodules b/.gitmodules index 5861d59cb7..23ce5ff059 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "kompute"] - path = ggml/src/kompute + path = ggml/src/ggml-kompute/kompute url = https://github.com/nomic-ai/kompute.git diff --git a/CMakeLists.txt b/CMakeLists.txt index 64a335378e..994e61e45f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -46,6 +46,13 @@ if (WIN32) add_compile_definitions(_CRT_SECURE_NO_WARNINGS) endif() +if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC") + add_compile_options("$<$:/source-charset:utf-8>") + add_compile_options("$<$:/source-charset:utf-8>") + add_compile_options("$<$:/execution-charset:utf-8>") + add_compile_options("$<$:/execution-charset:utf-8>") +endif() + # # option list # @@ -88,6 +95,10 @@ if (NOT DEFINED GGML_LLAMAFILE) set(GGML_LLAMAFILE_DEFAULT ON) endif() +if (NOT DEFINED GGML_AMX) + set(GGML_AMX ON) +endif() + if (NOT DEFINED GGML_CUDA_GRAPHS) set(GGML_CUDA_GRAPHS_DEFAULT ON) endif() @@ -136,7 +147,6 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files") set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files") - # At the moment some compile definitions are placed within the ggml/src # directory but not exported on the `ggml` target. This could be improved by # determining _precisely_ which defines are necessary for the llama-config diff --git a/CMakePresets.json b/CMakePresets.json index d22ffa4909..436448967c 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -24,11 +24,12 @@ "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." } }, - { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, - { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, - { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, - { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, - { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, + { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, + { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } }, { "name": "arm64-windows-msvc", "hidden": true, @@ -48,21 +49,37 @@ } }, - { "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, - { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] }, - { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] }, + { + "name": "arm64-apple-clang", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake" + } + }, - { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, + { "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] }, + + { "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] }, + { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] }, + { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] }, + + { "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] }, { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] }, - { "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] }, { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] }, { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] }, - { "name": "x64-windows-sycl-debug" , "inherits": [ "sycl-base", "debug" ] }, + { "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] }, { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] }, { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }, - { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] } + { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }, + + { "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] }, + { "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] } ] } diff --git a/Makefile b/Makefile index 2793978c3e..5c89943851 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,6 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ libllava.a \ - llama-baby-llama \ llama-batched \ llama-batched-bench \ llama-bench \ @@ -34,6 +33,7 @@ BUILD_TARGETS = \ llama-save-load-state \ llama-server \ llama-simple \ + llama-simple-chat \ llama-speculative \ llama-tokenize \ llama-vdot \ @@ -48,14 +48,12 @@ TEST_TARGETS = \ tests/test-backend-ops \ tests/test-chat-template \ tests/test-double-float \ - tests/test-grad0 \ tests/test-grammar-integration \ tests/test-grammar-parser \ tests/test-json-schema-to-grammar \ tests/test-llama-grammar \ tests/test-log \ tests/test-model-load-cancel \ - tests/test-opt \ tests/test-quantize-fns \ tests/test-quantize-perf \ tests/test-rope \ @@ -63,6 +61,7 @@ TEST_TARGETS = \ tests/test-tokenizer-0 \ tests/test-tokenizer-1-bpe \ tests/test-tokenizer-1-spm +# tests/test-opt \ # Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \ @@ -93,11 +92,6 @@ GGML_METAL := 1 DEPRECATE_WARNING := 1 endif -ifdef LLAMA_OPENMP -GGML_OPENMP := 1 -DEPRECATE_WARNING := 1 -endif - ifdef LLAMA_RPC GGML_RPC := 1 DEPRECATE_WARNING := 1 @@ -364,6 +358,10 @@ ifdef LLAMA_SERVER_SSL MK_LDFLAGS += -lssl -lcrypto endif +ifndef GGML_NO_CPU_AARCH64 + MK_CPPFLAGS += -DGGML_USE_CPU_AARCH64 +endif + # warnings WARN_FLAGS = \ -Wall \ @@ -528,65 +526,59 @@ ifndef GGML_NO_ACCELERATE # Mac OS - include Accelerate framework. # `-framework Accelerate` works both with Apple Silicon and Mac Intel ifeq ($(UNAME_S),Darwin) - MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS - MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK - MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 - MK_LDFLAGS += -framework Accelerate - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE + MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK + MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 + MK_LDFLAGS += -framework Accelerate + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif endif # GGML_NO_ACCELERATE -ifdef GGML_MUSA - CC := clang - CXX := clang++ - GGML_CUDA := 1 - MK_CPPFLAGS += -DGGML_USE_MUSA -endif - ifndef GGML_NO_OPENMP MK_CPPFLAGS += -DGGML_USE_OPENMP MK_CFLAGS += -fopenmp MK_CXXFLAGS += -fopenmp - ifdef GGML_MUSA - MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp - MK_LDFLAGS += -L/usr/lib/llvm-10/lib - endif # GGML_MUSA endif # GGML_NO_OPENMP ifdef GGML_OPENBLAS - MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) - MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) - MK_LDFLAGS += $(shell pkg-config --libs openblas) - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_OPENBLAS ifdef GGML_OPENBLAS64 - MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) - MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) - MK_LDFLAGS += $(shell pkg-config --libs openblas64) - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) + MK_LDFLAGS += $(shell pkg-config --libs openblas64) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_OPENBLAS64 ifdef GGML_BLIS - MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis - MK_LDFLAGS += -lblis -L/usr/local/lib - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_BLIS ifdef GGML_NVPL - MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas - MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas + MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_NVPL ifndef GGML_NO_LLAMAFILE - MK_CPPFLAGS += -DGGML_USE_LLAMAFILE - OBJ_GGML += ggml/src/llamafile/sgemm.o + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE + OBJ_GGML_EXT += ggml/src/ggml-cpu/llamafile/sgemm.o +endif + +ifndef GGML_NO_AMX + MK_CPPFLAGS += -DGGML_USE_AMX + OBJ_GGML_EXT += ggml/src/ggml-amx/ggml-amx.o ggml/src/ggml-amx/mmq.o endif ifdef GGML_RPC - MK_CPPFLAGS += -DGGML_USE_RPC - OBJ_GGML += ggml/src/ggml-rpc.o + MK_CPPFLAGS += -DGGML_USE_RPC + OBJ_GGML_EXT += ggml/src/ggml-rpc.o endif # GGML_RPC OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu)) @@ -601,41 +593,27 @@ else endif # GGML_CUDA_FA_ALL_QUANTS ifdef GGML_CUDA - ifdef GGML_MUSA - ifneq ('', '$(wildcard /opt/musa)') - CUDA_PATH ?= /opt/musa - else - CUDA_PATH ?= /usr/local/musa - endif - - MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include - MK_LDFLAGS += -lmusa -lmublas -lmusart -lpthread -ldl -lrt -L$(CUDA_PATH)/lib -L/usr/lib64 - MK_NVCCFLAGS += -x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22 + ifneq ('', '$(wildcard /opt/cuda)') + CUDA_PATH ?= /opt/cuda else - ifneq ('', '$(wildcard /opt/cuda)') - CUDA_PATH ?= /opt/cuda - else - CUDA_PATH ?= /usr/local/cuda - endif + CUDA_PATH ?= /usr/local/cuda + endif - MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include - MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib - MK_NVCCFLAGS += -use_fast_math - endif # GGML_MUSA + MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib + MK_NVCCFLAGS += -use_fast_math - OBJ_GGML += ggml/src/ggml-cuda.o - OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) - OBJ_GGML += $(OBJ_CUDA_TMPL) + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) ifdef LLAMA_FATAL_WARNINGS MK_NVCCFLAGS += -Werror all-warnings endif # LLAMA_FATAL_WARNINGS -ifndef GGML_MUSA ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT -endif # GGML_MUSA ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo @@ -648,11 +626,7 @@ endif # GGML_CUDA_DEBUG ifdef GGML_CUDA_NVCC NVCC = $(CCACHE) $(GGML_CUDA_NVCC) else - ifdef GGML_MUSA - NVCC = $(CCACHE) mcc - else - NVCC = $(CCACHE) nvcc - endif # GGML_MUSA + NVCC = $(CCACHE) nvcc endif # GGML_CUDA_NVCC ifdef CUDA_DOCKER_ARCH @@ -661,10 +635,6 @@ else ifndef CUDA_POWER_ARCH MK_NVCCFLAGS += -arch=native endif # CUDA_DOCKER_ARCH -ifdef GGML_CUDA_FORCE_DMMV - MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # GGML_CUDA_FORCE_DMMV - ifdef GGML_CUDA_FORCE_MMQ MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ endif # GGML_CUDA_FORCE_MMQ @@ -673,20 +643,6 @@ ifdef GGML_CUDA_FORCE_CUBLAS MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS endif # GGML_CUDA_FORCE_CUBLAS -ifdef GGML_CUDA_DMMV_X - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X) -else - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32 -endif # GGML_CUDA_DMMV_X - -ifdef GGML_CUDA_MMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y) -else ifdef GGML_CUDA_DMMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_DMMV_Y) # for backwards compatibility -else - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1 -endif # GGML_CUDA_MMV_Y - ifdef GGML_CUDA_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 endif # GGML_CUDA_F16 @@ -695,12 +651,6 @@ ifdef GGML_CUDA_DMMV_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 endif # GGML_CUDA_DMMV_F16 -ifdef GGML_CUDA_KQUANTS_ITER - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER) -else - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2 -endif - ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) else @@ -724,15 +674,9 @@ define NVCC_COMPILE $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endef # NVCC_COMPILE else - ifdef GGML_MUSA -define NVCC_COMPILE - $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -c $< -o $@ -endef # NVCC_COMPILE - else define NVCC_COMPILE $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endef # NVCC_COMPILE - endif # GGML_MUSA endif # JETSON_EOL_MODULE_DETECT ggml/src/ggml-cuda/%.o: \ @@ -742,8 +686,8 @@ ggml/src/ggml-cuda/%.o: \ ggml/src/ggml-cuda/common.cuh $(NVCC_COMPILE) -ggml/src/ggml-cuda.o: \ - ggml/src/ggml-cuda.cu \ +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ ggml/include/ggml-cuda.h \ ggml/include/ggml.h \ ggml/include/ggml-backend.h \ @@ -754,9 +698,9 @@ ggml/src/ggml-cuda.o: \ endif # GGML_CUDA ifdef GGML_VULKAN - MK_CPPFLAGS += -DGGML_USE_VULKAN - MK_LDFLAGS += $(shell pkg-config --libs vulkan) - OBJ_GGML += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o + MK_CPPFLAGS += -DGGML_USE_VULKAN + MK_LDFLAGS += $(shell pkg-config --libs vulkan) + OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o ifdef GGML_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS @@ -786,10 +730,10 @@ GLSLC_CMD = glslc _ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen _ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp _ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp -_ggml_vk_input_dir = ggml/src/vulkan-shaders +_ggml_vk_input_dir = ggml/src/ggml-vulkan/vulkan-shaders _ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp) -ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source) +ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source) $(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@ $(_ggml_vk_header): $(_ggml_vk_source) @@ -801,8 +745,8 @@ $(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen --target-hpp $(_ggml_vk_header) \ --target-cpp $(_ggml_vk_source) -vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp - $(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp + $(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp endif # GGML_VULKAN @@ -815,11 +759,7 @@ ifdef GGML_HIPBLAS AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - GGML_CUDA_DMMV_X ?= 32 - GGML_CUDA_MMV_Y ?= 1 - GGML_CUDA_KQUANTS_ITER ?= 2 - - MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA + MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA ifdef GGML_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA @@ -832,13 +772,6 @@ endif # GGML_HIP_UMA HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) - HIPFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X) - HIPFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y) - HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER) - -ifdef GGML_CUDA_FORCE_DMMV - HIPFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # GGML_CUDA_FORCE_DMMV ifdef GGML_CUDA_FORCE_MMQ HIPFLAGS += -DGGML_CUDA_FORCE_MMQ @@ -852,12 +785,12 @@ ifdef GGML_CUDA_NO_PEER_COPY HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY endif # GGML_CUDA_NO_PEER_COPY - OBJ_GGML += ggml/src/ggml-cuda.o - OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) - OBJ_GGML += $(OBJ_CUDA_TMPL) + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) -ggml/src/ggml-cuda.o: \ - ggml/src/ggml-cuda.cu \ +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ ggml/include/ggml-cuda.h \ ggml/include/ggml.h \ ggml/include/ggml-backend.h \ @@ -874,70 +807,167 @@ ggml/src/ggml-cuda/%.o: \ $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< endif # GGML_HIPBLAS +ifdef GGML_MUSA + ifeq ($(wildcard /opt/musa),) + MUSA_PATH ?= /usr/local/musa + else + MUSA_PATH ?= /opt/musa + endif + MTGPU_TARGETS ?= mp_21 mp_22 + + MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA + MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib + MK_LDFLAGS += -lmusa -lmusart -lmublas + + ifndef GGML_NO_OPENMP + # For Ubuntu Focal + MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp + MK_LDFLAGS += -L/usr/lib/llvm-10/lib + # For Ubuntu Jammy + MK_CPPFLAGS += -I/usr/lib/llvm-14/lib/clang/14.0.0/include + MK_LDFLAGS += -L/usr/lib/llvm-14/lib + endif # GGML_NO_OPENMP + + CC := $(MUSA_PATH)/bin/clang + CXX := $(MUSA_PATH)/bin/clang++ + MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc + + MUSAFLAGS += $(addprefix --cuda-gpu-arch=, $(MTGPU_TARGETS)) + +ifdef GGML_CUDA_FORCE_MMQ + MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + MUSAFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_F16 + +ifdef GGML_CUDA_DMMV_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_DMMV_F16 + +ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) +else + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 +endif # GGML_CUDA_PEER_MAX_BATCH_SIZE + +ifdef GGML_CUDA_NO_PEER_COPY + MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_FA_ALL_QUANTS + MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # GGML_CUDA_FA_ALL_QUANTS + + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) + +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ + ggml/include/ggml-cuda.h \ + ggml/include/ggml.h \ + ggml/include/ggml-backend.h \ + ggml/src/ggml-backend-impl.h \ + ggml/src/ggml-common.h \ + $(wildcard ggml/src/ggml-cuda/*.cuh) + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $< + +ggml/src/ggml-cuda/%.o: \ + ggml/src/ggml-cuda/%.cu \ + ggml/include/ggml.h \ + ggml/src/ggml-common.h \ + ggml/src/ggml-cuda/common.cuh + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $< +endif # GGML_MUSA + ifdef GGML_METAL - MK_CPPFLAGS += -DGGML_USE_METAL - MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJ_GGML += ggml/src/ggml-metal.o + MK_CPPFLAGS += -DGGML_USE_METAL + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJ_GGML_EXT += ggml/src/ggml-metal/ggml-metal.o + +ifdef GGML_METAL_USE_BF16 + MK_CPPFLAGS += -DGGML_METAL_USE_BF16 +endif # GGML_METAL_USE_BF16 ifdef GGML_METAL_NDEBUG MK_CPPFLAGS += -DGGML_METAL_NDEBUG endif ifdef GGML_METAL_EMBED_LIBRARY - MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY - OBJ_GGML += ggml/src/ggml-metal-embed.o + MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY + OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o endif endif # GGML_METAL ifdef GGML_METAL -ggml/src/ggml-metal.o: \ - ggml/src/ggml-metal.m \ +ggml/src/ggml-metal/ggml-metal.o: \ + ggml/src/ggml-metal/ggml-metal.m \ + ggml/src/ggml-metal/ggml-metal-impl.h \ ggml/include/ggml-metal.h \ ggml/include/ggml.h $(CC) $(CFLAGS) -c $< -o $@ ifdef GGML_METAL_EMBED_LIBRARY ggml/src/ggml-metal-embed.o: \ - ggml/src/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal-impl.h \ ggml/src/ggml-common.h @echo "Embedding Metal library" - @sed -e '/#include "ggml-common.h"/r ggml/src/ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml/src/ggml-metal.metal > ggml/src/ggml-metal-embed.metal + @sed -e '/__embed_ggml-common.h__/r ggml/src/ggml-common.h' -e '/__embed_ggml-common.h__/d' < ggml/src/ggml-metal/ggml-metal.metal > ggml/src/ggml-metal/ggml-metal-embed.metal.tmp + @sed -e '/#include "ggml-metal-impl.h"/r ggml/src/ggml-metal/ggml-metal-impl.h' -e '/#include "ggml-metal-impl.h"/d' < ggml/src/ggml-metal/ggml-metal-embed.metal.tmp > ggml/src/ggml-metal/ggml-metal-embed.metal $(eval TEMP_ASSEMBLY=$(shell mktemp -d)) - @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".incbin \"ggml/src/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".incbin \"ggml/src/ggml-metal/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s $(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@ @rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s @rmdir ${TEMP_ASSEMBLY} endif endif # GGML_METAL -OBJ_GGML += \ - ggml/src/ggml.o \ - ggml/src/ggml-alloc.o \ - ggml/src/ggml-backend.o \ - ggml/src/ggml-quants.o \ - ggml/src/ggml-aarch64.o +DIR_GGML = ggml +DIR_LLAMA = src +DIR_COMMON = common + +OBJ_GGML = \ + $(DIR_GGML)/src/ggml.o \ + $(DIR_GGML)/src/ggml-aarch64.o \ + $(DIR_GGML)/src/ggml-alloc.o \ + $(DIR_GGML)/src/ggml-backend.o \ + $(DIR_GGML)/src/ggml-backend-reg.o \ + $(DIR_GGML)/src/ggml-opt.o \ + $(DIR_GGML)/src/ggml-quants.o \ + $(DIR_GGML)/src/ggml-threading.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \ + $(OBJ_GGML_EXT) OBJ_LLAMA = \ - src/llama.o \ - src/llama-vocab.o \ - src/llama-grammar.o \ - src/llama-sampling.o \ - src/unicode.o \ - src/unicode-data.o + $(DIR_LLAMA)/llama.o \ + $(DIR_LLAMA)/llama-vocab.o \ + $(DIR_LLAMA)/llama-grammar.o \ + $(DIR_LLAMA)/llama-sampling.o \ + $(DIR_LLAMA)/unicode.o \ + $(DIR_LLAMA)/unicode-data.o OBJ_COMMON = \ - common/common.o \ - common/arg.o \ - common/log.o \ - common/console.o \ - common/ngram-cache.o \ - common/sampling.o \ - common/train.o \ - common/build-info.o \ - common/json-schema-to-grammar.o + $(DIR_COMMON)/common.o \ + $(DIR_COMMON)/arg.o \ + $(DIR_COMMON)/log.o \ + $(DIR_COMMON)/console.o \ + $(DIR_COMMON)/ngram-cache.o \ + $(DIR_COMMON)/sampling.o \ + $(DIR_COMMON)/build-info.o \ + $(DIR_COMMON)/json-schema-to-grammar.o OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON) @@ -993,7 +1023,6 @@ $(info I CXX: $(shell $(CXX) --version | head -n 1)) ifdef GGML_CUDA $(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])') -ifndef GGML_MUSA ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) ifndef CUDA_DOCKER_ARCH @@ -1003,7 +1032,6 @@ endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) -endif # GGML_MUSA endif # GGML_CUDA $(info ) @@ -1040,209 +1068,78 @@ endif # Build libraries # -# ggml +# Libraries +LIB_GGML = libggml.so +LIB_GGML_S = libggml.a -ggml/src/ggml.o: \ - ggml/src/ggml.c \ - ggml/include/ggml.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_LLAMA = libllama.so +LIB_LLAMA_S = libllama.a -ggml/src/ggml-alloc.o: \ - ggml/src/ggml-alloc.c \ - ggml/include/ggml.h \ - ggml/include/ggml-alloc.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_COMMON = libcommon.so +LIB_COMMON_S = libcommon.a -ggml/src/ggml-backend.o: \ - ggml/src/ggml-backend.cpp \ - ggml/src/ggml-backend-impl.h \ - ggml/include/ggml.h \ - ggml/include/ggml-backend.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Targets +BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S) -ggml/src/ggml-quants.o: \ - ggml/src/ggml-quants.c \ - ggml/include/ggml.h \ - ggml/src/ggml-quants.h \ - ggml/src/ggml-common.h - $(CC) $(CFLAGS) -c $< -o $@ +# Dependency files +DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d) -ggml/src/ggml-aarch64.o: \ - ggml/src/ggml-aarch64.c \ - ggml/include/ggml.h \ - ggml/src/ggml-aarch64.h \ - ggml/src/ggml-common.h - $(CC) $(CFLAGS) -c $< -o $@ +# Default target +all: $(BUILD_TARGETS) -ggml/src/ggml-blas.o: \ - ggml/src/ggml-blas.cpp \ - ggml/include/ggml-blas.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -ifndef GGML_NO_LLAMAFILE -ggml/src/llamafile/sgemm.o: \ - ggml/src/llamafile/sgemm.cpp \ - ggml/src/llamafile/sgemm.h \ - ggml/include/ggml.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # GGML_NO_LLAMAFILE - -ifdef GGML_RPC -ggml/src/ggml-rpc.o: \ - ggml/src/ggml-rpc.cpp \ - ggml/include/ggml-rpc.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # GGML_RPC - -$(LIB_GGML): \ - $(OBJ_GGML) - $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) - -$(LIB_GGML_S): \ - $(OBJ_GGML) - ar rcs $(LIB_GGML_S) $^ - -# llama - -src/unicode.o: \ - src/unicode.cpp \ - src/unicode.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -src/unicode-data.o: \ - src/unicode-data.cpp \ - src/unicode-data.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -src/llama.o: \ - src/llama.cpp \ - src/llama-impl.h \ - src/llama-vocab.h \ - src/llama-grammar.h \ - src/llama-sampling.h \ - src/unicode.h \ - include/llama.h \ - ggml/include/ggml-cuda.h \ - ggml/include/ggml-metal.h \ +# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files +# g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml +$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \ + ggml/src/ggml-cpu/ggml-cpu.cpp \ + ggml/include/ggml-backend.h \ ggml/include/ggml.h \ ggml/include/ggml-alloc.h \ - ggml/include/ggml-backend.h - $(CXX) $(CXXFLAGS) -c $< -o $@ + ggml/src/ggml-backend-impl.h \ + ggml/include/ggml-cpu.h \ + ggml/src/ggml-impl.h + $(CXX) $(CXXFLAGS) -c $< -o $@ -src/llama-vocab.o: \ - src/llama-vocab.cpp \ - src/llama-vocab.h \ - src/llama-impl.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Rules for building object files +$(DIR_GGML)/%.o: $(DIR_GGML)/%.c + $(CC) $(CFLAGS) -MMD -c $< -o $@ -src/llama-grammar.o: \ - src/llama-grammar.cpp \ - src/llama-grammar.h \ - src/llama-impl.h \ - src/llama-vocab.h \ - src/llama-sampling.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -src/llama-sampling.o: \ - src/llama-sampling.cpp \ - src/llama-sampling.h \ - src/llama-impl.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -$(LIB_LLAMA): \ - $(OBJ_LLAMA) \ - $(LIB_GGML) +$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +# Rules for building libraries +$(LIB_GGML): $(OBJ_GGML) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) -$(LIB_LLAMA_S): \ - $(OBJ_LLAMA) +$(LIB_GGML_S): $(OBJ_GGML) + ar rcs $(LIB_GGML_S) $^ + +$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_LLAMA_S): $(OBJ_LLAMA) ar rcs $(LIB_LLAMA_S) $^ -# common - -common/common.o: \ - common/common.cpp \ - common/common.h \ - common/console.h \ - common/sampling.h \ - common/json.hpp \ - common/json-schema-to-grammar.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/arg.o: \ - common/arg.cpp \ - common/arg.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/log.o: \ - common/log.cpp \ - common/log.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/sampling.o: \ - common/sampling.cpp \ - common/sampling.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/console.o: \ - common/console.cpp \ - common/console.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/json-schema-to-grammar.o: \ - common/json-schema-to-grammar.cpp \ - common/json-schema-to-grammar.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/train.o: \ - common/train.cpp \ - common/train.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/ngram-cache.o: \ - common/ngram-cache.cpp \ - common/ngram-cache.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -$(LIB_COMMON): \ - $(OBJ_COMMON) \ - $(LIB_LLAMA) \ - $(LIB_GGML) +$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) -$(LIB_COMMON_S): \ - $(OBJ_COMMON) +$(LIB_COMMON_S): $(OBJ_COMMON) ar rcs $(LIB_COMMON_S) $^ +# Include dependency files +-include $(DEP_FILES) + +# Clean rule clean: - rm -vrf *.dot $(BUILD_TARGETS) $(TEST_TARGETS) - rm -rvf src/*.o - rm -rvf tests/*.o - rm -rvf examples/*.o - rm -rvf common/*.o - rm -rvf *.a - rm -rvf *.dll - rm -rvf *.so - rm -rvf *.dot - rm -rvf ggml/*.a - rm -rvf ggml/*.dll - rm -rvf ggml/*.so - rm -vrf ggml/src/*.o - rm -rvf ggml/src/llamafile/*.o - rm -rvf common/build-info.cpp - rm -vrf ggml/src/ggml-metal-embed.metal - rm -vrf ggml/src/ggml-cuda/*.o - rm -vrf ggml/src/ggml-cuda/template-instances/*.o - rm -rvf $(BUILD_TARGETS) - rm -rvf $(TEST_TARGETS) - rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp - rm -rvf $(LEGACY_TARGETS_CLEAN) - find examples pocs -type f -name "*.o" -delete + rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS) + rm -rvf *.a *.dll *.so *.dot + find ggml src common tests examples pocs -type f -name "*.o" -delete + find ggml src common tests examples pocs -type f -name "*.d" -delete # # Examples @@ -1273,6 +1170,11 @@ llama-simple: examples/simple/simple.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-simple-chat: examples/simple-chat/simple-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-tokenize: examples/tokenize/tokenize.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1370,11 +1272,6 @@ llama-bench: examples/llama-bench/llama-bench.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-baby-llama: examples/baby-llama/baby-llama.cpp \ - $(OBJ_ALL) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - llama-export-lora: examples/export-lora/export-lora.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1440,22 +1337,13 @@ llama-server: \ examples/server/server.cpp \ examples/server/utils.hpp \ examples/server/httplib.h \ - examples/server/colorthemes.css.hpp \ - examples/server/style.css.hpp \ - examples/server/theme-beeninorder.css.hpp \ - examples/server/theme-ketivah.css.hpp \ - examples/server/theme-mangotango.css.hpp \ - examples/server/theme-playground.css.hpp \ - examples/server/theme-polarnight.css.hpp \ - examples/server/theme-snowstorm.css.hpp \ examples/server/index.html.hpp \ - examples/server/index-new.html.hpp \ - examples/server/index.js.hpp \ examples/server/completion.js.hpp \ - examples/server/system-prompts.js.hpp \ - examples/server/prompt-formats.js.hpp \ - examples/server/json-schema-to-grammar.mjs.hpp \ examples/server/loading.html.hpp \ + examples/server/deps_daisyui.min.css.hpp \ + examples/server/deps_markdown-it.js.hpp \ + examples/server/deps_tailwindcss.js.hpp \ + examples/server/deps_vue.esm-browser.js.hpp \ common/json.hpp \ common/stb_image.h \ $(OBJ_ALL) @@ -1557,11 +1445,6 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \ $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-grad0: tests/test-grad0.cpp \ - $(OBJ_GGML) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - tests/test-opt: tests/test-opt.cpp \ $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/Package.swift b/Package.swift index 3a17e6c349..6b68aecdeb 100644 --- a/Package.swift +++ b/Package.swift @@ -10,10 +10,16 @@ var sources = [ "src/unicode.cpp", "src/unicode-data.cpp", "ggml/src/ggml.c", + "ggml/src/ggml-aarch64.c", "ggml/src/ggml-alloc.c", "ggml/src/ggml-backend.cpp", + "ggml/src/ggml-backend-reg.cpp", + "ggml/src/ggml-cpu/ggml-cpu.c", + "ggml/src/ggml-cpu/ggml-cpu.cpp", + "ggml/src/ggml-cpu/ggml-cpu-aarch64.c", + "ggml/src/ggml-cpu/ggml-cpu-quants.c", + "ggml/src/ggml-threading.cpp", "ggml/src/ggml-quants.c", - "ggml/src/ggml-aarch64.c", ] var resources: [Resource] = [] @@ -21,6 +27,7 @@ var linkerSettings: [LinkerSetting] = [] var cSettings: [CSetting] = [ .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), .unsafeFlags(["-fno-objc-arc"]), + .headerSearchPath("ggml/src"), // NOTE: NEW_LAPACK will required iOS version 16.4+ // We should consider add this in the future when we drop support for iOS 14 // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) @@ -29,8 +36,9 @@ var cSettings: [CSetting] = [ ] #if canImport(Darwin) -sources.append("ggml/src/ggml-metal.m") -resources.append(.process("ggml/src/ggml-metal.metal")) +sources.append("ggml/src/ggml-common.h") +sources.append("ggml/src/ggml-metal/ggml-metal.m") +resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal")) linkerSettings.append(.linkedFramework("Accelerate")) cSettings.append( contentsOf: [ @@ -60,13 +68,15 @@ let package = Package( name: "llama", path: ".", exclude: [ + "build", "cmake", "examples", "scripts", "models", "tests", "CMakeLists.txt", - "Makefile" + "Makefile", + "ggml/src/ggml-metal-embed.metal" ], sources: sources, resources: resources, diff --git a/README.md b/README.md index dd4927b040..5f7933c132 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Hot topics -- **Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669** +- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123 +- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669 - Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) ---- @@ -29,7 +30,7 @@ variety of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks -- AVX, AVX2 and AVX512 support for x86 architectures +- AVX, AVX2, AVX512 and AMX support for x86 architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA) - Vulkan and SYCL backend support @@ -93,6 +94,7 @@ Typically finetunes of the base models below are supported as well. - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) +- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) (instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md)) @@ -122,14 +124,18 @@ Typically finetunes of the base models below are supported as well. - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) +- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html) - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) +- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama) - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) - Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) +- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) +- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) **UI:** @@ -170,6 +176,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) - [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) - [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* @@ -185,6 +192,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs +- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly **Games:** - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. @@ -451,14 +459,14 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio - Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205) - A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) -## Other documentations +## Other documentation - [main (cli)](./examples/main/README.md) - [server](./examples/server/README.md) - [jeopardy](./examples/jeopardy/README.md) - [GBNF grammars](./grammars/README.md) -**Development documentations** +**Development documentation** - [How to build](./docs/build.md) - [Running on Docker](./docs/docker.md) diff --git a/ci/run.sh b/ci/run.sh index e067782193..20610e5600 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -39,7 +39,7 @@ SRC=`pwd` CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON" if [ ! -z ${GG_BUILD_METAL} ]; then - CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" fi if [ ! -z ${GG_BUILD_CUDA} ]; then @@ -53,7 +53,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then exit 1 fi - CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" fi if [ ! -z ${GG_BUILD_VULKAN} ]; then @@ -326,36 +326,36 @@ function gg_run_open_llama_7b_v2 { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -460,34 +460,34 @@ function gg_run_pythia_1_4b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -591,36 +591,36 @@ function gg_run_pythia_2_8b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -706,8 +706,8 @@ function gg_run_embd_bge_small { ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 - (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log set +e } @@ -752,7 +752,7 @@ function gg_run_rerank_tiny { model_f16="${path_models}/ggml-model-f16.gguf" # for this model, the SEP token is "" - (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?hi\nwhat is panda?it's a bear\nwhat is panda?The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log + (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?hi\nwhat is panda?it's a bear\nwhat is panda?The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log # sample output # rerank score 0: 0.029 diff --git a/cmake/arm64-apple-clang.cmake b/cmake/arm64-apple-clang.cmake new file mode 100644 index 0000000000..5fcd2882af --- /dev/null +++ b/cmake/arm64-apple-clang.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Darwin ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-apple-darwin-macho ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in index f072b76a39..5c55bc6b82 100644 --- a/cmake/llama-config.cmake.in +++ b/cmake/llama-config.cmake.in @@ -3,18 +3,60 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) -set(GGML_BLAS @GGML_BLAS@) -set(GGML_CUDA @GGML_CUDA@) -set(GGML_METAL @GGML_METAL@) -set(GGML_HIPBLAS @GGML_HIPBLAS@) +set(GGML_STATIC @GGML_STATIC@) +set(GGML_NATIVE @GGML_NATIVE@) +set(GGML_LTO @GGML_LTO@) +set(GGML_CCACHE @GGML_CCACHE@) +set(GGML_AVX @GGML_AVX@) +set(GGML_AVX2 @GGML_AVX2@) +set(GGML_AVX512 @GGML_AVX512@) +set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@) +set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@) +set(GGML_AVX512_BF16 @GGML_AVX512_BF16@) +set(GGML_AMX_TILE @GGML_AMX_TILE@) +set(GGML_AMX_INT8 @GGML_AMX_INT8@) +set(GGML_AMX_BF16 @GGML_AMX_BF16@) +set(GGML_FMA @GGML_FMA@) +set(GGML_LASX @GGML_LASX@) +set(GGML_LSX @GGML_LSX@) +set(GGML_RVV @GGML_RVV@) +set(GGML_SVE @GGML_SVE@) + set(GGML_ACCELERATE @GGML_ACCELERATE@) -set(GGML_VULKAN @GGML_VULKAN@) +set(GGML_OPENMP @GGML_OPENMP@) +set(GGML_CPU_HBM @GGML_CPU_HBM@) +set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@) + +set(GGML_CUDA_FORCE_MMQ @GGML_CUDA_FORCE_MMQ@) +set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@) +set(GGML_CUDA_F16 @GGML_CUDA_F16@) +set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@) +set(GGML_CUDA_NO_PEER_COPY @GGML_CUDA_NO_PEER_COPY@) +set(GGML_CUDA_NO_VMM @GGML_CUDA_NO_VMM@) +set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@) +set(GGML_CUDA_GRAPHS @GGML_CUDA_GRAPHS@) + +set(GGML_HIP_UMA @GGML_HIP_UMA@) + set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@) -set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@) -set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@) -set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@) -set(GGML_SYCL @GGML_SYCL@) -set(GGML_OPENMP @GGML_OPENMP@) +set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@) +set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@) +set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@) +set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@) +set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@) +set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@) + +set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@) +set(GGML_METAL_NDEBUG @GGML_METAL_NDEBUG@) +set(GGML_METAL_SHADER_DEBUG @GGML_METAL_SHADER_DEBUG@) +set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@) +set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@) +set(GGML_METAL_STD @GGML_METAL_STD@) + +set(GGML_SYCL_F16 @GGML_SYCL_F16@) +set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@) +set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@) + @PACKAGE_INIT@ @@ -22,65 +64,111 @@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@") set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@") set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@") -# Ensure transient dependencies satisfied - find_package(Threads REQUIRED) -if (APPLE AND GGML_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED) +set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@") +set(_llama_link_deps "") +set(_llama_link_opts "") +foreach(_ggml_lib ggml ggml-base) + string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") + find_library(${_ggml_lib_var} ${_ggml_lib} + REQUIRED + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH + ) + list(APPEND _llama_link_deps "${${_ggml_lib_var}}") + message(STATUS "Found ${${_ggml_lib_var}}") +endforeach() + +foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan) + string(TOUPPER "GGML_${backend}" backend_id) + set(_ggml_lib "ggml-${backend}") + string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") + + find_library(${_ggml_lib_var} ${_ggml_lib} + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH + ) + if(${_ggml_lib_var}) + list(APPEND _llama_link_deps "${${_ggml_lib_var}}") + set(${backend_id} ON) + message(STATUS "Found backend ${${_ggml_lib_var}}") + else() + set(${backend_id} OFF) + endif() +endforeach() + +if (NOT LLAMA_SHARED_LIB) + if (APPLE AND GGML_ACCELERATE) + find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED) + list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK}) + endif() + + if (GGML_OPENMP) + find_package(OpenMP REQUIRED) + list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + endif() + + if (GGML_CPU_HBM) + find_library(memkind memkind REQUIRED) + list(APPEND _llama_link_deps memkind) + endif() + + if (GGML_BLAS) + find_package(BLAS REQUIRED) + list(APPEND _llama_link_deps ${BLAS_LIBRARIES}) + list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS}) + endif() + + if (GGML_CUDA) + find_package(CUDAToolkit REQUIRED) + endif() + + if (GGML_METAL) + find_library(FOUNDATION_LIBRARY Foundation REQUIRED) + find_library(METAL_FRAMEWORK Metal REQUIRED) + find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK}) + endif() + + if (GGML_VULKAN) + find_package(Vulkan REQUIRED) + list(APPEND _llama_link_deps Vulkan::Vulkan) + endif() + + if (GGML_HIP) + find_package(hip REQUIRED) + find_package(hipblas REQUIRED) + find_package(rocblas REQUIRED) + list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas) + endif() + + if (GGML_SYCL) + find_package(DNNL) + if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") + list(APPEND _llama_link_deps DNNL::dnnl) + endif() + if (WIN32) + find_package(IntelSYCL REQUIRED) + find_package(MKL REQUIRED) + list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) + endif() + endif() endif() -if (GGML_BLAS) - find_package(BLAS REQUIRED) -endif() - -if (GGML_CUDA) - find_package(CUDAToolkit REQUIRED) -endif() - -if (GGML_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) -endif() - -if (GGML_VULKAN) - find_package(Vulkan REQUIRED) -endif() - -if (GGML_HIPBLAS) - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) -endif() - -if (GGML_SYCL) - find_package(IntelSYCL REQUIRED) - find_package(MKL REQUIRED) -endif() - -if (GGML_OPENMP) - find_package(OpenMP REQUIRED) -endif() - - -find_library(ggml_LIBRARY ggml - REQUIRED - HINTS ${LLAMA_LIB_DIR}) - find_library(llama_LIBRARY llama REQUIRED - HINTS ${LLAMA_LIB_DIR}) - -set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@") -set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@") + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH +) add_library(llama UNKNOWN IMPORTED) - set_target_properties(llama PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}" INTERFACE_LINK_LIBRARIES "${_llama_link_deps}" + INTERFACE_LINK_OPTIONS "${_llama_link_opts}" INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}" IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" IMPORTED_LOCATION "${llama_LIBRARY}" diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 042e895add..5ab1ffa192 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -66,8 +66,6 @@ add_library(${TARGET} STATIC ngram-cache.h sampling.cpp sampling.h - train.cpp - train.h ) if (BUILD_SHARED_LIBS) diff --git a/common/arg.cpp b/common/arg.cpp index 205177d469..4115b2f751 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -128,13 +128,13 @@ static void common_params_handle_model_default(common_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); + params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); } } else if (!params.model_url.empty()) { if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); - f = string_split(f, '?').front(); - params.model = fs_get_cache_file(string_split(f, '/').back()); + auto f = string_split(params.model_url, '#').front(); + f = string_split(f, '?').front(); + params.model = fs_get_cache_file(string_split(f, '/').back()); } } else if (params.model.empty()) { params.model = DEFAULT_MODEL_PATH; @@ -251,6 +251,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context for (auto & antiprompt : params.antiprompt) { string_process_escapes(antiprompt); } + for (auto & seq_breaker : params.sparams.dry_sequence_breakers) { + string_process_escapes(seq_breaker); + } } if (!params.kv_overrides.empty()) { @@ -879,7 +882,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--samplers"}, "SAMPLERS", string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), [](common_params & params, const std::string & value) { - const auto sampler_names = string_split(value, ';'); + const auto sampler_names = string_split(value, ';'); params.sparams.samplers = common_sampler_types_from_names(sampler_names, true); } ).set_sparam()); @@ -941,10 +944,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--tfs"}, "N", - string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z), + {"--xtc-probability"}, "N", + string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability), [](common_params & params, const std::string & value) { - params.sparams.tfs_z = std::stof(value); + params.sparams.xtc_probability = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--xtc-threshold"}, "N", + string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold), + [](common_params & params, const std::string & value) { + params.sparams.xtc_threshold = std::stof(value); } ).set_sparam()); add_opt(common_arg( @@ -983,6 +993,64 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sparams.penalty_freq = std::stof(value); } ).set_sparam()); + add_opt(common_arg( + {"--dry-multiplier"}, "N", + string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier), + [](common_params & params, const std::string & value) { + params.sparams.dry_multiplier = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-base"}, "N", + string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base), + [](common_params & params, const std::string & value) { + float potential_base = std::stof(value); + if (potential_base >= 1.0f) + { + params.sparams.dry_base = potential_base; + } + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-allowed-length"}, "N", + string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length), + [](common_params & params, int value) { + params.sparams.dry_allowed_length = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-penalty-last-n"}, "N", + string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n), + [](common_params & params, int value) { + params.sparams.dry_penalty_last_n = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-sequence-breaker"}, "STRING", + string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n", + params.sparams.dry_sequence_breakers.empty() ? "none" : + std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()), + params.sparams.dry_sequence_breakers.end(), + std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'", + [](const std::string& a, const std::string& b) { + std::string formatted_b = (b == "\n") ? "\\n" : b; + return a + ", '" + formatted_b + "'"; + }).c_str()), + [](common_params & params, const std::string & value) { + static bool defaults_cleared = false; + + if (!defaults_cleared) { + params.sparams.dry_sequence_breakers.clear(); + defaults_cleared = true; + } + + if (value == "none") { + params.sparams.dry_sequence_breakers.clear(); + } else { + params.sparams.dry_sequence_breakers.emplace_back(value); + } + } + ).set_sparam()); add_opt(common_arg( {"--dynatemp-range"}, "N", string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range), @@ -999,7 +1067,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--mirostat"}, "N", - string_format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n" + string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n" "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat), [](common_params & params, int value) { params.sparams.mirostat = value; @@ -1083,7 +1151,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING")); add_opt(common_arg( - {"--attention"}, "{causal,non,causal}", + {"--attention"}, "{causal,non-causal}", "attention type for embeddings, use model default if unspecified", [](common_params & params, const std::string & value) { /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } @@ -1681,7 +1749,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_BENCH})); add_opt(common_arg( {"--embd-normalize"}, "N", - string_format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), + string_format("normalisation for embeddings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), [](common_params & params, int value) { params.embd_normalize = value; } @@ -1695,7 +1763,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); add_opt(common_arg( {"--embd-separator"}, "STRING", - "separator of embendings (default \\n) for example \"<#sep#>\"", + "separator of embeddings (default \\n) for example \"<#sep#>\"", [](common_params & params, const std::string & value) { params.embd_sep = value; } @@ -1788,6 +1856,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.n_threads_http = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); + add_opt(common_arg( + {"--cache-reuse"}, "N", + string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse), + [](common_params & params, int value) { + params.n_cache_reuse = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE")); add_opt(common_arg( {"--metrics"}, string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), @@ -1864,17 +1939,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.simple_io = true; } ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); - add_opt(common_arg( - {"-ld", "--logdir"}, "LOGDIR", - "path under which to save YAML logs (no logging if unset)", - [](common_params & params, const std::string & value) { - params.logdir = value; - - if (params.logdir.back() != DIRECTORY_SEPARATOR) { - params.logdir += DIRECTORY_SEPARATOR; - } - } - )); add_opt(common_arg( {"--positive-file"}, "FNAME", string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), diff --git a/common/common.cpp b/common/common.cpp index 451307b554..d314523db4 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -416,19 +416,6 @@ std::string string_format(const char * fmt, ...) { return std::string(buf.data(), size); } -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); - } - parts.emplace_back(input); - return parts; -} - std::string string_strip(const std::string & str) { size_t start = 0; size_t end = str.size(); @@ -888,6 +875,12 @@ struct common_init_result common_init_from_params(common_params & params) { return iparams; } + if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) { + LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__); + llama_free_model(model); + return iparams; + } + if (!params.control_vectors.empty()) { if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); @@ -955,7 +948,7 @@ struct common_init_result common_init_from_params(common_params & params) { } if (llama_model_has_encoder(model)) { - llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0)); + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); llama_token decoder_start_token_id = llama_model_decoder_start_token(model); if (decoder_start_token_id == -1) { decoder_start_token_id = bos; @@ -964,7 +957,7 @@ struct common_init_result common_init_from_params(common_params & params) { tmp.push_back(decoder_start_token_id); } if (llama_model_has_decoder(model)) { - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); } llama_kv_cache_clear(lctx); llama_synchronize(lctx); @@ -1016,6 +1009,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) { if (s == "f16") { return GGML_TYPE_F16; } + if (s == "bf16") { + return GGML_TYPE_BF16; + } if (s == "q8_0") { return GGML_TYPE_Q8_0; } @@ -1035,7 +1031,7 @@ static ggml_type kv_cache_type_from_str(const std::string & s) { return GGML_TYPE_Q5_1; } - throw std::runtime_error("Invalid cache type: " + s); + throw std::runtime_error("Unsupported cache type: " + s); } struct llama_context_params common_context_params_to_llama(const common_params & params) { @@ -1047,7 +1043,7 @@ struct llama_context_params common_context_params_to_llama(const common_params & cparams.n_ubatch = params.n_ubatch; cparams.n_threads = params.cpuparams.n_threads; cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ? - params.cpuparams.n_threads : params.cpuparams_batch.n_threads; + params.cpuparams.n_threads : params.cpuparams_batch.n_threads; cparams.logits_all = params.logits_all; cparams.embeddings = params.embedding; cparams.rope_scaling_type = params.rope_scaling_type; @@ -1900,211 +1896,3 @@ common_control_vector_data common_control_vector_load(const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%e, ", data[i]); - } - fprintf(stream, "%e]\n", data.back()); -} - -void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%d, ", data[i]); - } - fprintf(stream, "%d]\n", data.back()); -} - -void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) { - std::string data_str(data == NULL ? "" : data); - - if (data_str.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - size_t pos_start = 0; - size_t pos_found = 0; - - if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { - data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); - data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); - data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); - data_str = "\"" + data_str + "\""; - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - fprintf(stream, "%s: |\n", prop_name); - while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { - fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); - pos_start = pos_found + 1; - } -} - -void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const auto & sparams = params.sparams; - - fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); - fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); - fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); - fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); - fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); - fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); - fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); - fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_riscv_v: %s\n", ggml_cpu_has_riscv_v() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); - fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); - -#ifdef NDEBUG - fprintf(stream, "debug: false\n"); -#else - fprintf(stream, "debug: true\n"); -#endif // NDEBUG - - fprintf(stream, "model_desc: %s\n", model_desc); - fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); - -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ - - fprintf(stream, "time: %s\n", timestamp.c_str()); - - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); - - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); - fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); - fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); - fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); - fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); - yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str()); - fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); - fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - fprintf(stream, "ignore_eos: %s # default: false\n", sparams.ignore_eos ? "true" : "false"); - - yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); - fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); - fprintf(stream, "keep: %d # default: 0\n", params.n_keep); - fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); - - fprintf(stream, "logit_bias:\n"); - for (const auto & logit_bias : sparams.logit_bias) { - fprintf(stream, " %d: %f", logit_bias.token, logit_bias.bias); - } - - fprintf(stream, "lora:\n"); - for (auto & la : params.lora_adapters) { - if (la.scale == 1.0f) { - fprintf(stream, " - %s\n", la.path.c_str()); - } - } - fprintf(stream, "lora_scaled:\n"); - for (auto & la : params.lora_adapters) { - if (la.scale != 1.0f) { - fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale); - } - } - fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false"); - fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); - fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); - fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); - fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); - fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); - fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); - fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); - fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); - fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); - fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); - fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); - fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); - fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); - yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str()); - fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); - fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); - fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); - yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); - - fprintf(stream, "reverse_prompt:\n"); - for (std::string ap : params.antiprompt) { - size_t pos = 0; - while ((pos = ap.find('\n', pos)) != std::string::npos) { - ap.replace(pos, 1, "\\n"); - pos += 1; - } - - fprintf(stream, " - %s\n", ap.c_str()); - } - - fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); - fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); - fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); - fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false"); - fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); - - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); - yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); - - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency()); - fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); - fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); - fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); - fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p); - fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); - fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); -} diff --git a/common/common.h b/common/common.h index 71e6861564..7977cc7a99 100644 --- a/common/common.h +++ b/common/common.h @@ -84,12 +84,15 @@ enum llama_example { enum common_sampler_type { COMMON_SAMPLER_TYPE_NONE = 0, - COMMON_SAMPLER_TYPE_TOP_K = 1, - COMMON_SAMPLER_TYPE_TOP_P = 2, - COMMON_SAMPLER_TYPE_MIN_P = 3, - COMMON_SAMPLER_TYPE_TFS_Z = 4, - COMMON_SAMPLER_TYPE_TYPICAL_P = 5, - COMMON_SAMPLER_TYPE_TEMPERATURE = 6, + COMMON_SAMPLER_TYPE_DRY = 1, + COMMON_SAMPLER_TYPE_TOP_K = 2, + COMMON_SAMPLER_TYPE_TOP_P = 3, + COMMON_SAMPLER_TYPE_MIN_P = 4, + //COMMON_SAMPLER_TYPE_TFS_Z = 5, + COMMON_SAMPLER_TYPE_TYPICAL_P = 6, + COMMON_SAMPLER_TYPE_TEMPERATURE = 7, + COMMON_SAMPLER_TYPE_XTC = 8, + COMMON_SAMPLER_TYPE_INFILL = 9, }; // dimensionality reduction methods, used by cvector-generator @@ -102,35 +105,44 @@ enum dimre_method { struct common_sampler_params { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler - int32_t n_prev = 64; // number of previous tokens to remember - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. - int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens - int32_t top_k = 40; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float min_p = 0.05f; // 0.0 = disabled - float tfs_z = 1.00f; // 1.0 = disabled - float typ_p = 1.00f; // typical_p, 1.0 = disabled - float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities - float dynatemp_range = 0.00f; // 0.0 = disabled - float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler - int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float penalty_repeat = 1.00f; // 1.0 = disabled - float penalty_freq = 0.00f; // 0.0 = disabled - float penalty_present = 0.00f; // 0.0 = disabled - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = false; // consider newlines as a repeatable token - bool ignore_eos = false; - bool no_perf = false; // disable performance metrics + int32_t n_prev = 64; // number of previous tokens to remember + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled + float xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC + float typ_p = 1.00f; // typical_p, 1.0 = disabled + float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.00f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled + float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: + float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) + int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty + int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + bool penalize_nl = false; // consider newlines as a repeatable token + bool ignore_eos = false; + bool no_perf = false; // disable performance metrics + + std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY + std::vector samplers = { + COMMON_SAMPLER_TYPE_DRY, COMMON_SAMPLER_TYPE_TOP_K, - COMMON_SAMPLER_TYPE_TFS_Z, COMMON_SAMPLER_TYPE_TYPICAL_P, COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_MIN_P, - COMMON_SAMPLER_TYPE_TEMPERATURE + COMMON_SAMPLER_TYPE_XTC, + COMMON_SAMPLER_TYPE_TEMPERATURE, }; std::string grammar; // optional BNF-like grammar to constrain sampling @@ -143,7 +155,7 @@ struct common_sampler_params { struct common_params { int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 0; // context size + int32_t n_ctx = 4096; // context size int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt @@ -166,7 +178,7 @@ struct common_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - float defrag_thold = -1.0f; // KV cache defragmentation threshold + float defrag_thold = 0.1f; // KV cache defragmentation threshold struct cpu_params cpuparams; struct cpu_params cpuparams_batch; @@ -197,7 +209,6 @@ struct common_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT std::string input_prefix = ""; // string to prefix user inputs with // NOLINT std::string input_suffix = ""; // string to suffix user inputs with // NOLINT - std::string logdir = ""; // directory in which to save YAML log files // NOLINT std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT std::string logits_file = ""; // file for saving *all* logits // NOLINT @@ -268,16 +279,17 @@ struct common_params { // embedding bool embedding = false; // get only sentence embedding - int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) + int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix - std::string embd_sep = "\n"; // separator of embendings + std::string embd_sep = "\n"; // separator of embeddings bool reranking = false; // enable reranking support on server // server params int32_t port = 8080; // server listens on this network port int32_t timeout_read = 600; // http read timeout in seconds int32_t timeout_write = timeout_read; // http write timeout in seconds - int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT @@ -373,8 +385,6 @@ bool set_process_priority(enum ggml_sched_priority prio); LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) std::string string_format(const char * fmt, ...); -std::vector string_split(std::string input, char separator); - std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); @@ -382,6 +392,7 @@ void string_replace_all(std::string & s, const std::string & search, const std:: template static std::vector string_split(const std::string & str, char delim) { + static_assert(!std::is_same::value, "Please use the specialized version for std::string"); std::vector values; std::istringstream str_stream(str); std::string token; @@ -394,6 +405,22 @@ static std::vector string_split(const std::string & str, char delim) { return values; } +template<> +std::vector string_split(const std::string & input, char separator) +{ + std::vector parts; + size_t begin_pos = 0; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(begin_pos, separator_pos - begin_pos); + parts.emplace_back(part); + begin_pos = separator_pos + 1; + separator_pos = input.find(separator, begin_pos); + } + parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos)); + return parts; +} + bool string_parse_kv_override(const char * data, std::vector & overrides); void string_process_escapes(std::string & input); @@ -556,15 +583,3 @@ common_control_vector_data common_control_vector_load(const std::vector & data); -void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); -void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); - -void yaml_dump_non_result_info( - FILE * stream, const common_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 881eb49e33..dadc18c8b3 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -611,7 +611,7 @@ private: } return join_seq(); }; - return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space"); + return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space"); } /* diff --git a/common/sampling.cpp b/common/sampling.cpp index cd49ade69a..7922fde47d 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -130,10 +130,12 @@ std::string common_sampler_params::print() const { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" - "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n" + "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" + "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, - top_k, tfs_z, top_p, min_p, typ_p, temp, + dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, + top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, mirostat, mirostat_eta, mirostat_tau); return std::string(result); @@ -171,54 +173,54 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co params.penalize_nl, params.ignore_eos)); - if (params.temp > 0.0f) { - if (params.mirostat == 0) { - for (const auto & cnstr : params.samplers) { - switch (cnstr) { - case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + if (params.mirostat == 0) { + for (const auto & cnstr : params.samplers) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto& str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); + } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + } break; - case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TFS_Z: - llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); - break; - default: - GGML_ASSERT(false && "unknown sampler type"); - } + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); } - llama_sampler_chain_add(result->chain, llama_sampler_init_softmax()); - llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); - } else if (params.mirostat == 1) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); - } else if (params.mirostat == 2) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); - } else { - GGML_ASSERT(false && "unknown mirostat version"); } + llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + } else if (params.mirostat == 1) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + } else if (params.mirostat == 2) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); } else { - if (params.n_probs > 0) { - // some use cases require to sample greedily, but still obtain the probabilities of the top tokens - // ref: https://github.com/ggerganov/llama.cpp/pull/9605 - // - // the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but - // it is much faster, since we avoid sorting all tokens and should give a good approximation - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs)); - llama_sampler_chain_add(result->chain, llama_sampler_init_softmax()); - } - llama_sampler_chain_add(result->chain, llama_sampler_init_greedy()); + GGML_ASSERT(false && "unknown mirostat version"); } return result; @@ -366,36 +368,42 @@ std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return 'd'; case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; - case COMMON_SAMPLER_TYPE_TFS_Z: return 'f'; case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; + case COMMON_SAMPLER_TYPE_XTC: return 'x'; + case COMMON_SAMPLER_TYPE_INFILL: return 'i'; default : return '?'; } } std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return "dry"; case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; - case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z"; case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; + case COMMON_SAMPLER_TYPE_XTC: return "xtc"; + case COMMON_SAMPLER_TYPE_INFILL: return "infill"; default : return ""; } } std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names) { std::unordered_map sampler_canonical_name_map { + { "dry", COMMON_SAMPLER_TYPE_DRY }, { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, - { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z }, { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, + { "xtc", COMMON_SAMPLER_TYPE_XTC }, + { "infill", COMMON_SAMPLER_TYPE_INFILL }, }; // since samplers names are written multiple ways @@ -409,8 +417,6 @@ std::vector common_sampler_types_from_names(const std::vect { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, - { "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z }, - { "tfs", COMMON_SAMPLER_TYPE_TFS_Z }, { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, }; @@ -436,12 +442,14 @@ std::vector common_sampler_types_from_names(const std::vect std::vector common_sampler_types_from_chars(const std::string & chars) { std::unordered_map sampler_name_map = { + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, - { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, - { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE } + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, }; std::vector samplers; diff --git a/common/train.cpp b/common/train.cpp deleted file mode 100644 index 661ad8382e..0000000000 --- a/common/train.cpp +++ /dev/null @@ -1,1515 +0,0 @@ -#include "train.h" -#include "common.h" - -#include -#include -#include -#include -#include - -struct random_normal_distribution { - std::mt19937 gen; - std::normal_distribution rd; - float min; - float max; -}; - -struct random_uniform_distribution { - std::mt19937 gen; - std::uniform_real_distribution rd; -}; - -struct train_state * init_train_state() { - struct train_state * state = new struct train_state; - state->train_its = 0; - state->train_samples = 0; - state->train_tokens = 0; - state->train_epochs = 0; - state->shuffle_samples_hash = 0; - state->shuffle_sample_count = 0; - state->shuffle_next_sample = 0; - state->shuffle_rng_state_current = ""; - state->shuffle_rng_state_next = ""; - - state->opt = new struct ggml_opt_context; - state->opt->ctx = NULL; - state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); - state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; - state->opt->loss_after = 0.0f; - - return state; -} - -void free_train_state(struct train_state * state) { - delete state->opt; - delete state; -} - -struct random_normal_distribution * init_random_normal_distribution( - int seed, float mean, float std, float min, float max -) { - struct random_normal_distribution * rnd = (struct random_normal_distribution *) malloc(sizeof(struct random_normal_distribution)); - rnd->gen = std::mt19937(seed); - rnd->rd = std::normal_distribution{mean, std}; - rnd->min = min; - rnd->max = max; - return rnd; -} - -struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max) { - struct random_uniform_distribution * rnd = (struct random_uniform_distribution *) malloc(sizeof(struct random_uniform_distribution)); - rnd->gen = std::mt19937(seed); - rnd->rd = std::uniform_real_distribution{min, max}; - return rnd; -} - -void free_random_normal_distribution (struct random_normal_distribution * rnd) { - free(rnd); -} - -void free_random_uniform_distribution(struct random_uniform_distribution * rnd) { - free(rnd); -} - -struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) { - float scale = 1.0f; // xavier - switch (ggml_n_dims(tensor)) { - case 1: - scale /= sqrtf((float) tensor->ne[0]); - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]); - *dst = scale * frand_normal(rnd); - } - break; - case 2: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); - *dst = scale * frand_normal(rnd); - } - } - break; - case 3: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]); - *dst = scale * frand_normal(rnd); - } - } - } - break; - case 4: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]); - *dst = scale * frand_normal(rnd); - } - } - } - } - break; - default: - die("Unsupported tensor->n_dims"); - }; - return tensor; -} - -struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) { - switch (ggml_n_dims(tensor)) { - case 1: - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]); - *dst = frand_uniform(rnd); - } - break; - case 2: - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); - *dst = frand_uniform(rnd); - } - } - break; - case 3: - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]); - *dst = frand_uniform(rnd); - } - } - } - break; - case 4: - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]); - *dst = frand_uniform(rnd); - } - } - } - } - break; - default: - die("Unsupported tensor->n_dims"); - }; - return tensor; -} - -float frand() { - return (float)rand()/((float)(RAND_MAX) + 1.0f); -} - -float frand_normal(struct random_normal_distribution * rnd) { - return fclamp(rnd->rd(rnd->gen), rnd->min, rnd->max); -} - -float frand_uniform(struct random_uniform_distribution * rnd) { - return rnd->rd(rnd->gen); -} - -int clamp(const int v, const int min, const int max) { - return ((v < min) ? (min) : (v > max) ? (max) : v); -} - -float fclamp(const float v, const float min, const float max) { - return ((v < min) ? (min) : (v > max) ? (max) : v); -} - -void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == 1); - GGML_ASSERT(tensor->ne[2] == 1); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == 1); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == ne2); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == ne2); - GGML_ASSERT(tensor->ne[3] == ne3); -} - -int64_t get_example_targets_batch( - struct llama_context * lctx, - struct ggml_tensor * tokens_input, - struct ggml_tensor * target_probs, - int64_t example_id, - const size_t * samples_offs, - const size_t * samples_begin, - const size_t * samples_size, - size_t samples_count, - const llama_token * train_data, - size_t n_train_data, - bool separate_with_eos, - bool separate_with_bos, - bool fill_with_next_samples, - bool sample_random_offsets -) { - GGML_ASSERT(samples_count > 0); - GGML_ASSERT(ggml_is_matrix(tokens_input)); - GGML_ASSERT(ggml_is_3d(target_probs)); - int64_t n_vocab = target_probs->ne[0]; - int64_t n_tokens = tokens_input->ne[0]; - int64_t n_batch = tokens_input->ne[1]; - GGML_ASSERT(n_vocab == target_probs->ne[0]); - GGML_ASSERT(n_tokens == target_probs->ne[1]); - GGML_ASSERT(n_batch == target_probs->ne[2]); - - int64_t used_samples = 0; - - ggml_set_f32(target_probs, 0.0f); - llama_token bos = llama_token_bos(llama_get_model(lctx)); - llama_token eos = llama_token_eos(llama_get_model(lctx)); - // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples); - for (int k=0; k= sample_size && fill_with_next_samples) { - if (!sample_separation_eos) { - // insert eos token to separate samples - sample_separation_eos = true; - } else if (!sample_separation_bos) { - // insert bos token to separate samples - sample_separation_bos = true; - token = bos; - } else { - // sample separation is done, continue with next sample - sample_separation_eos = !separate_with_eos; - sample_separation_bos = !separate_with_bos; - sample_offs = 0; - sample_idx = (example_id + used_samples) % samples_count; - sample_begin = samples_begin[sample_idx]; - sample_size = samples_size[sample_idx]; - ++used_samples; - } - } - // note: no else-if here - if (sample_offs < sample_size) { - token = clamp(train_data[sample_begin+sample_offs], 0, (llama_token) (n_vocab - 1)); - ++sample_offs; - } - ggml_set_f32_nd(target_probs, token, (int) i, (int) k, 0, +1.0f); - if (i+1> rng; -} - -std::string mt19937_get_state(const std::mt19937& rng) { - std::stringstream s_rng_state; - s_rng_state.imbue(std::locale::classic()); - s_rng_state << rng; - return s_rng_state.str(); -} - -std::string mt19937_seed_to_state(unsigned seed) { - std::mt19937 rng(seed); - return mt19937_get_state(rng); -} - -std::string shuffle_samples( - const std::string & rng_state, - size_t * shuffled_offs, - size_t * shuffled_begins, - size_t * shuffled_sizes, - const size_t * begins, - const size_t * sizes, - size_t count) { - if (count == 0) return rng_state; - - std::mt19937 rng; - mt19937_set_state(rng, rng_state); - - // sort indices by random value for each index - std::vector idcs; - { - std::vector rnd; - idcs.resize(count); - rnd.resize(count); - for (unsigned i=0; i h_string; - std::hash h_ull; - size_t h = h_string(std::string(fn)); - h = hash_combine(h, h_ull((unsigned long long) sample_count)); - for (size_t i=0; i< sample_count; ++i) { - h = hash_combine(h, h_ull((unsigned long long) samples_begin[i])); - h = hash_combine(h, h_ull((unsigned long long) samples_size[i])); - } - return h; -} - -std::string replace_str(const char * s, const char * needle, const char * replacement) { - std::string str = s; - size_t pos = str.find(needle); - if (pos != std::string::npos) { - str.replace(pos, strlen(needle), replacement); - } - return str; -} - -void print_duration(double fmillis) { - if (fmillis < 1000.0f) { - printf("%.1fms", (float) fmillis); - return; - } - const int64_t one_sec = 1000; - const int64_t one_min = one_sec * 60; - const int64_t one_hour = one_min * 60; - const int64_t one_day = one_hour * 24; - - int64_t millis = (int64_t) fmillis; - int64_t days = millis/one_day; - int64_t hours = (millis - days*one_day)/one_hour; - int64_t minutes = (millis - days*one_day - hours*one_hour)/one_min; - int64_t seconds = (millis - days*one_day - hours*one_hour - minutes*one_min)/one_sec; - - // to print int64_t either cast to (long long int) or use macro PRId64 from - if (days > 0) { - printf("%lldd ", (long long int) days); - } - printf("%02lld:%02lld:%02lld", (long long int) hours, (long long int) minutes, (long long int) seconds); -} - -float cosine_decay(int64_t step, int64_t decay_steps, float minimum) { - if (step > decay_steps) { - step = decay_steps; - } - const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps)); - const float decay = (1 - minimum)*cosine_decay + minimum; - return decay; -} - -float cosine_decay_restart(int64_t step, int64_t decay_steps, float minimum, float restart_step_mult) { - while (step > decay_steps) { - step -= decay_steps; - decay_steps = (int64_t) (restart_step_mult * decay_steps); - } - return cosine_decay(step, decay_steps, minimum); -} - -float learning_schedule( - int64_t step, - int64_t warmup_steps, - int64_t cos_decay_steps, - float learning_rate, - float overall_minimum, - float cos_decay_minimum, - float cos_decay_restart_step_mult, - bool enable_restart) { - - float result = - (step < warmup_steps) - ? (float) step / (float) warmup_steps - : enable_restart - ? cosine_decay_restart( - step - warmup_steps, - cos_decay_steps, - cos_decay_minimum, - cos_decay_restart_step_mult) - : cosine_decay( - step, - cos_decay_steps, - cos_decay_minimum); - - float min = overall_minimum / learning_rate; - result = min + result * (1.0f - min); - return result; -} - -static bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) { - GGML_ASSERT(a != NULL); - GGML_ASSERT(b != NULL); - GGML_ASSERT(a->type == b->type); - GGML_ASSERT(ggml_are_same_shape(a, b)); - GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b)); - - return true; -} - -void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) { - if (dst == NULL) { - return; - } - struct ggml_tensor * t = ggml_get_tensor(ctx, name); - GGML_ASSERT(are_same_layout(dst, t)); - memcpy(dst->data, t->data, ggml_nbytes(t)); - - if (strlen(ggml_get_name(dst)) == 0) { - ggml_set_name(dst, name); - } -} - -// gguf constants -static const char * LLM_KV_OPTIMIZER_TYPE = "optimizer.type"; -static const char * LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"; -static const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"; -static const char * LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"; -static const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"; -static const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"; -static const char * LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"; -static const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"; -static const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"; -static const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"; -static const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"; -static const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"; -static const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"; -static const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"; - -static const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"; -static const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"; -static const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"; - -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"; - -static const char * LLM_KV_TRAINING_FILE_VERSION = "training.file_version"; -static const char * LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"; -static const char * LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"; -static const char * LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"; -static const char * LLM_KV_TRAINING_EPOCH_COUNT = "training.epoch_count"; -static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH = "training.shuffle.samples_hash"; -static const char * LLM_KV_TRAINING_SHUFFLE_RNG_STATE = "training.shuffle.rng_state"; -static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT = "training.shuffle.sample_count"; -static const char * LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE = "training.shuffle.next_sample"; - -#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \ -{ \ - const std::string skey(key); \ - const int kid = gguf_find_key(ctx, skey.c_str()); \ - if (kid >= 0) { \ - enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \ - if (ktype != (type)) { \ - die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \ - } \ - (dst) = func(ctx, kid); \ - } else if (req) { \ - die_fmt("key not found in model: %s", skey.c_str()); \ - } \ -} - -void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) { - // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read - - uint32_t file_version; - GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION); - GGML_ASSERT(file_version == 0); - - GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT); - GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT); - GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED); - - uint64_t nx; - GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT); - opt->nx = (size_t) nx; - - // don't call ggml_opt_init until optimizer type and optimizer specific parameters are know - - std::string opt_type; - GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE); - if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) { - opt->params.type = GGML_OPT_TYPE_ADAM; - - GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS); - GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS); - GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT); - - ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); - - copy_tensor_by_name(opt->adam.m, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); - copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); - copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); - } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) { - opt->params.type = GGML_OPT_TYPE_LBFGS; - - GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT); - GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS); - GGUF_GET_KEY(fctx, opt->lbfgs.step, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP); - GGUF_GET_KEY(fctx, opt->lbfgs.j, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J); - GGUF_GET_KEY(fctx, opt->lbfgs.k, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K); - GGUF_GET_KEY(fctx, opt->lbfgs.end, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END); - GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT); - - ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); - - copy_tensor_by_name(opt->lbfgs.x, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); - copy_tensor_by_name(opt->lbfgs.xp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); - copy_tensor_by_name(opt->lbfgs.g, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); - copy_tensor_by_name(opt->lbfgs.gp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); - copy_tensor_by_name(opt->lbfgs.d, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); - copy_tensor_by_name(opt->lbfgs.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); - copy_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); - copy_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); - copy_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); - copy_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); - } else { - die("unknown optimizer type\n"); - } -} - -void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) { - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past); - gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter); - gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized); - - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, opt->adam.fx_prev); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement); - - ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); - ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); - if (opt->adam.pf) { - ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); - } - - gguf_add_tensor(fctx, opt->adam.m); - gguf_add_tensor(fctx, opt->adam.v); - if (opt->adam.pf) { - gguf_add_tensor(fctx, opt->adam.pf); - } - } break; - case GGML_OPT_TYPE_LBFGS: - { - gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, opt->lbfgs.fx_best); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, opt->lbfgs.step); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, opt->lbfgs.j); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, opt->lbfgs.k); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, opt->lbfgs.end); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement); - - ggml_set_name(opt->lbfgs.x, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); - ggml_set_name(opt->lbfgs.xp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); - ggml_set_name(opt->lbfgs.g, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); - ggml_set_name(opt->lbfgs.gp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); - ggml_set_name(opt->lbfgs.d, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); - if (opt->lbfgs.pf) { - ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); - } - ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); - ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); - ggml_set_name(opt->lbfgs.lms, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); - ggml_set_name(opt->lbfgs.lmy, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); - - gguf_add_tensor(fctx, opt->lbfgs.x); - gguf_add_tensor(fctx, opt->lbfgs.xp); - gguf_add_tensor(fctx, opt->lbfgs.g); - gguf_add_tensor(fctx, opt->lbfgs.gp); - gguf_add_tensor(fctx, opt->lbfgs.d); - if (opt->lbfgs.pf) { - gguf_add_tensor(fctx, opt->lbfgs.pf); - } - gguf_add_tensor(fctx, opt->lbfgs.lmal); - gguf_add_tensor(fctx, opt->lbfgs.lmys); - gguf_add_tensor(fctx, opt->lbfgs.lms); - gguf_add_tensor(fctx, opt->lbfgs.lmy); - } break; - } -} - -bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train) { - if (gguf_find_key(fctx, LLM_KV_TRAINING_FILE_VERSION) < 0) { - return false; - } - - uint32_t file_version; - GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION); - GGML_ASSERT(file_version <= 1); - - if (file_version == 0) { - - GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT); - GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT); - - } else if (file_version == 1) { - - GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_ITERATION_COUNT); - GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_TOKEN_COUNT); - GGUF_GET_KEY(fctx, train->train_epochs, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_EPOCH_COUNT); - - GGUF_GET_KEY(fctx, train->shuffle_samples_hash, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH); - GGUF_GET_KEY(fctx, train->shuffle_rng_state_current, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_SHUFFLE_RNG_STATE); - GGUF_GET_KEY(fctx, train->shuffle_sample_count, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->shuffle_next_sample, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE); - } - - load_opt_context_gguf(fctx, f_ggml_ctx, train->opt); - return true; -} - -void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train) { - gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION, 1); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_ITERATION_COUNT, train->train_its); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SAMPLE_COUNT, train->train_samples); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_TOKEN_COUNT, train->train_tokens); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_EPOCH_COUNT, train->train_epochs); - - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH, (uint64_t) train->shuffle_samples_hash); - gguf_set_val_str(fctx, LLM_KV_TRAINING_SHUFFLE_RNG_STATE, train->shuffle_rng_state_current.c_str()); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT, (uint64_t) train->shuffle_sample_count); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE, (uint64_t) train->shuffle_next_sample); - - save_opt_context_gguf(fctx, train->opt); -} - - -struct llama_file { - // use FILE * so we don't have to re-open the file to mmap - FILE * fp; - size_t size; - - llama_file(const char * fname, const char * mode) { - fp = std::fopen(fname, mode); - if (fp == NULL) { - size = 0; - } else { - seek(0, SEEK_END); - size = tell(); - seek(0, SEEK_SET); - } - } - - size_t tell() const { -#ifdef _WIN32 - __int64 ret = _ftelli64(fp); -#else - long ret = std::ftell(fp); -#endif - GGML_ASSERT(ret != -1); // this really shouldn't fail - return (size_t) ret; - } - - void seek(size_t offset, int whence) { -#ifdef _WIN32 - int ret = _fseeki64(fp, (__int64) offset, whence); -#else - int ret = std::fseek(fp, (long) offset, whence); -#endif - GGML_ASSERT(ret == 0); // same - } - - void read_raw(void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - std::size_t ret = std::fread(ptr, size, 1, fp); - if (ferror(fp)) { - die_fmt("read error: %s", strerror(errno)); - } - if (ret != 1) { - die("unexpectedly reached end of file"); - } - } - - std::uint32_t read_u32() { - std::uint32_t ret; - read_raw(&ret, sizeof(ret)); - return ret; - } - - std::string read_string(std::uint32_t len) { - std::vector chars(len); - read_raw(chars.data(), len); - return std::string(chars.data(), len); - } - - void write_raw(const void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - size_t ret = std::fwrite(ptr, size, 1, fp); - if (ret != 1) { - die_fmt("write error: %s", strerror(errno)); - } - } - - void write_u32(std::uint32_t val) { - write_raw(&val, sizeof(val)); - } - - ~llama_file() { - if (fp) { - std::fclose(fp); - } - } -}; - -static size_t utf8_len(char src) { - const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; - uint8_t highbits = static_cast(src) >> 4; - return lookup[highbits]; -} - -// mark each byte with its utf8 unit number. -// returns the number of utf8 characters. -// e.g. when bytes == '\x61\xD0\xB0\x62', -// then utf8_units will become [0,0,1,0] -// utf8_nunits will become [1,2,2,1] and 3 is returned. -// bytes where utf8_units is zero, are the begin of an utf8 character. -static size_t mark_utf8_units(const char* bytes, int * utf8_units, int * utf8_nunits, size_t count) { - size_t offs = 0; - size_t count_utf8 = 0; - while(offs < count) { - int len = (int) utf8_len(bytes[offs]); - for (int i=0; i & out_tokens, - std::vector & out_samples_begin, - std::vector & out_samples_size) { - struct llama_file f(filename, "rb"); - - if (f.size == 0) { - out_tokens.clear(); - out_samples_begin.clear(); - out_samples_size.clear(); - printf("%s: warning: empty or not existing training data file '%s'\n", - __func__, filename); - return out_tokens.size(); - } - - // account for possible leading whitespace that will be added by tokenizer - // e.g. '\t' will be tokenized by llama spm tokenizer to [29871, 12] - const int n_max_tokens_overhead = 1; - - std::vector buf; - buf.resize(f.size); - - f.read_raw(buf.data(), f.size); - - std::vector utf8_units; - std::vector utf8_nunits; - utf8_units.resize(buf.size()); - utf8_nunits.resize(buf.size()); - mark_utf8_units(buf.data(), utf8_units.data(), utf8_nunits.data(), buf.size()); - - if (sample_start.size() == 0) { - // tokenize all data at once - out_tokens.resize(buf.size() + n_max_tokens_overhead); - - int n_tokens = llama_tokenize( - llama_get_model(lctx), - buf.data(), - (int) buf.size(), - out_tokens.data(), - (int) out_tokens.size(), - false, false); - if (n_tokens < 0) { - out_tokens.resize(-n_tokens); - n_tokens = llama_tokenize( - llama_get_model(lctx), - buf.data(), - (int) buf.size(), - out_tokens.data(), - (int) out_tokens.size(), - false, false); - } - if (n_tokens >= 0) { - out_tokens.resize(n_tokens); - } - - // generate sample starts at all token positions - out_samples_begin.clear(); - out_samples_begin.push_back(0); - out_samples_size.push_back(std::min((size_t) context_length, out_tokens.size())); - size_t end = (out_tokens.size() >= context_length) ? (out_tokens.size() - context_length) : 0; - for (size_t sample_begin = 1; sample_begin < end; ++sample_begin) { - out_samples_begin.push_back(sample_begin); - out_samples_size.push_back(context_length); - } - } else { - // split data into samples and tokenize each sample - std::string data_str(buf.data(), buf.size()); - out_samples_begin.clear(); - out_samples_size.clear(); - out_tokens.clear(); - - // find all positions of pattern sample_start - size_t sample_begin = data_str.find(sample_start, 0); - while (sample_begin != std::string::npos) { - out_samples_begin.push_back(sample_begin); - const size_t search_start = sample_begin + sample_start.size(); - sample_begin = data_str.find(sample_start, search_start); - } - if (out_samples_begin.size() == 0) { - printf("%s: warning: sample start pattern '%s' not found. inserting single sample at data begin\n", - __func__, sample_start.c_str()); - out_samples_begin.push_back(0); - } - - out_samples_size.resize(out_samples_begin.size(), 0); - - std::vector buf_sample; - std::vector tok_sample; - - const size_t sample_begin_offset = (include_sample_start ? 0 : sample_start.size()); - size_t found_too_big_sample = 0; - size_t found_too_small_sample = 0; - size_t found_empty_sample = 0; - size_t found_min_sample_size = SIZE_MAX; - size_t found_max_sample_size = 0; - - size_t max_token_text_size = 0; - int n_vocab = llama_n_vocab(llama_get_model(lctx)); - for (llama_token token=0; token < n_vocab; ++token) { - max_token_text_size = std::max( - max_token_text_size, - strlen(llama_token_get_text(llama_get_model(lctx), token))); - } - - // upper bound of context byte length. - // strings with this byte length should always tokenize to at least context_length tokens. - size_t context_byte_len = max_token_text_size*context_length; - - for (unsigned i=0; i 0) { - // sample end is in the middle of an utf8 character. - // advance sample_end to the begin of the next utf8 character. - sample_end += utf8_nunits[sample_end] - utf8_units[sample_end]; - } - size_t sample_size = sample_end - sample_begin; - if (sample_size == 0) { - ++found_empty_sample; - } - - if (sample_size > 0) { - // llama_tokenize expects zero terminated string, - // copy sample into buffer and zero terminate it. - buf_sample.resize(sample_size); - memcpy(buf_sample.data(), data_str.data() + sample_begin, sample_size); - - // printf("sample: '%s'\n", buf_sample.data()); - - // tokenize the sample - tok_sample.resize(buf_sample.size() + n_max_tokens_overhead); - int n_tokens = llama_tokenize(llama_get_model(lctx), - buf_sample.data(), - (int) buf_sample.size(), - tok_sample.data(), - (int) tok_sample.size(), - false, false); - if (n_tokens < 0) { - tok_sample.resize(-n_tokens); - n_tokens = llama_tokenize(llama_get_model(lctx), - buf_sample.data(), - (int) buf_sample.size(), - tok_sample.data(), - (int) tok_sample.size(), - false, false); - GGML_ASSERT(n_tokens >= 0); - } - GGML_ASSERT(n_tokens <= (int) tok_sample.size()); - - if ((size_t) n_tokens > context_length) { - ++found_too_big_sample; - } else if ((size_t) n_tokens < context_length) { - ++found_too_small_sample; - } - found_max_sample_size = std::max(found_max_sample_size, (size_t) n_tokens); - found_min_sample_size = std::min(found_min_sample_size, (size_t) n_tokens); - - // write out tokens, start and size of sample - // overwrite the string start position with the token start position - out_samples_begin[i] = out_tokens.size(); - out_samples_size[i] = (size_t) n_tokens; - out_tokens.insert(out_tokens.end(), tok_sample.begin(), tok_sample.begin() + n_tokens); - } else { - out_samples_begin[i] = out_tokens.size(); - out_samples_size[i] = 0; - } - - } - if (found_too_big_sample > 0) { - printf("%s: warning: found %zu samples (max length %zu) that exceed context length of %u. samples will be cut off.\n", - __func__, found_too_big_sample, found_max_sample_size, context_length); - } - - if (found_too_small_sample > 0) { - printf("%s: warning: found %zu samples (min length %zu) that are shorter than context length of %u.\n", - __func__, found_too_small_sample, found_min_sample_size, context_length); - } - - if (found_empty_sample) { - printf("%s: warning: found %zu empty samples.\n", - __func__, found_empty_sample); - } - } - printf("%s: total number of samples: %zu\n", - __func__, out_samples_begin.size()); - - GGML_ASSERT(out_samples_begin.size() == out_samples_size.size()); - - return out_tokens.size(); -} - -std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration) { - std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest); - return replace_str(filename, pattern_it, sit.c_str()); -} - -struct train_params_common get_default_train_params_common() { - struct train_params_common params; - params.fn_train_data = "shakespeare.txt"; - params.fn_checkpoint_in = "checkpoint.gguf"; - params.fn_checkpoint_out = "checkpoint-ITERATION.gguf"; - params.pattern_fn_it = "ITERATION"; - params.fn_latest = "LATEST"; - - params.print_usage = false; - - params.save_every = 10; - - params.seed = -1; - - params.n_ctx = 128; - params.n_threads = 6; - params.n_batch = 8; - params.n_gradient_accumulation = 1; - params.n_epochs = -1; - params.n_gpu_layers = 0; - - params.custom_n_ctx = false; - - params.use_flash = false; - params.use_checkpointing = true; - - params.sample_start = ""; - params.include_sample_start = false; - params.escape = false; - params.overlapping_samples = false; - params.fill_with_next_samples = false; - params.separate_with_eos = false; - params.separate_with_bos = true; - params.sample_random_offsets = false; - params.force_reshuffle = false; - - params.opt_past = 0; - params.opt_delta = 1e-5f; - params.opt_max_no_improvement = 0; - - params.warmup = 100; - params.cos_decay_steps = 1000; - params.cos_decay_restart = 1.1f; - params.cos_decay_min = 0.1f; - params.enable_restart = false; - - params.adam_n_iter = 256; - params.adam_alpha = 1e-3f; - params.adam_min_alpha = 0; - params.adam_decay = 1e-1f; - params.adam_decay_min_ndim = 2; - params.adam_beta1 = 0.9f; - params.adam_beta2 = 0.999f; - params.adam_gclip = 1.0f; - params.adam_eps_f = 0.0f; - - return params; -} - -void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train_params_common * params) { - // fprintf(stderr, "usage: %s [options]\n", argv[0]); - // fprintf(stderr, "\n"); - // fprintf(stderr, "options:\n"); - // fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " --train-data FNAME path from which to load training data (default '%s')\n", params->fn_train_data); - fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in); - fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out); - fprintf(stderr, " --pattern-fn-it STR pattern in output filenames to be replaced by iteration number (default '%s')\n", params->pattern_fn_it); - fprintf(stderr, " --fn-latest STR string to use instead of iteration number for saving latest output (default '%s')\n", params->fn_latest); - fprintf(stderr, " --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%d')\n", params->save_every); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n"); - fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx); - fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads); - fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch); - fprintf(stderr, " --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\n", params->n_gradient_accumulation); - fprintf(stderr, " --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str()); - fprintf(stderr, " --include-sample-start Include the sample start in the samples. (default off)\n"); - fprintf(stderr, " --escape process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stderr, " --overlapping-samples Samples may overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n"); - fprintf(stderr, " --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n"); - fprintf(stderr, " --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : ""); - fprintf(stderr, " --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : ""); - fprintf(stderr, " --no-separate-with-eos When fill-with-next-samples, don't insert end-of-sequence token between samples.%s\n", !params->separate_with_eos ? " (default)" : ""); - fprintf(stderr, " --no-separate-with-bos When fill-with-next-samples, don't insert begin-of-sequence token between samples.%s\n", !params->separate_with_bos ? " (default)" : ""); - fprintf(stderr, " --sample-random-offsets Use samples beginning at random offsets. Together with fill-with-next-samples this may help for training endless text generation.%s\n", params->sample_random_offsets ? " (default)" : ""); - fprintf(stderr, " --force-reshuffle Force a reshuffling of data at program start, otherwise the shuffling of loaded checkpoint is resumed.\n"); - fprintf(stderr, " --no-flash Don't use flash attention \n"); - fprintf(stderr, " --use-flash Use flash attention (default)\n"); - fprintf(stderr, " --no-checkpointing Don't use gradient checkpointing\n"); - fprintf(stderr, " --use-checkpointing Use gradient checkpointing (default)\n"); - fprintf(stderr, " --warmup N Only for Adam optimizer. Number of warmup steps (default %d)\n", params->warmup); - fprintf(stderr, " --cos-decay-steps N Only for Adam optimizer. Number of cosine decay steps (default %d)\n", params->cos_decay_steps); - fprintf(stderr, " --cos-decay-restart N Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart); - fprintf(stderr, " --cos-decay-min N Only for Adam optimizer. Cosine decay minimum (default %f)\n", params->cos_decay_min); - fprintf(stderr, " --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay %s\n", params->enable_restart ? "(default)" : ""); - fprintf(stderr, " --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay %s\n", !params->enable_restart ? "(default)" : ""); - fprintf(stderr, " --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\n", params->opt_past); - fprintf(stderr, " --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\n", params->opt_delta); - fprintf(stderr, " --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\n", params->opt_max_no_improvement); - fprintf(stderr, " --epochs N Maximum number epochs to process. (default %d)\n", params->n_epochs); - fprintf(stderr, " --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter); - fprintf(stderr, " --adam-alpha N Adam learning rate alpha (default %f)\n", params->adam_alpha); - fprintf(stderr, " --adam-min-alpha N Adam minimum learning rate alpha - including warmup phase (default %f)\n", params->adam_min_alpha); - fprintf(stderr, " --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay); - fprintf(stderr, " --adam-decay-min-ndim N Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\n", params->adam_decay_min_ndim); - fprintf(stderr, " --adam-beta1 N AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\n", params->adam_beta1); - fprintf(stderr, " --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2); - fprintf(stderr, " --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip); - fprintf(stderr, " --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f); - fprintf(stderr, " -ngl N, --n-gpu-layers N Number of model layers to offload to GPU (default %d)", params->n_gpu_layers); - fprintf(stderr, "\n"); -} - -bool consume_common_train_arg( - int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param -) { - int& i = *idx; - std::string arg = argv[i]; - const std::string arg_prefix = "--"; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - if (arg == "--train-data") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_train_data = argv[i]; - } else if (arg == "--checkpoint-in") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_checkpoint_in = argv[i]; - } else if (arg == "--checkpoint-out") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_checkpoint_out = argv[i]; - } else if (arg == "--pattern-fn-it") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->pattern_fn_it = argv[i]; - } else if (arg == "--fn-latest") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_latest = argv[i]; - } else if (arg == "--save-every") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->save_every = std::stoi(argv[i]); - } else if (arg == "-s" || arg == "--seed") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->seed = std::stoi(argv[i]); - } else if (arg == "-c" || arg == "--ctx") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_ctx = std::stoi(argv[i]); - params->custom_n_ctx = true; - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_threads = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_batch = std::stoi(argv[i]); - } else if (arg == "--grad-acc") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_gradient_accumulation = std::max(1, std::stoi(argv[i])); - } else if (arg == "--sample-start") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->sample_start = std::string(argv[i]); - } else if (arg == "--escape") { - params->escape = true; - } else if (arg == "--include-sample-start") { - params->include_sample_start = true; - } else if (arg == "--overlapping-samples") { - params->overlapping_samples = true; - } else if (arg == "--fill-with-next-samples") { - params->fill_with_next_samples = true; - } else if (arg == "--separate-with-eos") { - params->separate_with_eos = true; - } else if (arg == "--separate-with-bos") { - params->separate_with_bos = true; - } else if (arg == "--no-separate-with-eos") { - params->separate_with_eos = false; - } else if (arg == "--no-separate-with-bos") { - params->separate_with_bos = false; - } else if (arg == "--sample-random-offsets") { - params->sample_random_offsets = true; - } else if (arg == "--force-reshuffle") { - params->force_reshuffle = true; - } else if (arg == "--no-flash") { - params->use_flash = false; - } else if (arg == "--use-flash") { - params->use_flash = true; - } else if (arg == "--no-checkpointing") { - params->use_checkpointing = false; - } else if (arg == "--use-checkpointing") { - params->use_checkpointing = true; - } else if (arg == "--warmup") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->warmup = std::stoi(argv[i]); - } else if (arg == "--cos-decay-steps") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_steps = std::stoi(argv[i]); - } else if (arg == "--cos-decay-restart") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_restart = std::stof(argv[i]); - } else if (arg == "--cos-decay-min") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_min = std::stof(argv[i]); - } else if (arg == "--enable-restart") { - params->enable_restart = true; - } else if (arg == "--disable-restart") { - params->enable_restart = false; - } else if (arg == "--opt-past") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_past = std::stoi(argv[i]); - } else if (arg == "--opt-delta") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_delta = std::stof(argv[i]); - } else if (arg == "--opt-max-no-improvement") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_max_no_improvement = std::stoi(argv[i]); - } else if (arg == "--adam-epsf") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_eps_f = std::stof(argv[i]); - } else if (arg == "--epochs") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_epochs = std::stoi(argv[i]); - } else if (arg == "--adam-iter") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_n_iter = std::stoi(argv[i]); - } else if (arg == "--adam-alpha") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_alpha = std::stof(argv[i]); - } else if (arg == "--adam-min-alpha") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_min_alpha = std::stof(argv[i]); - } else if (arg == "--adam-decay") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_decay = std::stof(argv[i]); - } else if (arg == "--adam-decay-min-ndim") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_decay_min_ndim = std::stoi(argv[i]); - } else if (arg == "--adam-beta1") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_beta1 = std::stof(argv[i]); - } else if (arg == "--adam-beta2") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_beta2 = std::stof(argv[i]); - } else if (arg == "--adam-gclip") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_gclip = std::stof(argv[i]); - } else if (arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - if (llama_supports_gpu_offload()) { - params->n_gpu_layers = std::stoi(argv[i]); - } else { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } - } else if (arg == "-h" || arg == "--help") { - params->print_usage = true; - return true; - } else { - return false; - } - return true; -} - -void finish_processing_train_args(struct train_params_common * params) { - if (params->escape) { - string_process_escapes(params->sample_start); - } -} - -void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel) { - struct train_opt_callback_data * data = (struct train_opt_callback_data *) vdata; - struct train_params_common * params = data->params; - struct train_state * train = data->train; - struct ggml_opt_context * opt = train->opt; - int n_batch = params->n_batch; - int n_ctx = params->n_ctx; - - if (accum_step == 0) { - // time measurement - int64_t now = ggml_time_ms(); - if (now > data->last_time && opt->iter > data->first_iter) { - double dt = (double) (now - data->last_time); - if (data->millis_per_iter == 0.0) { - data->millis_per_iter = dt; - } else { - const double gain = 0.7; - data->millis_per_iter = data->millis_per_iter*(1.0-gain) + dt*gain; - } - } - - double remaining_millis = 0.0; - if (data->millis_per_iter > 0.0) { - const int n_iter = params->adam_n_iter; - const int done_iter = opt->iter - data->first_iter; - const int remaining_iter = n_iter - done_iter; - remaining_millis = remaining_iter * data->millis_per_iter; - } - - // file saving - const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every); - if (save_now) { - int new_iters = opt->iter - data->last_save_iter; - train->train_its += new_iters; - train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_ctx; - - if (data->save_cb) { - data->save_cb(data->save_data, train); - } - - data->last_save_iter = opt->iter; - } - - // exclude file saving from time measurement, by measuring last_time after saving - data->last_time = ggml_time_ms(); - - *sched = learning_schedule( - opt->iter, - params->warmup, - params->cos_decay_steps, - params->adam_alpha, - params->adam_min_alpha, - params->cos_decay_min, - params->cos_decay_restart, - params->enable_restart); - - int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f); - if (impr_plot > 0) impr_plot = 0; - if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0; - printf("%s: iter=%6d sample=%zu/%zu sched=%f loss=%f", - __func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count, - *sched, opt->loss_after); - - - if (data->millis_per_iter > 0) { - printf(" dt="); - print_duration(data->millis_per_iter); - printf(" eta="); - print_duration(remaining_millis); - } - - float improvement = opt->loss_before - opt->loss_after; - const float plot_scale = 10.0f; - int bar_len = (int)(1 + improvement*plot_scale + 0.5); - printf(" |"); - for (int i=0; i"); - printf("\n"); - } - - int64_t used_samples = get_example_targets_batch( - data->lctx, - data->tokens_input, - data->target_probs, - train->shuffle_next_sample, - data->shuffled_samples_offs, - data->shuffled_samples_begin, - data->shuffled_samples_size, - data->samples_count, - data->tokens_data, - data->tokens_size, - params->separate_with_eos, - params->separate_with_bos, - params->fill_with_next_samples, - params->sample_random_offsets); - - train->train_samples += used_samples; - train->shuffle_next_sample += used_samples; - - if (train->shuffle_next_sample >= train->shuffle_sample_count) { - ++train->train_epochs; - printf("%s: reshuffle samples. completed epochs: %llu\n", __func__, (long long unsigned) train->train_epochs); - // note: we may have used some samples from the current shuffling more than once - train->shuffle_rng_state_current = train->shuffle_rng_state_next; - train->shuffle_rng_state_next = shuffle_samples( - train->shuffle_rng_state_current, - data->shuffled_samples_offs, - data->shuffled_samples_begin, - data->shuffled_samples_size, - data->samples_begin, - data->samples_size, - data->samples_count); - train->shuffle_next_sample = 0; - } - - const bool last_epoch_reached = (params->n_epochs > 0 && (int64_t) train->train_epochs - data->first_epoch >= params->n_epochs); - if (last_epoch_reached) { - // allow optimization iteration at last epoch to be completed before canceling - if (data->iter_at_last_epoch < 0) { - data->iter_at_last_epoch = opt->iter; - } else if (opt->iter > data->iter_at_last_epoch) { - *cancel = true; - } - } -} diff --git a/common/train.h b/common/train.h deleted file mode 100644 index 263d940c04..0000000000 --- a/common/train.h +++ /dev/null @@ -1,233 +0,0 @@ -// Various helper functions and utilities for training - -#pragma once - -#include -#include -#include - -#include "ggml.h" -#include "llama.h" - -#define LLAMA_TRAIN_MAX_NODES 16384 - -typedef std::string mt19937_state; - -struct train_state { - struct ggml_opt_context * opt; - - uint64_t train_its; - uint64_t train_samples; - uint64_t train_tokens; - uint64_t train_epochs; - - size_t shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes) - mt19937_state shuffle_rng_state_current; - mt19937_state shuffle_rng_state_next; - size_t shuffle_sample_count; - size_t shuffle_next_sample; -}; - -struct train_params_common { - const char * fn_train_data; - const char * fn_checkpoint_in; - const char * fn_checkpoint_out; - const char * pattern_fn_it; - const char * fn_latest; - - bool print_usage; - - int save_every; - - uint32_t seed; - - int n_ctx; - int n_threads; - int n_batch; - int n_gradient_accumulation; - int n_epochs; - int n_gpu_layers; - - bool custom_n_ctx; - - bool use_flash; - bool use_checkpointing; - - std::string sample_start; - bool include_sample_start; - bool escape; - bool overlapping_samples; - bool fill_with_next_samples; - bool separate_with_eos; - bool separate_with_bos; - bool sample_random_offsets; - - bool force_reshuffle; - - int warmup; - int cos_decay_steps; - float cos_decay_restart; - float cos_decay_min; - bool enable_restart; - - int opt_past; - float opt_delta; - int opt_max_no_improvement; - - int adam_n_iter; - float adam_alpha; - float adam_min_alpha; - float adam_decay; - int adam_decay_min_ndim; - float adam_beta1; - float adam_beta2; - float adam_gclip; - float adam_eps_f; -}; - -typedef void (*save_train_files_callback)(void * data, struct train_state * train); - -struct train_opt_callback_data { - struct train_params_common * params; - struct train_state * train; - save_train_files_callback save_cb; - void * save_data; - struct llama_context * lctx; - int last_save_iter; - llama_token * tokens_data; - size_t tokens_size; - size_t * samples_begin; - size_t * samples_size; - size_t * shuffled_samples_offs; - size_t * shuffled_samples_begin; - size_t * shuffled_samples_size; - size_t samples_count; - struct ggml_tensor * tokens_input; - struct ggml_tensor * target_probs; - int first_iter; - int first_epoch; - int iter_at_last_epoch; - int64_t last_time; - double millis_per_iter; -}; - -struct train_state * init_train_state(); -void free_train_state(struct train_state * state); - -struct train_params_common get_default_train_params_common(); -void print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params); - -bool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param); -void finish_processing_train_args(struct train_params_common * params); - -struct random_normal_distribution; -struct random_uniform_distribution; - -struct random_normal_distribution * init_random_normal_distribution (int seed, float mean, float std, float min, float max); -struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max); - -void free_random_normal_distribution (struct random_normal_distribution * rnd); -void free_random_uniform_distribution(struct random_uniform_distribution * rnd); - -struct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd); -struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd); - -// generate random float in interval [0,1) -float frand(); -float frand_normal (struct random_normal_distribution * rnd); -float frand_uniform(struct random_uniform_distribution * rnd); - -int clamp (const int v, const int min, const int max); -float fclamp(const float v, const float min, const float max); - -void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0); -void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1); -void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2); -void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); - -size_t tokenize_file( - struct llama_context * lctx, - const char * filename, - const std::string & sample_start, - bool include_sample_start, - bool overlapping_samples, - unsigned context_length, - std::vector & out_tokens, - std::vector & out_samples_begin, - std::vector & out_samples_size); - -int64_t get_example_targets_batch( - struct llama_context * lctx, - struct ggml_tensor * tokens_input, - struct ggml_tensor * target_probs, - int64_t example_id, - const size_t * samples_offs, - const size_t * samples_begin, - const size_t * samples_size, - size_t samples_count, - const llama_token * train_data, - size_t n_train_data, - bool separate_with_eos, - bool separate_with_bos, - bool fill_with_next_samples, - bool sample_random_offsets); - - -void mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state); -mt19937_state mt19937_get_state(const std::mt19937& rng); -mt19937_state mt19937_seed_to_state(unsigned seed); - -mt19937_state shuffle_samples( - const mt19937_state & rng_state, - size_t * shuffled_offs, - size_t * shuffled_begins, - size_t * shuffled_sizes, - const size_t * begins, - const size_t * sizes, - size_t count); - -size_t hash_combine(size_t h1, size_t h2); - -size_t compute_samples_hash( - const char* fn, - const size_t* samples_begin, - const size_t* samples_size, - size_t sample_count); - - -std::string replace_str(const char * s, const char * needle, const char * replacement); - -void print_duration(double milliseconds); - -float cosine_decay( - int64_t step, - int64_t decay_steps, - float minimum); - -float cosine_decay_restart( - int64_t step, - int64_t decay_steps, - float minimum, - float restart_step_mult); - -float learning_schedule( - int64_t step, - int64_t warmup_steps, - int64_t decay_steps, - float learning_rate, - float overall_minimum, - float cos_decay_minimum, - float cos_decay_restart_step_mult, - bool enable_restart); - -void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name); - -void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt); -void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt); - -bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train); -void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train); - -std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration); - -void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index f965e2c68f..5156d1dc52 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -72,7 +72,8 @@ class Model: def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False, use_temp_file: bool = False, eager: bool = False, metadata_override: Path | None = None, model_name: str | None = None, - split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False): + split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, + small_first_shard: bool = False, hparams: dict[str, Any] | None = None): if type(self) is Model: raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") @@ -87,7 +88,7 @@ class Model: self.is_safetensors = len(self.part_names) > 0 if not self.is_safetensors: self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin") - self.hparams = Model.load_hparams(self.dir_model) + self.hparams = Model.load_hparams(self.dir_model) if hparams is None else hparams self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None @@ -573,6 +574,9 @@ class Model: if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" + if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7": + # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5 + res = "bert-bge-large" if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": # ref: https://huggingface.co/mosaicml/mpt-7b res = "mpt" @@ -1538,6 +1542,17 @@ class LlamaModel(Model): special_vocab._set_special_token("eot", 32010) special_vocab.add_to_gguf(self.gguf_writer) + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams @@ -1554,17 +1569,6 @@ class LlamaModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - tokenizer_config_file = self.dir_model / 'tokenizer_config.json' - if tokenizer_config_file.is_file(): - with open(tokenizer_config_file, "r", encoding="utf-8") as f: - tokenizer_config_json = json.load(f) - if "add_prefix_space" in tokenizer_config_json: - self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) - - # Apply to granite small models only - if self.hparams.get("vocab_size", 32000) == 49152: - self.gguf_writer.add_add_bos_token(False) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -2703,7 +2707,7 @@ class XLMRobertaModel(BertModel): self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) self.gguf_writer.add_add_space_prefix(add_prefix) - self.gguf_writer.add_token_type_count(1) + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) if precompiled_charsmap: self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) @@ -2864,6 +2868,9 @@ class Rwkv6Model(Model): self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.chat_template = "rwkv-world" + # hack: Add '\n\n' as the EOT token to make it chat normally + special_vocab._set_special_token("eot", 261) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): @@ -3147,6 +3154,11 @@ class OlmoModel(Model): return [(self.map_tensor_name(name), data_torch)] +@Model.register("Olmo1124ForCausalLM") +class Olmo1124Model(Model): + model_arch = gguf.MODEL_ARCH.OLMO_1124 + + @Model.register("OlmoeForCausalLM") class OlmoeModel(Model): model_arch = gguf.MODEL_ARCH.OLMOE @@ -3855,10 +3867,7 @@ class JaisModel(Model): # Embeddings scale self.embeddings_scale = 1.0 - # note: For some JAIS flavors, output is tied to (same as) wte in original model - self.output_is_wte = False if 'mup_embeddings_scale' in self.hparams: - self.output_is_wte = True # Hack (?) self.embeddings_scale = self.hparams['mup_embeddings_scale'] elif 'embeddings_scale' in self.hparams: self.embeddings_scale = self.hparams['embeddings_scale'] @@ -3915,10 +3924,7 @@ class JaisModel(Model): if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): tensors.append((new_name, data_torch * self.embeddings_scale)) - if self.output_is_wte: - tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale)) elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): - assert not self.output_is_wte tensors.append((new_name, data_torch * self.width_scale)) else: tensors.append((new_name, data_torch)) diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 022354a3b6..28cd02e5a7 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -72,6 +72,7 @@ models = [ {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index 439a78de10..ed1014cae0 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -12,6 +12,7 @@ import json from math import prod from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast +from transformers import AutoConfig import torch @@ -230,7 +231,7 @@ def get_base_tensor_name(lora_tensor_name: str) -> str: def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( - description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file") + description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file") parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", @@ -256,17 +257,23 @@ def parse_args() -> argparse.Namespace: help="only print out what will be done, without writing any new files", ) parser.add_argument( - "--base", type=Path, required=True, - help="directory containing base model file", + "--base", type=Path, + help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config", ) parser.add_argument( "lora_path", type=Path, - help="directory containing LoRA adapter file", + help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)", ) return parser.parse_args() +def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]: + # normally, adapter does not come with base model config, we need to load it from AutoConfig + config = AutoConfig.from_pretrained(hf_model_id) + return config.to_dict() + + if __name__ == '__main__': args = parse_args() logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) @@ -281,7 +288,7 @@ if __name__ == '__main__': ftype = ftype_map[args.outtype] - dir_base_model: Path = args.base + dir_base_model: Path | None = args.base dir_lora: Path = args.lora_path lora_config = dir_lora / "adapter_config.json" input_model = dir_lora / "adapter_model.safetensors" @@ -301,9 +308,29 @@ if __name__ == '__main__': input_model = os.path.join(dir_lora, "adapter_model.bin") lora_model = torch.load(input_model, map_location="cpu", weights_only=True) + # load LoRA config + with open(lora_config, "r") as f: + lparams: dict[str, Any] = json.load(f) + # load base model - logger.info(f"Loading base model: {dir_base_model.name}") - hparams = Model.load_hparams(dir_base_model) + if dir_base_model is None: + if "base_model_name_or_path" in lparams: + model_id = lparams["base_model_name_or_path"] + logger.info(f"Loading base model from Hugging Face: {model_id}") + try: + hparams = load_hparams_from_hf(model_id) + except OSError as e: + logger.error(f"Failed to load base model config: {e}") + logger.error("Please try downloading the base model and add its path to --base") + sys.exit(1) + else: + logger.error("'base_model_name_or_path' is not found in adapter_config.json") + logger.error("Base model config is required. Please download the base model and add its path to --base") + sys.exit(1) + else: + logger.info(f"Loading base model: {dir_base_model.name}") + hparams = Model.load_hparams(dir_base_model) + with torch.inference_mode(): try: model_class = Model.from_model_architecture(hparams["architectures"][0]) @@ -323,13 +350,15 @@ if __name__ == '__main__': self.dir_model_card = dir_lora_model self.lora_alpha = float(lora_alpha) + def set_vocab(self): + pass + def set_type(self): self.gguf_writer.add_type(gguf.GGUFType.ADAPTER) self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora") def set_gguf_parameters(self): self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha) - super().set_gguf_parameters() def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: # Never add extra tensors (e.g. rope_freqs) for LoRA adapters @@ -348,6 +377,9 @@ if __name__ == '__main__': if ".base_layer.weight" in name: continue logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") + if ".embed_tokens.weight" in name or ".lm_head.weight" in name: + logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") + logger.error("Please refer to https://github.com/ggerganov/llama.cpp/pull/9948") sys.exit(1) if base_name in tensor_map: @@ -381,9 +413,6 @@ if __name__ == '__main__': yield (dest_name + ".lora_a", lora_a) yield (dest_name + ".lora_b", lora_b) - with open(lora_config, "r") as f: - lparams: dict[str, Any] = json.load(f) - alpha: float = lparams["lora_alpha"] model_instance = LoraModel( @@ -396,6 +425,7 @@ if __name__ == '__main__': dry_run=args.dry_run, dir_lora_model=dir_lora, lora_alpha=alpha, + hparams=hparams, ) logger.info("Exporting model...") diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index ea34182e41..8d8312e915 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -34,13 +34,16 @@ The SYCL backend would be broken by some PRs due to no online CI. The following release is verified with good quality: -|Commit ID|Tag|Release|Verified Platform| -|-|-|-|-| -|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| +|Commit ID|Tag|Release|Verified Platform| Update date| +|-|-|-|-|-| +|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| ## News +- 2024.11 + - Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer. - 2024.8 - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs. @@ -310,12 +313,14 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR # Build LLAMA with Nvidia BLAS acceleration through SYCL +# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance +GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture # Option 1: Use FP32 (recommended for better performance in most cases) -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # Option 2: Use FP16 -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON # build all binary cmake --build build --config Release -j -v @@ -333,8 +338,9 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithrocBLAS/include:$CPLUS_INCLUDE ## AMD # Use FP32, FP16 is not supported -# Find your GGML_SYCL_HIP_TARGET with rocminfo, under the key 'Name:' -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_HIP_TARGET=${GGML_SYCL_HIP_TARGET} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:' +GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # build all binary cmake --build build --config Release -j -v @@ -377,7 +383,7 @@ found 2 SYCL devices: |Chosen Device ID|Setting| |-|-| -|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action| +|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action| |1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`| |0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| @@ -644,6 +650,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 |--------------------|---------------------------------------|---------------------------------------------| | GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.
FP32 path - recommended for better perforemance than FP16 on quantized model| | GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. | +| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. | | GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. | | CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. | | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | diff --git a/docs/build.md b/docs/build.md index 4e362ebc78..359952b30f 100644 --- a/docs/build.md +++ b/docs/build.md @@ -186,13 +186,9 @@ The following compilation options are also available to tweak performance: | Option | Legal values | Default | Description | |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | -| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | -| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | | GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | | GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models | | GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | -| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | @@ -230,7 +226,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -- -j 16 ``` On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`. @@ -247,7 +243,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm ```bash HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ HIP_DEVICE_LIB_PATH= \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build -- -j 16 ``` @@ -259,7 +255,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% - cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release cmake --build build ``` Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) @@ -268,13 +264,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. -The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): - -| Option | Legal values | Default | Description | -|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | -| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | -| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | ### Vulkan @@ -282,9 +271,9 @@ The following compilation options are also available to tweak performance (yes, #### w64devkit -Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases). +Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases). -Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required. +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies: ```sh @@ -302,6 +291,29 @@ EOF ``` Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`. +#### Git Bash MINGW64 + +Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings + +Download and install [`Visual Studio Community Edition`](https://visualstudio.microsoft.com/) and make sure you select `C++` + +Download and install [`CMake`](https://cmake.org/download/) with the default settings + +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. + +Go into your `llama.cpp` directory and right click, select `Open Git Bash Here` and then run the following commands + +``` +cmake -B build -DGGML_VULKAN=ON +cmake --build build --config Release +``` + +Now you can load the model in conversation mode using `Vulkan` + +``` +build/bin/release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv +``` + #### MSYS2 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies. ```sh @@ -375,7 +387,7 @@ cmake --build build --config release You can test with: -`./build/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32` +`./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32` If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`: ```bash diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index ead630661c..d63a96c1c2 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -13,7 +13,6 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() add_subdirectory(cvector-generator) - add_subdirectory(baby-llama) add_subdirectory(batched-bench) add_subdirectory(batched) add_subdirectory(convert-llama2c-to-ggml) @@ -49,6 +48,7 @@ else() endif() add_subdirectory(save-load-state) add_subdirectory(simple) + add_subdirectory(simple-chat) add_subdirectory(speculative) add_subdirectory(tokenize) endif() diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt deleted file mode 100644 index 71b82105c8..0000000000 --- a/examples/baby-llama/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET llama-baby-llama) -add_executable(${TARGET} baby-llama.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp deleted file mode 100644 index 3ce91070b4..0000000000 --- a/examples/baby-llama/baby-llama.cpp +++ /dev/null @@ -1,1639 +0,0 @@ -#include "ggml.h" -#include "train.h" - -#include -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#ifdef LLAMA_DEFAULT_RMS_EPS -constexpr float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; -#else -constexpr float rms_norm_eps = 5e-6f; -#endif - -static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { - struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr); - - if (plan.work_size > 0) { - buf.resize(plan.work_size); - plan.work_data = buf.data(); - } - - ggml_graph_compute(graph, &plan); -} - -static struct ggml_tensor * randomize_tensor( - struct ggml_tensor * tensor, int ndims, const int64_t ne[], float fmin, float fmax -) { - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i0] = frand()*(fmax - fmin) + fmin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - } - break; - default: - assert(false); - } - - return tensor; -} - -struct llama_hparams { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? - uint32_t n_embd = 4096; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; - - bool operator!=(const llama_hparams & other) const { - return memcmp(this, &other, sizeof(llama_hparams)); - } -}; - -static uint32_t get_n_ff(const struct llama_hparams* hparams) { - const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult; - return n_ff; -} - -struct llama_hparams_lora { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? - uint32_t n_embd = 4096; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; - uint32_t n_lora = 64; - - bool operator!=(const llama_hparams_lora & other) const { - return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0; - } -}; - -struct llama_layer { - // normalization - struct ggml_tensor * attention_norm; - - // attention - struct ggml_tensor * wq; - struct ggml_tensor * wk; - struct ggml_tensor * wv; - struct ggml_tensor * wo; - - // normalization - struct ggml_tensor * ffn_norm; - - // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; -}; - -struct llama_layer_lora { - // normalization - struct ggml_tensor * attention_norm; - - // attention - struct ggml_tensor * wqa; - struct ggml_tensor * wqb; - struct ggml_tensor * wka; - struct ggml_tensor * wkb; - struct ggml_tensor * wva; - struct ggml_tensor * wvb; - struct ggml_tensor * woa; - struct ggml_tensor * wob; - - // normalization - struct ggml_tensor * ffn_norm; - - // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; -}; - - -struct llama_kv_cache { - struct ggml_context * ctx = NULL; - - struct ggml_tensor * k; - struct ggml_tensor * v; - - // llama_ctx_buffer buf; - - int n; // number of tokens currently in the cache -}; - -struct llama_model { - struct ggml_context * ctx = NULL; - - llama_hparams hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * output; - - std::vector layers; -}; - -struct llama_model_lora { - struct ggml_context * ctx = NULL; - - llama_hparams_lora hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * outputa; - struct ggml_tensor * outputb; - - std::vector layers; -}; - -static void init_model(struct llama_model * model) { - const auto & hparams = model->hparams; - - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - const uint32_t n_vocab = hparams.n_vocab; - - const uint32_t n_ff = get_n_ff(&hparams); - - struct ggml_context * ctx = model->ctx; - - model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab}); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd}); - model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("output.weight", {n_embd, n_vocab}); - - model->layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - // std::string layers_i = "layers." + std::to_string(i); - - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd}); - - layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - - layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd}); - - layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); - layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); - layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); - } -} - - -static void init_model_lora(struct llama_model_lora * model) { - const auto & hparams = model->hparams; - - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_mult = hparams.n_mult; - const uint32_t n_layer = hparams.n_layer; - const uint32_t n_vocab = hparams.n_vocab; - const uint32_t n_lora = hparams.n_lora; - - const uint32_t n_ff = ((2*(4*n_embd)/3 + n_mult - 1)/n_mult)*n_mult; - - struct ggml_context * ctx = model->ctx; - - model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab}); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd}); - model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // ("output.weight", {n_embd, n_vocab}); - model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // ("output.weight", {n_embd, n_vocab}); - - model->layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - // std::string layers_i = "layers." + std::to_string(i); - - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd}); - - layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - - layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd}); - - layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); - layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); - layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); - } -} - -static void set_param_model(struct llama_model * model) { - const auto& hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct ggml_context* ctx = model->ctx; - - ggml_set_param(ctx, model->tok_embeddings); - ggml_set_param(ctx, model->norm); - ggml_set_param(ctx, model->output); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - ggml_set_param(ctx, layer.attention_norm); - ggml_set_param(ctx, layer.wq); - ggml_set_param(ctx, layer.wk); - ggml_set_param(ctx, layer.wv); - ggml_set_param(ctx, layer.wo); - ggml_set_param(ctx, layer.ffn_norm); - ggml_set_param(ctx, layer.w1); - ggml_set_param(ctx, layer.w2); - ggml_set_param(ctx, layer.w3); - } -} - -static void set_param_model_lora(struct llama_model_lora * model) { - const auto& hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct ggml_context* ctx = model->ctx; - - ggml_set_param(ctx, model->tok_embeddings); - ggml_set_param(ctx, model->norm); - ggml_set_param(ctx, model->outputa); - ggml_set_param(ctx, model->outputb); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - ggml_set_param(ctx, layer.attention_norm); - ggml_set_param(ctx, layer.wqa); - ggml_set_param(ctx, layer.wqb); - ggml_set_param(ctx, layer.wka); - ggml_set_param(ctx, layer.wkb); - ggml_set_param(ctx, layer.wva); - ggml_set_param(ctx, layer.wvb); - ggml_set_param(ctx, layer.woa); - ggml_set_param(ctx, layer.wob); - ggml_set_param(ctx, layer.ffn_norm); - ggml_set_param(ctx, layer.w1); - ggml_set_param(ctx, layer.w2); - ggml_set_param(ctx, layer.w3); - } -} - -static void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) { - const auto & hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); - - randomize_tensor_normal(model->tok_embeddings , rnd); - randomize_tensor_normal(model->norm , rnd); - randomize_tensor_normal(model->output , rnd); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - randomize_tensor_normal(layer.attention_norm, rnd); - - randomize_tensor_normal(layer.wq, rnd); - randomize_tensor_normal(layer.wk, rnd); - randomize_tensor_normal(layer.wv, rnd); - randomize_tensor_normal(layer.wo, rnd); - - randomize_tensor_normal(layer.ffn_norm, rnd); - - randomize_tensor_normal(layer.w1, rnd); - randomize_tensor_normal(layer.w2, rnd); - randomize_tensor_normal(layer.w3, rnd); - } - - free_random_normal_distribution(rnd); -} - - -static void randomize_model_lora( - struct llama_model_lora * model, int seed, float mean, float std, float min, float max -) { - const auto & hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); - - randomize_tensor_normal(model->tok_embeddings, rnd); - randomize_tensor_normal(model->norm , rnd); - randomize_tensor_normal(model->outputa , rnd); - randomize_tensor_normal(model->outputb , rnd); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - randomize_tensor_normal(layer.attention_norm, rnd); - - randomize_tensor_normal(layer.wqa, rnd); - randomize_tensor_normal(layer.wqb, rnd); - randomize_tensor_normal(layer.wka, rnd); - randomize_tensor_normal(layer.wkb, rnd); - randomize_tensor_normal(layer.wva, rnd); - randomize_tensor_normal(layer.wvb, rnd); - randomize_tensor_normal(layer.woa, rnd); - randomize_tensor_normal(layer.wob, rnd); - - randomize_tensor_normal(layer.ffn_norm, rnd); - - randomize_tensor_normal(layer.w1, rnd); - randomize_tensor_normal(layer.w2, rnd); - randomize_tensor_normal(layer.w3, rnd); - } - - free_random_normal_distribution(rnd); -} - -static void init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) { - const auto & hparams = model->hparams; - - const uint32_t n_ctx = hparams.n_ctx; - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - - const int64_t n_mem = n_layer*n_ctx*n_batch; - const int64_t n_elements = n_embd*n_mem; - - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); - - // struct ggml_init_params params; - // params.mem_size = cache.buf.size; - // params.mem_buffer = cache.buf.addr; - // params.no_alloc = false; - if (!cache->ctx) { - struct ggml_init_params params; - params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; - params.mem_buffer = NULL; - params.no_alloc = false; - - cache->ctx = ggml_init(params); - - if (!cache->ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - exit(1); - } - } - - cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); -} - -static bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) { - const auto & hparams = model->hparams; - - const uint32_t n_ctx = hparams.n_ctx; - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - - const int64_t n_mem = n_layer*n_ctx*n_batch; - const int64_t n_elements = n_embd*n_mem; - - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); - - // struct ggml_init_params params; - // params.mem_size = cache.buf.size; - // params.mem_buffer = cache.buf.addr; - // params.no_alloc = false; - if (!cache->ctx) { - struct ggml_init_params params; - params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; - params.mem_buffer = NULL; - params.no_alloc = false; - - cache->ctx = ggml_init(params); - - if (!cache->ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - } - - cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - - return true; -} - -static struct ggml_tensor * forward( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N,1,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [n_embd, N, 1, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N))); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // kv_self.v shape [n_embd * n_ctx * n_layer, 1] - // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] - // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - } - - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Q shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // K shape [n_embd/n_head, n_past + N, n_head, 1] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - // KQ shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // split cached V into n_head heads - //// V shape [n_past + N, n_embd/n_head, n_head, 1] - // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] - struct ggml_tensor * V = - ggml_view_3d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(vc), - n_ctx*ggml_element_size(vc)*n_embd/n_head, - il*n_ctx*ggml_element_size(vc)*n_embd); - - // KQV shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - - // cur = ffn_norm*cur - // cur shape [n_embd,N,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - } - - // tmp shape [n_ff,N,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - - // SILU activation - // cur shape [n_ff,N,1,1] - cur = ggml_silu(ctx0, cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul(ctx0, cur, tmp); - - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - } - - // cur shape [n_embd,N,1,1] - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - // inpL shape [n_embd,N,1,1] - inpL = cur; - } - - // norm - { - - // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // inpL = norm*inpL - // inpL shape [n_embd,N,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static struct ggml_tensor * forward_batch( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past, - const int n_batch -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); - memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N*n_batch,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - assert_shape_2d(inpL, n_embd, N*n_batch); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); - assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); - assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [N, n_embd, n_batch, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wv, - cur), - n_embd, N, n_batch), - 1, 0, 2, 3)); - - assert_shape_3d(Vcur, N, n_embd, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il] - // v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_2d(ctx0, kc, - ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch), - ggml_element_size(kc)*n_embd*n_ctx, - (ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, - ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch), - ggml_element_size(vc)*n_ctx*n_embd, - ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx)); - - assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer); - assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer); - } - - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Q shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // K shape [n_embd/n_head, n_past + N, n_head, n_batch] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_4d(ctx0, - ggml_view_3d(ctx0, - kc, - n_embd, - (n_past + N), - n_batch, - n_embd*ggml_element_size(kc), - n_ctx*n_embd*ggml_element_size(kc), - il*n_batch*n_ctx*n_embd*ggml_element_size(kc)), - n_embd/n_head, n_head, n_past + N, n_batch), - 0, 2, 1, 3); - assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch); - - // K * Q - // KQ shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - assert_shape_4d(KQ, n_past + N, N, n_head, n_batch); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch); - - // split cached V into n_head heads - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il] - struct ggml_tensor * V = - ggml_view_4d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, n_batch, - ggml_element_size(vc)*n_ctx, - ggml_element_size(vc)*n_ctx*n_embd/n_head, - ggml_element_size(vc)*n_ctx*n_embd, - il*n_batch*n_ctx*n_embd*ggml_element_size(vc)); - assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch); - - // KQV shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch); - assert_shape_2d(cur, n_embd, N*n_batch); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N*n_batch,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - assert_shape_2d(inpFF, n_embd, N*n_batch); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = ffn_norm*cur - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // tmp shape [n_ff,N*n_batch,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - assert_shape_2d(tmp, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // SILU activation - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_silu(ctx0, cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul(ctx0, cur, tmp); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_add(ctx0, cur, inpFF); - assert_shape_2d(cur, n_embd, N*n_batch); - - // input for next layer - // inpL shape [n_embd,N*n_batch,1,1] - inpL = cur; - assert_shape_2d(inpL, n_embd, N*n_batch); - } - - // norm - { - - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(inpL, n_embd, N*n_batch); - - // inpL = norm*inpL - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - assert_shape_2d(inpL, n_embd, N*n_batch); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N*n_batch,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - assert_shape_2d(inpL, n_vocab, N*n_batch); - - { - // inpL shape [n_vocab,N,n_batch,1] - inpL = ggml_reshape_3d(ctx0, - inpL, - n_vocab, N, n_batch); - assert_shape_3d(inpL, n_vocab, N, n_batch); - } - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static struct ggml_tensor * forward_lora( - struct llama_model_lora * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N,1,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wqa, - ggml_mul_mat(ctx0, - model->layers[il].wqb, - cur)), - n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wka, - ggml_mul_mat(ctx0, - model->layers[il].wkb, - cur)), - n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [n_embd, N, 1, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, - ggml_transpose(ctx0, - ggml_reshape_2d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wva, - ggml_mul_mat(ctx0, - model->layers[il].wvb, - cur)), - n_embd, N))); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // kv_self.v shape [n_embd * n_ctx * n_layer, 1] - // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] - // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - } - - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Q shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // K shape [n_embd/n_head, n_past + N, n_head, 1] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - // KQ shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // split cached V into n_head heads - //// V shape [n_past + N, n_embd/n_head, n_head, 1] - // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] - struct ggml_tensor * V = - ggml_view_3d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(vc), - n_ctx*ggml_element_size(vc)*n_embd/n_head, - il*n_ctx*ggml_element_size(vc)*n_embd); - - // KQV shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].woa, - ggml_mul_mat(ctx0, - model->layers[il].wob, - cur)); - } - - // inpFF shape [n_embd,N,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - - // cur = ffn_norm*cur - // cur shape [n_embd,N,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - } - - // tmp shape [n_ff,N,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - - // SILU activation - // cur shape [n_ff,N,1,1] - cur = ggml_silu(ctx0, cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul(ctx0, cur, tmp); - - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - } - - // cur shape [n_embd,N,1,1] - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - // inpL shape [n_embd,N,1,1] - inpL = cur; - } - - // norm - { - - // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // inpL = norm*inpL - // inpL shape [n_embd,N,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - //embeddings = inpL; - } - - - // lm_head - // inpL shape [n_vocab,N,1,1] - inpL = ggml_mul_mat(ctx0, - model->outputa, - ggml_mul_mat(ctx0, - model->outputb, - inpL)); - - // ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { - assert(ggml_is_matrix(logits)); - assert(ggml_is_matrix(probs)); - assert(ggml_is_vector(best_samples)); - assert(logits->ne[1] == best_samples->ne[0]); - assert(logits->ne[0] == probs->ne[0]); - assert(logits->ne[1] == probs->ne[1]); - for (int i = 0; i < logits->ne[1]; ++i) { - float max_logit = ggml_get_f32_1d(logits, i * logits->ne[0]); - ggml_set_i32_1d(best_samples, i, 0); - for (int k = 0; k < logits->ne[0]; ++k) { - float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k); - if (logit > max_logit) { - max_logit = logit; - ggml_set_i32_1d(best_samples, i, k); - } - } - float psum = 0; - for (int k = 0; k < logits->ne[0]; ++k) { - float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k); - float p = (logit == -INFINITY) ? 0 : expf(logit - max_logit); - psum += p; - ggml_set_f32_1d(probs, i * probs->ne[0] + k, p); - } - for (int k = 0; k < logits->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - ggml_set_f32_1d(probs, i * probs->ne[0] + k, p / psum); - } - } -} - -static void sample_softmax_batch( - struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs, - struct ggml_tensor * best_samples -) { - GGML_ASSERT(ggml_is_matrix(best_samples)); - GGML_ASSERT(ggml_is_3d(logits)); - GGML_ASSERT(ggml_is_3d(probs)); - int n_tokens = best_samples->ne[0]; - int n_batch = best_samples->ne[1]; - int n_vocab = logits->ne[0]; - GGML_ASSERT(n_tokens == logits->ne[1]); - GGML_ASSERT(n_batch == logits->ne[2]); - GGML_ASSERT(n_vocab == probs->ne[0]); - GGML_ASSERT(n_tokens == probs->ne[1]); - GGML_ASSERT(n_batch == probs->ne[2]); - - for (int k = 0; k < n_batch; ++k) { - struct ggml_tensor * best_samples_k = ggml_view_1d(ctx, - best_samples, - best_samples->ne[0], - k*best_samples->nb[1]); - struct ggml_tensor * logits_k = ggml_view_2d(ctx, - logits, - logits->ne[0], - logits->ne[1], - logits->nb[1], - k*logits->nb[2]); - struct ggml_tensor * probs_k = ggml_view_2d(ctx, - probs, - probs->ne[0], - probs->ne[1], - probs->nb[1], - k*probs->nb[2]); - sample_softmax(logits_k, probs_k, best_samples_k); - } -} - -static void print_row(struct ggml_tensor * probs, int i) { - for (int k = 0; k < probs->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - printf(" %.2f", p); - } - printf("\n"); -} - -static void print_matrix(struct ggml_tensor * probs) { - assert(ggml_is_matrix(probs)); - for (int i = 0; i < probs->ne[1]; ++i) { - for (int k = 0; k < probs->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - printf(" %.2f", p); - } - printf("\n"); - } -} - -static void print_token(int token, int n_vocab) { - for (int k = 0; k < token; ++k) { - printf(" "); - } - printf("X"); - for (int k = token+1; k < n_vocab; ++k) { - printf(" "); - } - printf("\n"); -} - -static void print_tokens(struct ggml_tensor * tokens, int n_vocab) { - for (int i=0; ine[0]; ++i) { - int token = ggml_get_i32_1d(tokens, i); - print_token(token, n_vocab); - } -} - -static void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) { - int n_tokens = tokens_input->ne[0]; - int n_vocab = targets->ne[0]; - float randomness = 0.0f; - // ggml_set_zero(targets); - ggml_set_f32(targets, -1.0f); - ggml_set_i32_1d(tokens_input, 0, 0); - for (int i=1; i 1.0f) ? 1.0f : z; // clamp to [0..1] - int token = std::max(1,std::min(1+(int)(z*(float)(n_vocab-1)), n_vocab-1)); - ggml_set_f32_1d(targets, (i-1)*n_vocab + token, +1.0f); - if (ine[0]; - int n_batch = tokens_input->ne[1]; - GGML_ASSERT(n_tokens == targets->ne[1]); - GGML_ASSERT(n_batch == targets->ne[2]); - - for (int k=0; kne[0], - k*tokens_input->nb[1]); - struct ggml_tensor * targets_k = ggml_view_2d(ctx, - targets, - targets->ne[0], - targets->ne[1], - targets->nb[1], - k*targets->nb[2]); - get_example_targets(example_id*n_batch + k, tokens_input_k, targets_k); - } -} - -static void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) { - int n_tokens = tokens_input->ne[0]; - int n_vocab = targets->ne[0]; - for (int i=0; i work_buffer; - - for (int ex=0; ex&2 "Couldn't get number of tokens from ./llama-cli output!" exit 1 fi - n_tokens=$(($(cut -d/ -f2 <<<"$session_size_msg") + $(cut -d/ -f2 <<<"$sample_time_msg"))) + n_tokens=$(awk '{sum+=$1} END {print sum}' <<< "$(cut -d/ -f2 <<< "$session_and_sample_msg")") if ((n_tokens > CTX_ROTATE_POINT)); then tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE" diff --git a/examples/convert_legacy_llama.py b/examples/convert_legacy_llama.py index 9ab9ab06ed..c4ec5c524e 100755 --- a/examples/convert_legacy_llama.py +++ b/examples/convert_legacy_llama.py @@ -840,6 +840,8 @@ class OutputFile: self.gguf.add_base_model_version(key, base_model_entry["version"]) if "organization" in base_model_entry: self.gguf.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + self.gguf.add_base_model_description(key, base_model_entry["description"]) if "url" in base_model_entry: self.gguf.add_base_model_url(key, base_model_entry["url"]) if "doi" in base_model_entry: @@ -849,12 +851,32 @@ class OutputFile: if "repo_url" in base_model_entry: self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"]) + if metadata.datasets is not None: + self.gguf.add_dataset_count(len(metadata.datasets)) + for key, dataset_entry in enumerate(metadata.datasets): + if "name" in dataset_entry: + self.gguf.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + self.gguf.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + self.gguf.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + self.gguf.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + self.gguf.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + self.gguf.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + self.gguf.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + self.gguf.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + self.gguf.add_dataset_repo_url(key, dataset_entry["repo_url"]) + if metadata.tags is not None: self.gguf.add_tags(metadata.tags) if metadata.languages is not None: self.gguf.add_languages(metadata.languages) - if metadata.datasets is not None: - self.gguf.add_datasets(metadata.datasets) def add_meta_arch(self, params: Params) -> None: # Metadata About The Neural Architecture Itself diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index 69e141ecb9..d1731bba64 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -339,7 +339,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) { static bool get_hidden_layers(llama_context * ctx, std::vector & tokens) { llama_kv_cache_clear(ctx); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { fprintf(stderr, "%s : failed to eval\n", __func__); return false; } diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index fb52db4e15..c08e3e5f67 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -131,7 +131,7 @@ static bool run(llama_context * ctx, const common_params & params) { std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { LOG_ERR("%s : failed to eval\n", __func__); return false; } diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index d1ff3e8bc4..70ff47768c 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -496,6 +496,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -508,9 +510,14 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use batch.logits to save computations instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return false; } @@ -523,6 +530,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index f82c614f57..15b358dc4e 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -43,50 +43,6 @@ static std::vector * g_output_tokens; static bool is_interacting = false; -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const std::vector & input_tokens, const std::string & output, - const std::vector & output_tokens -) { - if (params.logdir.empty()) { - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_ERR("%s: warning: failed to create logdir %s, cannot write logfile\n", - __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: infill\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Generation Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_string_multiline(logfile, "output", output.c_str()); - yaml_dump_vector_int(logfile, "output_tokens", output_tokens); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { @@ -96,7 +52,6 @@ static void sigint_handler(int signo) { console::cleanup(); LOG("\n"); common_perf_print(*g_ctx, *g_smpl); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); // make sure all logs are flushed LOG("Interrupted by user\n"); @@ -396,7 +351,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } @@ -625,7 +580,6 @@ int main(int argc, char ** argv) { LOG("\n"); common_perf_print(ctx, smpl); - write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); llama_free(ctx); llama_free_model(model); diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index a8779bf3bc..fc9f0097f5 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -540,7 +540,7 @@ class SchemaConverter: return self._add_rule( name, to_rule(transform()) if self._raw_pattern \ - else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space") + else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space") def _resolve_ref(self, ref): diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c22bdedcfa..3dc84a75cb 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -6,34 +6,28 @@ #include #include #include +#include #include #include -#include #include #include #include #include #include #include -#include #include +#include +#include "common.h" #include "ggml.h" #include "llama.h" -#include "common.h" -#include "ggml-cuda.h" -#include "ggml-sycl.h" - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif #ifdef _WIN32 -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -# define NOMINMAX -#endif -#include +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include #endif // utils @@ -42,8 +36,7 @@ static uint64_t get_time_ns() { return std::chrono::nanoseconds(clock::now().time_since_epoch()).count(); } -template -static std::string join(const std::vector & values, const std::string & delim) { +template static std::string join(const std::vector & values, const std::string & delim) { std::ostringstream str; for (size_t i = 0; i < values.size(); i++) { str << values[i]; @@ -54,137 +47,73 @@ static std::string join(const std::vector & values, const std::string & delim return str.str(); } -template -static std::vector transform_to_str(const std::vector & values, F f) { +template static std::vector transform_to_str(const std::vector & values, F f) { std::vector str_values; std::transform(values.begin(), values.end(), std::back_inserter(str_values), f); return str_values; } -template -static T avg(const std::vector & v) { +template static T avg(const std::vector & v) { if (v.empty()) { return 0; } T sum = std::accumulate(v.begin(), v.end(), T(0)); - return sum / (T)v.size(); + return sum / (T) v.size(); } -template -static T stdev(const std::vector & v) { +template static T stdev(const std::vector & v) { if (v.size() <= 1) { return 0; } - T mean = avg(v); + T mean = avg(v); T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0)); - T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1)); + T stdev = std::sqrt(sq_sum / (T) (v.size() - 1) - mean * mean * (T) v.size() / (T) (v.size() - 1)); return stdev; } static std::string get_cpu_info() { - std::string id; -#ifdef __linux__ - FILE * f = fopen("/proc/cpuinfo", "r"); - if (f) { - char buf[1024]; - while (fgets(buf, sizeof(buf), f)) { - if (strncmp(buf, "model name", 10) == 0) { - char * p = strchr(buf, ':'); - if (p) { - p++; - while (std::isspace(*p)) { - p++; - } - while (std::isspace(p[strlen(p) - 1])) { - p[strlen(p) - 1] = '\0'; - } - id = p; - break; - } - } - } - fclose(f); - } -#elif defined(_WIN32) - HKEY hKey; - if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, - TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), - 0, - KEY_READ, - &hKey) != ERROR_SUCCESS) { - // fail to open registry key - return ""; - } - char cpu_brand[256]; - DWORD cpu_brand_size = sizeof(cpu_brand); - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - (LPBYTE)cpu_brand, - &cpu_brand_size) == ERROR_SUCCESS) { - id.assign(cpu_brand, cpu_brand_size); - if (id.find('\0') != std::string::npos) { - id.resize(id.find('\0')); + std::vector cpu_list; + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) { + cpu_list.push_back(ggml_backend_dev_description(dev)); } } - RegCloseKey(hKey); -#endif - // TODO: other platforms - return id; + return join(cpu_list, ", "); } static std::string get_gpu_info() { - std::string id; -#ifdef GGML_USE_CUDA - int count = ggml_backend_cuda_get_device_count(); - for (int i = 0; i < count; i++) { - char buf[128]; - ggml_backend_cuda_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; + std::vector gpu_list; + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) { + gpu_list.push_back(ggml_backend_dev_description(dev)); } } -#endif -#ifdef GGML_USE_SYCL - int count = ggml_backend_sycl_get_device_count(); - for (int i = 0; i < count; i++) { - char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; - } - } -#endif -#ifdef GGML_USE_CANN - uint32_t count = ggml_backend_cann_get_device_count(); - for (uint32_t i = 0; i < count; i++) { - char buf[128]; - ggml_backend_cann_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; - } - } -#endif - // TODO: other backends - return id; + return join(gpu_list, ", "); } // command line params -enum output_formats {NONE, CSV, JSON, JSONL, MARKDOWN, SQL}; +enum output_formats { NONE, CSV, JSON, JSONL, MARKDOWN, SQL }; static const char * output_format_str(output_formats format) { switch (format) { - case NONE: return "none"; - case CSV: return "csv"; - case JSON: return "json"; - case JSONL: return "jsonl"; - case MARKDOWN: return "md"; - case SQL: return "sql"; - default: GGML_ABORT("invalid output format"); + case NONE: + return "none"; + case CSV: + return "csv"; + case JSON: + return "json"; + case JSONL: + return "jsonl"; + case MARKDOWN: + return "md"; + case SQL: + return "sql"; + default: + GGML_ABORT("invalid output format"); } } @@ -209,10 +138,14 @@ static bool output_format_from_str(const std::string & s, output_formats & forma static const char * split_mode_str(llama_split_mode mode) { switch (mode) { - case LLAMA_SPLIT_MODE_NONE: return "none"; - case LLAMA_SPLIT_MODE_LAYER: return "layer"; - case LLAMA_SPLIT_MODE_ROW: return "row"; - default: GGML_ABORT("invalid split mode"); + case LLAMA_SPLIT_MODE_NONE: + return "none"; + case LLAMA_SPLIT_MODE_LAYER: + return "layer"; + case LLAMA_SPLIT_MODE_ROW: + return "row"; + default: + GGML_ABORT("invalid split mode"); } } @@ -223,59 +156,59 @@ static std::string pair_str(const std::pair & p) { } struct cmd_params { - std::vector model; - std::vector n_prompt; - std::vector n_gen; + std::vector model; + std::vector n_prompt; + std::vector n_gen; std::vector> n_pg; - std::vector n_batch; - std::vector n_ubatch; - std::vector type_k; - std::vector type_v; - std::vector n_threads; - std::vector cpu_mask; - std::vector cpu_strict; - std::vector poll; - std::vector n_gpu_layers; - std::vector rpc_servers; - std::vector split_mode; - std::vector main_gpu; - std::vector no_kv_offload; - std::vector flash_attn; - std::vector> tensor_split; - std::vector use_mmap; - std::vector embeddings; - ggml_numa_strategy numa; - int reps; - ggml_sched_priority prio; - int delay; - bool verbose; - bool progress; - output_formats output_format; - output_formats output_format_stderr; + std::vector n_batch; + std::vector n_ubatch; + std::vector type_k; + std::vector type_v; + std::vector n_threads; + std::vector cpu_mask; + std::vector cpu_strict; + std::vector poll; + std::vector n_gpu_layers; + std::vector rpc_servers; + std::vector split_mode; + std::vector main_gpu; + std::vector no_kv_offload; + std::vector flash_attn; + std::vector> tensor_split; + std::vector use_mmap; + std::vector embeddings; + ggml_numa_strategy numa; + int reps; + ggml_sched_priority prio; + int delay; + bool verbose; + bool progress; + output_formats output_format; + output_formats output_format_stderr; }; static const cmd_params cmd_params_defaults = { - /* model */ {"models/7B/ggml-model-q4_0.gguf"}, - /* n_prompt */ {512}, - /* n_gen */ {128}, + /* model */ { "models/7B/ggml-model-q4_0.gguf" }, + /* n_prompt */ { 512 }, + /* n_gen */ { 128 }, /* n_pg */ {}, - /* n_batch */ {2048}, - /* n_ubatch */ {512}, - /* type_k */ {GGML_TYPE_F16}, - /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {cpu_get_num_math()}, - /* cpu_mask */ {"0x0"}, - /* cpu_strict */ {false}, - /* poll */ {50}, - /* n_gpu_layers */ {99}, - /* rpc_servers */ {""}, - /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, - /* main_gpu */ {0}, - /* no_kv_offload */ {false}, - /* flash_attn */ {false}, - /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, - /* use_mmap */ {true}, - /* embeddings */ {false}, + /* n_batch */ { 2048 }, + /* n_ubatch */ { 512 }, + /* type_k */ { GGML_TYPE_F16 }, + /* type_v */ { GGML_TYPE_F16 }, + /* n_threads */ { cpu_get_num_math() }, + /* cpu_mask */ { "0x0" }, + /* cpu_strict */ { false }, + /* poll */ { 50 }, + /* n_gpu_layers */ { 99 }, + /* rpc_servers */ { "" }, + /* split_mode */ { LLAMA_SPLIT_MODE_LAYER }, + /* main_gpu */ { 0 }, + /* no_kv_offload */ { false }, + /* flash_attn */ { false }, + /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, + /* use_mmap */ { true }, + /* embeddings */ { false }, /* numa */ GGML_NUMA_STRATEGY_DISABLED, /* reps */ 5, /* prio */ GGML_SCHED_PRIO_NORMAL, @@ -292,44 +225,68 @@ static void print_usage(int /* argc */, char ** argv) { printf("options:\n"); printf(" -h, --help\n"); printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", + join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" -pg (default: %s)\n", + join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", + join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", + join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", + join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", + join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", + join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", + join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", + join(cmd_params_defaults.cpu_strict, ",").c_str()); printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", + join(cmd_params_defaults.n_gpu_layers, ",").c_str()); if (llama_supports_rpc()) { - printf(" -rpc, --rpc (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str()); + printf(" -rpc, --rpc (default: %s)\n", + join(cmd_params_defaults.rpc_servers, ",").c_str()); } - printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); - printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", + join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", + join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", + join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn <0|1> (default: %s)\n", + join(cmd_params_defaults.flash_attn, ",").c_str()); + printf(" -mmp, --mmap <0|1> (default: %s)\n", + join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" --numa (default: disabled)\n"); - printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", + join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio); printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay); - printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); - printf(" -oe, --output-err (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); + printf(" -o, --output (default: %s)\n", + output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err (default: %s)\n", + output_format_str(cmd_params_defaults.output_format_stderr)); printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf(" --progress (default: %s)\n", cmd_params_defaults.progress ? "1" : "0"); printf("\n"); - printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); + printf( + "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter " + "multiple times.\n"); } static ggml_type ggml_type_from_name(const std::string & s) { if (s == "f16") { return GGML_TYPE_F16; } + if (s == "bf16") { + return GGML_TYPE_BF16; + } if (s == "q8_0") { return GGML_TYPE_Q8_0; } @@ -352,22 +309,21 @@ static ggml_type ggml_type_from_name(const std::string & s) { return GGML_TYPE_COUNT; } - static cmd_params parse_cmd_params(int argc, char ** argv) { - cmd_params params; - std::string arg; - bool invalid_param = false; - const std::string arg_prefix = "--"; - const char split_delim = ','; + cmd_params params; + std::string arg; + bool invalid_param = false; + const std::string arg_prefix = "--"; + const char split_delim = ','; - params.verbose = cmd_params_defaults.verbose; - params.output_format = cmd_params_defaults.output_format; + params.verbose = cmd_params_defaults.verbose; + params.output_format = cmd_params_defaults.output_format; params.output_format_stderr = cmd_params_defaults.output_format_stderr; - params.reps = cmd_params_defaults.reps; - params.numa = cmd_params_defaults.numa; - params.prio = cmd_params_defaults.prio; - params.delay = cmd_params_defaults.delay; - params.progress = cmd_params_defaults.progress; + params.reps = cmd_params_defaults.reps; + params.numa = cmd_params_defaults.numa; + params.prio = cmd_params_defaults.prio; + params.delay = cmd_params_defaults.delay; + params.progress = cmd_params_defaults.progress; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -409,7 +365,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])}); + params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) }); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -429,7 +385,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -448,7 +404,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -508,7 +464,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector modes; for (const auto & m : p) { llama_split_mode mode; @@ -547,10 +503,16 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } else { std::string value(argv[i]); - /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } - else { invalid_param = true; break; } + /**/ if (value == "distribute" || value == "") { + params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; + } else if (value == "isolate") { + params.numa = GGML_NUMA_STRATEGY_ISOLATE; + } else if (value == "numactl") { + params.numa = GGML_NUMA_STRATEGY_NUMACTL; + } else { + invalid_param = true; + break; + } } } else if (arg == "-fa" || arg == "--flash-attn") { if (++i >= argc) { @@ -580,9 +542,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } for (auto ts : string_split(argv[i], split_delim)) { // split string by ; and / - const std::regex regex{R"([;/]+)"}; - std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; - std::vector split_arg{it, {}}; + const std::regex regex{ R"([;/]+)" }; + std::sregex_token_iterator it{ ts.begin(), ts.end(), regex, -1 }; + std::vector split_arg{ it, {} }; GGML_ASSERT(split_arg.size() <= llama_max_devices()); std::vector tensor_split(llama_max_devices()); @@ -641,52 +603,94 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } // set defaults - if (params.model.empty()) { params.model = cmd_params_defaults.model; } - if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; } - if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; } - if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; } - if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } - if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; } - if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } - if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } - if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } - if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; } - if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } - if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } - if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; } - if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } - if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } - if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } - if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } - if (params.cpu_mask.empty()) { params.cpu_mask = cmd_params_defaults.cpu_mask; } - if (params.cpu_strict.empty()) { params.cpu_strict = cmd_params_defaults.cpu_strict; } - if (params.poll.empty()) { params.poll = cmd_params_defaults.poll; } + if (params.model.empty()) { + params.model = cmd_params_defaults.model; + } + if (params.n_prompt.empty()) { + params.n_prompt = cmd_params_defaults.n_prompt; + } + if (params.n_gen.empty()) { + params.n_gen = cmd_params_defaults.n_gen; + } + if (params.n_pg.empty()) { + params.n_pg = cmd_params_defaults.n_pg; + } + if (params.n_batch.empty()) { + params.n_batch = cmd_params_defaults.n_batch; + } + if (params.n_ubatch.empty()) { + params.n_ubatch = cmd_params_defaults.n_ubatch; + } + if (params.type_k.empty()) { + params.type_k = cmd_params_defaults.type_k; + } + if (params.type_v.empty()) { + params.type_v = cmd_params_defaults.type_v; + } + if (params.n_gpu_layers.empty()) { + params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; + } + if (params.rpc_servers.empty()) { + params.rpc_servers = cmd_params_defaults.rpc_servers; + } + if (params.split_mode.empty()) { + params.split_mode = cmd_params_defaults.split_mode; + } + if (params.main_gpu.empty()) { + params.main_gpu = cmd_params_defaults.main_gpu; + } + if (params.no_kv_offload.empty()) { + params.no_kv_offload = cmd_params_defaults.no_kv_offload; + } + if (params.flash_attn.empty()) { + params.flash_attn = cmd_params_defaults.flash_attn; + } + if (params.tensor_split.empty()) { + params.tensor_split = cmd_params_defaults.tensor_split; + } + if (params.use_mmap.empty()) { + params.use_mmap = cmd_params_defaults.use_mmap; + } + if (params.embeddings.empty()) { + params.embeddings = cmd_params_defaults.embeddings; + } + if (params.n_threads.empty()) { + params.n_threads = cmd_params_defaults.n_threads; + } + if (params.cpu_mask.empty()) { + params.cpu_mask = cmd_params_defaults.cpu_mask; + } + if (params.cpu_strict.empty()) { + params.cpu_strict = cmd_params_defaults.cpu_strict; + } + if (params.poll.empty()) { + params.poll = cmd_params_defaults.poll; + } return params; } struct cmd_params_instance { - std::string model; - int n_prompt; - int n_gen; - int n_batch; - int n_ubatch; - ggml_type type_k; - ggml_type type_v; - int n_threads; - std::string cpu_mask; - bool cpu_strict; - int poll; - int n_gpu_layers; - std::string rpc_servers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; - bool flash_attn; + std::string model; + int n_prompt; + int n_gen; + int n_batch; + int n_ubatch; + ggml_type type_k; + ggml_type type_v; + int n_threads; + std::string cpu_mask; + bool cpu_strict; + int poll; + int n_gpu_layers; + std::string rpc_servers; + llama_split_mode split_mode; + int main_gpu; + bool no_kv_offload; + bool flash_attn; std::vector tensor_split; - bool use_mmap; - bool embeddings; + bool use_mmap; + bool embeddings; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -695,35 +699,31 @@ struct cmd_params_instance { if (!rpc_servers.empty()) { mparams.rpc_servers = rpc_servers.c_str(); } - mparams.split_mode = split_mode; - mparams.main_gpu = main_gpu; + mparams.split_mode = split_mode; + mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); - mparams.use_mmap = use_mmap; + mparams.use_mmap = use_mmap; return mparams; } bool equal_mparams(const cmd_params_instance & other) const { - return model == other.model && - n_gpu_layers == other.n_gpu_layers && - rpc_servers == other.rpc_servers && - split_mode == other.split_mode && - main_gpu == other.main_gpu && - use_mmap == other.use_mmap && + return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers && + split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && tensor_split == other.tensor_split; } llama_context_params to_llama_cparams() const { llama_context_params cparams = llama_context_default_params(); - cparams.n_ctx = n_prompt + n_gen; - cparams.n_batch = n_batch; - cparams.n_ubatch = n_ubatch; - cparams.type_k = type_k; - cparams.type_v = type_v; + cparams.n_ctx = n_prompt + n_gen; + cparams.n_batch = n_batch; + cparams.n_ubatch = n_ubatch; + cparams.type_k = type_k; + cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; - cparams.flash_attn = flash_attn; - cparams.embeddings = embeddings; + cparams.flash_attn = flash_attn; + cparams.embeddings = embeddings; return cparams; } @@ -733,6 +733,7 @@ static std::vector get_cmd_params_instances(const cmd_param std::vector instances; // this ordering minimizes the number of times that each model needs to be reloaded + // clang-format off for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) for (const auto & rpc : params.rpc_servers) @@ -838,165 +839,125 @@ static std::vector get_cmd_params_instances(const cmd_param instances.push_back(instance); } } + // clang-format on return instances; } struct test { static const std::string build_commit; - static const int build_number; - static const bool cuda; - static const bool vulkan; - static const bool kompute; - static const bool metal; - static const bool sycl; - static const bool gpu_blas; - static const bool blas; + static const int build_number; static const std::string cpu_info; static const std::string gpu_info; - std::string model_filename; - std::string model_type; - uint64_t model_size; - uint64_t model_n_params; - int n_batch; - int n_ubatch; - int n_threads; - std::string cpu_mask; - bool cpu_strict; - int poll; - bool has_rpc; - ggml_type type_k; - ggml_type type_v; - int n_gpu_layers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; - bool flash_attn; - std::vector tensor_split; - bool use_mmap; - bool embeddings; - int n_prompt; - int n_gen; - std::string test_time; - std::vector samples_ns; + std::string model_filename; + std::string model_type; + uint64_t model_size; + uint64_t model_n_params; + int n_batch; + int n_ubatch; + int n_threads; + std::string cpu_mask; + bool cpu_strict; + int poll; + ggml_type type_k; + ggml_type type_v; + int n_gpu_layers; + llama_split_mode split_mode; + int main_gpu; + bool no_kv_offload; + bool flash_attn; + std::vector tensor_split; + bool use_mmap; + bool embeddings; + int n_prompt; + int n_gen; + std::string test_time; + std::vector samples_ns; test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) { model_filename = inst.model; char buf[128]; llama_model_desc(lmodel, buf, sizeof(buf)); - model_type = buf; - model_size = llama_model_size(lmodel); + model_type = buf; + model_size = llama_model_size(lmodel); model_n_params = llama_model_n_params(lmodel); - n_batch = inst.n_batch; - n_ubatch = inst.n_ubatch; - n_threads = inst.n_threads; - cpu_mask = inst.cpu_mask; - cpu_strict = inst.cpu_strict; - poll = inst.poll; - has_rpc = !inst.rpc_servers.empty(); - type_k = inst.type_k; - type_v = inst.type_v; - n_gpu_layers = inst.n_gpu_layers; - split_mode = inst.split_mode; - main_gpu = inst.main_gpu; - no_kv_offload = inst.no_kv_offload; - flash_attn = inst.flash_attn; - tensor_split = inst.tensor_split; - use_mmap = inst.use_mmap; - embeddings = inst.embeddings; - n_prompt = inst.n_prompt; - n_gen = inst.n_gen; + n_batch = inst.n_batch; + n_ubatch = inst.n_ubatch; + n_threads = inst.n_threads; + cpu_mask = inst.cpu_mask; + cpu_strict = inst.cpu_strict; + poll = inst.poll; + type_k = inst.type_k; + type_v = inst.type_v; + n_gpu_layers = inst.n_gpu_layers; + split_mode = inst.split_mode; + main_gpu = inst.main_gpu; + no_kv_offload = inst.no_kv_offload; + flash_attn = inst.flash_attn; + tensor_split = inst.tensor_split; + use_mmap = inst.use_mmap; + embeddings = inst.embeddings; + n_prompt = inst.n_prompt; + n_gen = inst.n_gen; // RFC 3339 date-time format - time_t t = time(NULL); + time_t t = time(NULL); std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t)); test_time = buf; (void) ctx; } - uint64_t avg_ns() const { - return ::avg(samples_ns); - } + uint64_t avg_ns() const { return ::avg(samples_ns); } - uint64_t stdev_ns() const { - return ::stdev(samples_ns); - } + uint64_t stdev_ns() const { return ::stdev(samples_ns); } std::vector get_ts() const { - int n_tokens = n_prompt + n_gen; + int n_tokens = n_prompt + n_gen; std::vector ts; - std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; }); + std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), + [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; }); return ts; } - double avg_ts() const { - return ::avg(get_ts()); - } + double avg_ts() const { return ::avg(get_ts()); } - double stdev_ts() const { - return ::stdev(get_ts()); - } + double stdev_ts() const { return ::stdev(get_ts()); } static std::string get_backend() { - if (cuda) { - return GGML_CUDA_NAME; + std::vector backends; + for (size_t i = 0; i < ggml_backend_reg_count(); i++) { + auto * reg = ggml_backend_reg_get(i); + std::string name = ggml_backend_reg_name(reg); + if (name != "CPU") { + backends.push_back(ggml_backend_reg_name(reg)); + } } - if (vulkan) { - return "Vulkan"; - } - if (kompute) { - return "Kompute"; - } - if (metal) { - return "Metal"; - } - if (sycl) { - return GGML_SYCL_NAME; - } - if (gpu_blas) { - return "GPU BLAS"; - } - if (blas) { - return "BLAS"; - } - - return "CPU"; + return backends.empty() ? "CPU" : join(backends, ","); } static const std::vector & get_fields() { static const std::vector fields = { - "build_commit", "build_number", - "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas", - "cpu_info", "gpu_info", - "model_filename", "model_type", "model_size", "model_n_params", - "n_batch", "n_ubatch", - "n_threads", "cpu_mask", "cpu_strict", "poll", - "type_k", "type_v", - "n_gpu_layers", "split_mode", - "main_gpu", "no_kv_offload", "flash_attn", - "tensor_split", "use_mmap", "embeddings", - "n_prompt", "n_gen", "test_time", - "avg_ns", "stddev_ns", - "avg_ts", "stddev_ts", + "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", + "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", + "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", + "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", + "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", + "avg_ts", "stddev_ts", }; return fields; } - enum field_type {STRING, BOOL, INT, FLOAT}; + enum field_type { STRING, BOOL, INT, FLOAT }; static field_type get_field_type(const std::string & field) { - if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || - field == "n_threads" || field == "poll" || - field == "model_size" || field == "model_n_params" || - field == "n_gpu_layers" || field == "main_gpu" || - field == "n_prompt" || field == "n_gen" || - field == "avg_ns" || field == "stddev_ns") { + if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" || + field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || + field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "avg_ns" || + field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "cpu_strict" || - field == "flash_attn" || field == "use_mmap" || field == "embeddings") { + if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" || + field == "use_mmap" || field == "embeddings") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -1007,7 +968,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; - int max_nonzero = 0; + int max_nonzero = 0; for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { max_nonzero = i; @@ -1021,44 +982,53 @@ struct test { tensor_split_str += "/"; } } - std::vector values = { - build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(sycl), std::to_string(has_rpc), std::to_string(gpu_blas), std::to_string(blas), - cpu_info, gpu_info, - model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), - std::to_string(n_batch), std::to_string(n_ubatch), - std::to_string(n_threads), cpu_mask, std::to_string(cpu_strict), std::to_string(poll), - ggml_type_name(type_k), ggml_type_name(type_v), - std::to_string(n_gpu_layers), split_mode_str(split_mode), - std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn), - tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), - std::to_string(n_prompt), std::to_string(n_gen), test_time, - std::to_string(avg_ns()), std::to_string(stdev_ns()), - std::to_string(avg_ts()), std::to_string(stdev_ts()) - }; + std::vector values = { build_commit, + std::to_string(build_number), + cpu_info, + gpu_info, + get_backend(), + model_filename, + model_type, + std::to_string(model_size), + std::to_string(model_n_params), + std::to_string(n_batch), + std::to_string(n_ubatch), + std::to_string(n_threads), + cpu_mask, + std::to_string(cpu_strict), + std::to_string(poll), + ggml_type_name(type_k), + ggml_type_name(type_v), + std::to_string(n_gpu_layers), + split_mode_str(split_mode), + std::to_string(main_gpu), + std::to_string(no_kv_offload), + std::to_string(flash_attn), + tensor_split_str, + std::to_string(use_mmap), + std::to_string(embeddings), + std::to_string(n_prompt), + std::to_string(n_gen), + test_time, + std::to_string(avg_ns()), + std::to_string(stdev_ns()), + std::to_string(avg_ts()), + std::to_string(stdev_ts()) }; return values; } std::map get_map() const { std::map map; - auto fields = get_fields(); - auto values = get_values(); - std::transform(fields.begin(), fields.end(), values.begin(), - std::inserter(map, map.end()), std::make_pair); + auto fields = get_fields(); + auto values = get_values(); + std::transform(fields.begin(), fields.end(), values.begin(), std::inserter(map, map.end()), + std::make_pair); return map; } }; const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; -const bool test::cuda = !!ggml_cpu_has_cuda(); -const bool test::vulkan = !!ggml_cpu_has_vulkan(); -const bool test::kompute = !!ggml_cpu_has_kompute(); -const bool test::metal = !!ggml_cpu_has_metal(); -const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); -const bool test::blas = !!ggml_cpu_has_blas(); -const bool test::sycl = !!ggml_cpu_has_sycl(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -1066,9 +1036,12 @@ struct printer { virtual ~printer() {} FILE * fout; + virtual void print_header(const cmd_params & params) { (void) params; } + virtual void print_test(const test & t) = 0; - virtual void print_footer() { } + + virtual void print_footer() {} }; struct csv_printer : public printer { @@ -1084,7 +1057,7 @@ struct csv_printer : public printer { return escaped; } - void print_header(const cmd_params & params) override { + void print_header(const cmd_params & params) override { std::vector fields = test::get_fields(); fprintf(fout, "%s\n", join(fields, ",").c_str()); (void) params; @@ -1097,7 +1070,6 @@ struct csv_printer : public printer { } }; - static std::string escape_json(const std::string & value) { std::string escaped; for (auto c : value) { @@ -1105,7 +1077,7 @@ static std::string escape_json(const std::string & value) { escaped += "\\\""; } else if (c == '\\') { escaped += "\\\\"; - } else if (c <= 0x1f) { + } else if (c <= 0x1f) { char buf[8]; snprintf(buf, sizeof(buf), "\\u%04x", c); escaped += buf; @@ -1138,7 +1110,8 @@ struct json_printer : public printer { void print_fields(const std::vector & fields, const std::vector & values) { assert(fields.size() == values.size()); for (size_t i = 0; i < fields.size(); i++) { - fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str()); + fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), + format_json_value(fields.at(i), values.at(i)).c_str()); } } @@ -1156,12 +1129,9 @@ struct json_printer : public printer { fflush(fout); } - void print_footer() override { - fprintf(fout, "\n]\n"); - } + void print_footer() override { fprintf(fout, "\n]\n"); } }; - struct jsonl_printer : public printer { void print_fields(const std::vector & fields, const std::vector & values) { assert(fields.size() == values.size()); @@ -1221,7 +1191,7 @@ struct markdown_printer : public printer { return 13; } - int width = std::max((int)field.length(), 10); + int width = std::max((int) field.length(), 10); if (test::get_field_type(field) == test::STRING) { return -width; @@ -1263,7 +1233,8 @@ struct markdown_printer : public printer { fields.emplace_back("size"); fields.emplace_back("params"); fields.emplace_back("backend"); - bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; + bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos || + test::get_backend().find("BLAS") != std::string::npos; if (!is_cpu_backend) { fields.emplace_back("n_gpu_layers"); } @@ -1334,18 +1305,18 @@ struct markdown_printer : public printer { fprintf(fout, "|"); for (const auto & field : fields) { std::string value; - char buf[128]; + char buf[128]; if (field == "model") { value = t.model_type; } else if (field == "size") { - if (t.model_size < 1024*1024*1024) { + if (t.model_size < 1024 * 1024 * 1024) { snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0); } else { snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0); } value = buf; } else if (field == "params") { - if (t.model_n_params < 1000*1000*1000) { + if (t.model_n_params < 1000 * 1000 * 1000) { snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6); } else { snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9); @@ -1353,9 +1324,6 @@ struct markdown_printer : public printer { value = buf; } else if (field == "backend") { value = test::get_backend(); - if (t.has_rpc) { - value += "+RPC"; - } } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); @@ -1410,7 +1378,8 @@ struct sql_printer : public printer { std::vector fields = test::get_fields(); fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n"); for (size_t i = 0; i < fields.size(); i++) { - fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), i < fields.size() - 1 ? "," : ""); + fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), + i < fields.size() - 1 ? "," : ""); } fprintf(fout, ");\n"); fprintf(fout, "\n"); @@ -1428,11 +1397,11 @@ struct sql_printer : public printer { } }; -static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { +static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); std::vector tokens(n_batch); @@ -1440,27 +1409,27 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); - tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; + tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 1; i < n_tokens; i++) { tokens[i] = std::rand() % n_vocab; } - llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); + llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens)); n_processed += n_tokens; } llama_synchronize(ctx); } -static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { +static void test_gen(llama_context * ctx, int n_gen, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 0; i < n_gen; i++) { - llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); + llama_decode(ctx, llama_batch_get_one(&token, 1)); llama_synchronize(ctx); token = std::rand() % n_vocab; } @@ -1518,7 +1487,7 @@ int main(int argc, char ** argv) { set_process_priority(params.prio); // initialize printer - std::unique_ptr p = create_printer(params.output_format); + std::unique_ptr p = create_printer(params.output_format); std::unique_ptr p_err = create_printer(params.output_format_stderr); if (p) { @@ -1533,13 +1502,13 @@ int main(int argc, char ** argv) { std::vector params_instances = get_cmd_params_instances(params); - llama_model * lmodel = nullptr; + llama_model * lmodel = nullptr; const cmd_params_instance * prev_inst = nullptr; - int params_idx = 0; + int params_idx = 0; auto params_count = params_instances.size(); for (const auto & inst : params_instances) { - params_idx ++; + params_idx++; if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count); } @@ -1582,7 +1551,7 @@ int main(int argc, char ** argv) { tpp.poll = t.poll; tpp.prio = params.prio; - struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); + struct ggml_threadpool * threadpool = ggml_threadpool_new(&tpp); if (!threadpool) { fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads); exit(1); @@ -1596,13 +1565,13 @@ int main(int argc, char ** argv) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count); } //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count); } - test_gen(ctx, 1, 0, t.n_threads); + test_gen(ctx, 1, t.n_threads); } for (int i = 0; i < params.reps; i++) { @@ -1612,15 +1581,17 @@ int main(int argc, char ** argv) { if (t.n_prompt > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps); + fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, + i + 1, params.reps); } - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps); + fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, + i + 1, params.reps); } - test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads); + test_gen(ctx, t.n_gen, t.n_threads); } uint64_t t_ns = get_time_ns() - t_start; diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp index f5ffd063f8..b3858ddfb6 100644 --- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -283,9 +283,6 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, nullptr, nullptr, nullptr, - 0, - 0, - 0, }; if (embd) { diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index dcd9803a2a..65cd4eb515 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -46,7 +46,6 @@ actor LlamaContext { let sparams = llama_sampler_chain_default_params() self.sampling = llama_sampler_chain_init(sparams) llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4)) - llama_sampler_chain_add(self.sampling, llama_sampler_init_softmax()) llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234)) } diff --git a/examples/llama.vim b/examples/llama.vim new file mode 100644 index 0000000000..57eb2a9772 --- /dev/null +++ b/examples/llama.vim @@ -0,0 +1,783 @@ +" LLM-based text completion using llama.cpp +" +" requires: +" +" - neovim or vim +" - curl +" - llama.cpp server instance +" - FIM-compatible model +" +" sample config: +" +" - Tab - accept the current suggestion +" - Shift+Tab - accept just the first line of the suggestion +" - Ctrl+F - toggle FIM completion manually +" +" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim +" +" start the llama.cpp server with a FIM-compatible model. for example: +" +" $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -dt 0.1 --ubatch-size 512 --batch-size 1024 --cache-reuse 256 +" +" --batch-size [512, model max context] +" +" adjust the batch size to control how much of the provided local context will be used during the inference +" lower values will use smaller part of the context around the cursor, which will result in faster processing +" +" --ubatch-size [64, 2048] +" +" chunks the batch into smaller chunks for faster processing +" depends on the specific hardware. use llama-bench to profile and determine the best size +" +" --cache-reuse (ge:llama_config.n_predict, 1024] +" +" this should be either 0 (disabled) or strictly larger than g:llama_config.n_predict +" using non-zero value enables context reuse on the server side which dramatically improves the performance at +" large contexts. a value of 256 should be good for all cases +" +" run this once to initialise llama.vim: +" +" :call llama#init() +" +" more info: https://github.com/ggerganov/llama.cpp/pull/9787 +" + +" colors (adjust to your liking) +highlight llama_hl_hint guifg=#ff772f ctermfg=202 +highlight llama_hl_info guifg=#77ff2f ctermfg=119 + +" general parameters: +" +" endpoint: llama.cpp server endpoint +" n_prefix: number of lines before the cursor location to include in the local prefix +" n_suffix: number of lines after the cursor location to include in the local suffix +" n_predict: max number of tokens to predict +" t_max_prompt_ms: max alloted time for the prompt processing (TODO: not yet supported) +" t_max_predict_ms: max alloted time for the prediction +" show_info: show extra info about the inference (0 - disabled, 1 - statusline, 2 - inline) +" auto_fim: trigger FIM completion automatically on cursor movement +" max_line_suffix: do not auto-trigger FIM completion if there are more than this number of characters to the right of the cursor +" +" ring buffer of chunks, accumulated with time upon: +" +" - completion request +" - yank +" - entering a buffer +" - leaving a buffer +" - writing a file +" +" parameters for the ring-buffer with extra context: +" +" ring_n_chunks: max number of chunks to pass as extra context to the server (0 to disable) +" ring_chunk_size: max size of the chunks (in number of lines) +" note: adjust these numbers so that you don't overrun your context +" at ring_n_chunks = 64 and ring_chunk_size = 64 you need ~32k context +" ring_scope: the range around the cursor position (in number of lines) for gathering chunks after FIM +" ring_update_ms: how often to process queued chunks in normal mode +" +let s:default_config = { + \ 'endpoint': 'http://127.0.0.1:8012/infill', + \ 'n_prefix': 256, + \ 'n_suffix': 64, + \ 'n_predict': 128, + \ 't_max_prompt_ms': 500, + \ 't_max_predict_ms': 3000, + \ 'show_info': 2, + \ 'auto_fim': v:true, + \ 'max_line_suffix': 8, + \ 'ring_n_chunks': 64, + \ 'ring_chunk_size': 64, + \ 'ring_scope': 1024, + \ 'ring_update_ms': 1000, + \ } + +let g:llama_config = get(g:, 'llama_config', s:default_config) + +function! s:get_indent(str) + let l:count = 0 + for i in range(len(a:str)) + if a:str[i] == "\t" + let l:count += &tabstop - 1 + else + break + endif + endfor + return l:count +endfunction + +function! s:rand(i0, i1) abort + return a:i0 + rand() % (a:i1 - a:i0 + 1) +endfunction + +function! llama#init() + if !executable('curl') + echohl WarningMsg + echo 'llama.vim requires the "curl" command to be available' + echohl None + return + endif + + let s:pos_x = 0 " cursor position upon start of completion + let s:pos_y = 0 + + let s:line_cur = '' + + let s:line_cur_prefix = '' + let s:line_cur_suffix = '' + + let s:ring_chunks = [] " current set of chunks used as extra context + let s:ring_queued = [] " chunks that are queued to be sent for processing + let s:ring_n_evict = 0 + + let s:hint_shown = v:false + let s:pos_y_pick = -9999 " last y where we picked a chunk + let s:pos_dx = 0 + let s:content = [] + let s:can_accept = v:false + + let s:timer_fim = -1 + let s:t_fim_start = reltime() " used to measure total FIM time + let s:t_last_move = reltime() " last time the cursor moved + + let s:current_job = v:null + + let s:ghost_text_nvim = exists('*nvim_buf_get_mark') + let s:ghost_text_vim = has('textprop') + + if s:ghost_text_vim + let s:hlgroup_hint = 'llama_hl_hint' + let s:hlgroup_info = 'llama_hl_info' + + if empty(prop_type_get(s:hlgroup_hint)) + call prop_type_add(s:hlgroup_hint, {'highlight': s:hlgroup_hint}) + endif + if empty(prop_type_get(s:hlgroup_info)) + call prop_type_add(s:hlgroup_info, {'highlight': s:hlgroup_info}) + endif + endif + + augroup llama + autocmd! + autocmd InsertEnter * inoremap llama#fim_inline(v:false) + autocmd InsertLeavePre * call llama#fim_cancel() + + autocmd CursorMoved * call s:on_move() + autocmd CursorMovedI * call s:on_move() + autocmd CompleteChanged * call llama#fim_cancel() + + if g:llama_config.auto_fim + autocmd CursorMovedI * call llama#fim(v:true) + endif + + " gather chunks upon yanking + autocmd TextYankPost * if v:event.operator ==# 'y' | call s:pick_chunk(v:event.regcontents, v:false, v:true) | endif + + " gather chunks upon entering/leaving a buffer + autocmd BufEnter * call timer_start(100, {-> s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)}) + autocmd BufLeave * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + + " gather chunk upon saving the file + autocmd BufWritePost * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + augroup END + + silent! call llama#fim_cancel() + + " init background update of the ring buffer + if g:llama_config.ring_n_chunks > 0 + call s:ring_update() + endif +endfunction + +" compute how similar two chunks of text are +" 0 - no similarity, 1 - high similarity +" TODO: figure out something better +function! s:chunk_sim(c0, c1) + let l:lines0 = len(a:c0) + let l:lines1 = len(a:c1) + + let l:common = 0 + + for l:line0 in a:c0 + for l:line1 in a:c1 + if l:line0 == l:line1 + let l:common += 1 + break + endif + endfor + endfor + + return 2.0 * l:common / (l:lines0 + l:lines1) +endfunction + +" pick a random chunk of size g:llama_config.ring_chunk_size from the provided text and queue it for processing +" +" no_mod - do not pick chunks from buffers with pending changes +" do_evict - evict chunks that are very similar to the new one +" +function! s:pick_chunk(text, no_mod, do_evict) + " do not pick chunks from buffers with pending changes or buffers that are not files + if a:no_mod && (getbufvar(bufnr('%'), '&modified') || !buflisted(bufnr('%')) || !filereadable(expand('%'))) + return + endif + + " if the extra context option is disabled - do nothing + if g:llama_config.ring_n_chunks <= 0 + return + endif + + " don't pick very small chunks + if len(a:text) < 3 + return + endif + + if len(a:text) + 1 < g:llama_config.ring_chunk_size + let l:chunk = a:text + else + let l:l0 = s:rand(0, max([0, len(a:text) - g:llama_config.ring_chunk_size/2])) + let l:l1 = min([l:l0 + g:llama_config.ring_chunk_size/2, len(a:text)]) + + let l:chunk = a:text[l:l0:l:l1] + endif + + let l:chunk_str = join(l:chunk, "\n") . "\n" + + " check if this chunk is already added + let l:exist = v:false + + for i in range(len(s:ring_chunks)) + if s:ring_chunks[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + for i in range(len(s:ring_queued)) + if s:ring_queued[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + if l:exist + return + endif + + " evict queued chunks that are very similar to the new one + for i in range(len(s:ring_queued) - 1, 0, -1) + if s:chunk_sim(s:ring_queued[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_queued, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " also from s:ring_chunks + for i in range(len(s:ring_chunks) - 1, 0, -1) + if s:chunk_sim(s:ring_chunks[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_chunks, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " TODO: become parameter ? + if len(s:ring_queued) == 16 + call remove(s:ring_queued, 0) + endif + + call add(s:ring_queued, {'data': l:chunk, 'str': l:chunk_str, 'time': reltime(), 'filename': expand('%')}) + + "let &statusline = 'extra context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) +endfunction + +" picks a queued chunk, sends it for processing and adds it to s:ring_chunks +" called every g:llama_config.ring_update_ms +function! s:ring_update() + call timer_start(g:llama_config.ring_update_ms, {-> s:ring_update()}) + + " update only if in normal mode or if the cursor hasn't moved for a while + if mode() !=# 'n' && reltimefloat(reltime(s:t_last_move)) < 3.0 + return + endif + + if len(s:ring_queued) == 0 + return + endif + + " move the first queued chunk to the ring buffer + if len(s:ring_chunks) == g:llama_config.ring_n_chunks + call remove(s:ring_chunks, 0) + endif + + call add(s:ring_chunks, remove(s:ring_queued, 0)) + + "let &statusline = 'updated context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) + + " send asynchronous job with the new extra context so that it is ready for the next FIM + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " no samplers needed here + let l:request = json_encode({ + \ 'input_prefix': "", + \ 'input_suffix': "", + \ 'input_extra': l:extra_context, + \ 'prompt': "", + \ 'n_predict': 1, + \ 'temperature': 0.0, + \ 'stream': v:false, + \ 'samplers': ["temperature"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': 1, + \ 't_max_predict_ms': 1 + \ }) + + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] + + " no callbacks because we don't need to process the response + if s:ghost_text_nvim + call jobstart(l:curl_command, {}) + elseif s:ghost_text_vim + call job_start(l:curl_command, {}) + endif +endfunction + +" necessary for 'inoremap ' +function! llama#fim_inline(is_auto) abort + call llama#fim(a:is_auto) + return '' +endfunction + +" the main FIM call +" takes local context around the cursor and sends it together with the extra context to the server for completion +function! llama#fim(is_auto) abort + " we already have a suggestion for the current cursor position + if s:hint_shown && !a:is_auto + call llama#fim_cancel() + return + endif + + call llama#fim_cancel() + + " avoid sending repeated requests too fast + if reltimefloat(reltime(s:t_fim_start)) < 0.6 + if s:timer_fim != -1 + call timer_stop(s:timer_fim) + let s:timer_fim = -1 + endif + + let s:t_fim_start = reltime() + let s:timer_fim = timer_start(600, {-> llama#fim(v:true)}) + return + endif + + let s:t_fim_start = reltime() + + let s:content = [] + let s:can_accept = v:false + + let s:pos_x = col('.') - 1 + let s:pos_y = line('.') + let l:max_y = line('$') + + let l:lines_prefix = getline(max([1, s:pos_y - g:llama_config.n_prefix]), s:pos_y - 1) + let l:lines_suffix = getline(s:pos_y + 1, min([l:max_y, s:pos_y + g:llama_config.n_suffix])) + + let s:line_cur = getline('.') + + let s:line_cur_prefix = strpart(s:line_cur, 0, s:pos_x) + let s:line_cur_suffix = strpart(s:line_cur, s:pos_x) + + if a:is_auto && len(s:line_cur_suffix) > g:llama_config.max_line_suffix + return + endif + + let l:prefix = "" + \ . join(l:lines_prefix, "\n") + \ . "\n" + + let l:prompt = "" + \ . s:line_cur_prefix + + let l:suffix = "" + \ . s:line_cur_suffix + \ . "\n" + \ . join(l:lines_suffix, "\n") + \ . "\n" + + " prepare the extra context data + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " the indentation of the current line + let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + + let l:request = json_encode({ + \ 'input_prefix': l:prefix, + \ 'input_suffix': l:suffix, + \ 'input_extra': l:extra_context, + \ 'prompt': l:prompt, + \ 'n_predict': g:llama_config.n_predict, + \ 'n_indent': l:indent, + \ 'top_k': 40, + \ 'top_p': 0.99, + \ 'stream': v:false, + \ 'samplers': ["top_k", "top_p", "infill"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': g:llama_config.t_max_prompt_ms, + \ 't_max_predict_ms': g:llama_config.t_max_predict_ms + \ }) + + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] + + if s:current_job != v:null + if s:ghost_text_nvim + call jobstop(s:current_job) + elseif s:ghost_text_vim + call job_stop(s:current_job) + endif + endif + + " send the request asynchronously + if s:ghost_text_nvim + let s:current_job = jobstart(l:curl_command, { + \ 'on_stdout': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'on_exit': function('s:fim_on_exit'), + \ 'stdout_buffered': v:true + \ }) + elseif s:ghost_text_vim + let s:current_job = job_start(l:curl_command, { + \ 'out_cb': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'exit_cb': function('s:fim_on_exit') + \ }) + endif + + " TODO: per-file location + let l:delta_y = abs(s:pos_y - s:pos_y_pick) + + " gather some extra context nearby and process it in the background + " only gather chunks if the cursor has moved a lot + " TODO: something more clever? reranking? + if a:is_auto && l:delta_y > 32 + " expand the prefix even further + call s:pick_chunk(getline(max([1, s:pos_y - g:llama_config.ring_scope]), max([1, s:pos_y - g:llama_config.n_prefix])), v:false, v:false) + + " pick a suffix chunk + call s:pick_chunk(getline(min([l:max_y, s:pos_y + g:llama_config.n_suffix]), min([l:max_y, s:pos_y + g:llama_config.n_suffix + g:llama_config.ring_chunk_size])), v:false, v:false) + + let s:pos_y_pick = s:pos_y + endif +endfunction + +" if first_line == v:true accept only the first line of the response +function! llama#fim_accept(first_line) + " insert the suggestion at the cursor location + if s:can_accept && len(s:content) > 0 + call setline(s:pos_y, s:line_cur[:(s:pos_x - 1)] . s:content[0]) + if len(s:content) > 1 + if !a:first_line + call append(s:pos_y, s:content[1:-1]) + endif + endif + + " move the cursor to the end of the accepted text + if !a:first_line && len(s:content) > 1 + call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx + 1) + else + call cursor(s:pos_y, s:pos_x + len(s:content[0])) + endif + endif + + call llama#fim_cancel() +endfunction + +function! llama#fim_cancel() + let s:hint_shown = v:false + + " clear the virtual text + let l:bufnr = bufnr('%') + + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) + elseif s:ghost_text_vim + call prop_remove({'type': s:hlgroup_hint, 'all': v:true}) + call prop_remove({'type': s:hlgroup_info, 'all': v:true}) + endif + + " remove the mappings + silent! iunmap + silent! iunmap + silent! iunmap +endfunction + +function! s:on_move() + let s:t_last_move = reltime() + + call llama#fim_cancel() +endfunction + +" callback that processes the FIM result from the server and displays the suggestion +function! s:fim_on_stdout(pos_x, pos_y, is_auto, job_id, data, event = v:null) + if s:ghost_text_nvim + let l:raw = join(a:data, "\n") + elseif s:ghost_text_vim + let l:raw = a:data + endif + + if len(l:raw) == 0 + return + endif + + if a:pos_x != col('.') - 1 || a:pos_y != line('.') + return + endif + + " show the suggestion only in insert mode + if mode() !=# 'i' + return + endif + + let s:pos_x = a:pos_x + let s:pos_y = a:pos_y + + let s:can_accept = v:true + let l:has_info = v:false + + if s:can_accept && v:shell_error + if !a:is_auto + call add(s:content, "<| curl error: is the server on? |>") + endif + let s:can_accept = v:false + endif + + let l:n_prompt = 0 + let l:t_prompt_ms = 1.0 + let l:s_prompt = 0 + + let l:n_predict = 0 + let l:t_predict_ms = 1.0 + let l:s_predict = 0 + + " get the generated suggestion + if s:can_accept + let l:response = json_decode(l:raw) + + for l:part in split(get(l:response, 'content', ''), "\n", 1) + call add(s:content, l:part) + endfor + + " remove trailing new lines + while len(s:content) > 0 && s:content[-1] == "" + call remove(s:content, -1) + endwhile + + let l:generation_settings = get(l:response, 'generation_settings', {}) + let l:n_ctx = get(l:generation_settings, 'n_ctx', 0) + + let l:n_cached = get(l:response, 'tokens_cached', 0) + let l:truncated = get(l:response, 'truncated', v:false) + + " if response.timings is available + if len(get(l:response, 'timings', {})) > 0 + let l:has_info = v:true + let l:timings = get(l:response, 'timings', {}) + + let l:n_prompt = get(l:timings, 'prompt_n', 0) + let l:t_prompt_ms = get(l:timings, 'prompt_ms', 1) + let l:s_prompt = get(l:timings, 'prompt_per_second', 0) + + let l:n_predict = get(l:timings, 'predicted_n', 0) + let l:t_predict_ms = get(l:timings, 'predicted_ms', 1) + let l:s_predict = get(l:timings, 'predicted_per_second', 0) + endif + endif + + if len(s:content) == 0 + call add(s:content, "") + let s:can_accept = v:false + endif + + if len(s:content) == 0 + return + endif + + " NOTE: the following is logic for discarding predictions that repeat existing text + " the code is quite ugly and there is very likely a simpler and more canonical way to implement this + " + " still, I wonder if there is some better way that avoids having to do these special hacks? + " on one hand, the LLM 'sees' the contents of the file before we start editing, so it is normal that it would + " start generating whatever we have given it via the extra context. but on the other hand, it's not very + " helpful to re-generate the same code that is already there + + " truncate the suggestion if the first line is empty + if len(s:content) == 1 && s:content[0] == "" + let s:content = [""] + endif + + " ... and the next lines are repeated + if len(s:content) > 1 && s:content[0] == "" && s:content[1:] == getline(s:pos_y + 1, s:pos_y + len(s:content) - 1) + let s:content = [""] + endif + + " truncate the suggestion if it repeats the suffix + if len(s:content) == 1 && s:content[0] == s:line_cur_suffix + let s:content = [""] + endif + + " find the first non-empty line (strip whitespace) + let l:cmp_y = s:pos_y + 1 + while l:cmp_y < line('$') && getline(l:cmp_y) =~? '^\s*$' + let l:cmp_y += 1 + endwhile + + if (s:line_cur_prefix . s:content[0]) == getline(l:cmp_y) + " truncate the suggestion if it repeats the next line + if len(s:content) == 1 + let s:content = [""] + endif + + " ... or if the second line of the suggestion is the prefix of line l:cmp_y + 1 + if len(s:content) == 2 && s:content[-1] == getline(l:cmp_y + 1)[:len(s:content[-1]) - 1] + let s:content = [""] + endif + + " ... or if the middle chunk of lines of the suggestion is the same as [l:cmp_y + 1, l:cmp_y + len(s:content) - 1) + if len(s:content) > 2 && join(s:content[1:-1], "\n") == join(getline(l:cmp_y + 1, l:cmp_y + len(s:content) - 1), "\n") + let s:content = [""] + endif + endif + + " keep only lines that have the same or larger whitespace prefix as s:line_cur_prefix + "let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + "for i in range(1, len(s:content) - 1) + " if strlen(matchstr(s:content[i], '^\s*')) < l:indent + " let s:content = s:content[:i - 1] + " break + " endif + "endfor + + let s:pos_dx = len(s:content[-1]) + + let s:content[-1] .= s:line_cur_suffix + + call llama#fim_cancel() + + " display virtual text with the suggestion + let l:bufnr = bufnr('%') + + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + endif + + " construct the info message + if g:llama_config.show_info > 0 && l:has_info + let l:prefix = ' ' + + if l:truncated + let l:info = printf("%s | WARNING: the context is full: %d / %d, increase the server context size or reduce g:llama_config.ring_n_chunks", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx + \ ) + else + let l:info = printf("%s | c: %d / %d, r: %d / %d, e: %d, q: %d / 16 | p: %d (%.2f ms, %.2f t/s) | g: %d (%.2f ms, %.2f t/s) | t: %.2f ms", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx, len(s:ring_chunks), g:llama_config.ring_n_chunks, s:ring_n_evict, len(s:ring_queued), + \ l:n_prompt, l:t_prompt_ms, l:s_prompt, + \ l:n_predict, l:t_predict_ms, l:s_predict, + \ 1000.0 * reltimefloat(reltime(s:t_fim_start)) + \ ) + endif + + if g:llama_config.show_info == 1 + " display the info in the statusline + let &statusline = l:info + let l:info = '' + endif + endif + + " display the suggestion and append the info to the end of the first line + if s:ghost_text_nvim + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { + \ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']], + \ 'virt_text_win_col': virtcol('.') - 1 + \ }) + + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, { + \ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}), + \ 'virt_text_win_col': virtcol('.') + \ }) + elseif s:ghost_text_vim + let l:new_suffix = s:content[0] + if !empty(l:new_suffix) + call prop_add(s:pos_y, s:pos_x + 1, { + \ 'type': s:hlgroup_hint, + \ 'text': l:new_suffix + \ }) + endif + for line in s:content[1:] + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_hint, + \ 'text': line, + \ 'text_padding_left': s:get_indent(line), + \ 'text_align': 'below' + \ }) + endfor + if !empty(l:info) + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_info, + \ 'text': l:info, + \ 'text_padding_left': col('$'), + \ 'text_wrap': 'truncate' + \ }) + endif + endif + + " setup accept shortcuts + inoremap :call llama#fim_accept(v:false) + inoremap :call llama#fim_accept(v:true) + + let s:hint_shown = v:true +endfunction + +function! s:fim_on_exit(job_id, exit_code, event = v:null) + if a:exit_code != 0 + echom "Job failed with exit code: " . a:exit_code + endif + + let s:current_job = v:null +endfunction diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 14e02c8ddc..aae49c965e 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -4,6 +4,7 @@ // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-alloc.h" #include "ggml-backend.h" diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 5f9abe2b62..1610985858 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -20,7 +20,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 8558c6bdca..be69885408 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -401,6 +401,39 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co return true; } +struct llava_embd_batch { + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_0; + std::vector seq_ids; + std::vector logits; + llama_batch batch; + llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { + pos .resize(n_tokens); + n_seq_id.resize(n_tokens); + seq_ids .resize(n_tokens + 1); + logits .resize(n_tokens); + seq_id_0.resize(1); + seq_id_0[0] = seq_id; + seq_ids [n_tokens] = nullptr; + batch = { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ embd, + /*pos =*/ pos.data(), + /*n_seq_id =*/ n_seq_id.data(), + /*seq_id =*/ seq_ids.data(), + /*logits =*/ logits.data(), + }; + for (int i = 0; i < n_tokens; i++) { + batch.pos [i] = pos_0 + i; + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } +}; + bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { int n_embd = llama_n_embd(llama_get_model(ctx_llama)); @@ -409,8 +442,9 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { + float * embd = image_embed->embed+i*n_embd; + llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0); + if (llama_decode(ctx_llama, llava_batch.batch)) { LOG_ERR("%s : failed to eval\n", __func__); return false; } @@ -432,7 +466,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); if (!image_embed_result) { clip_image_u8_free(img); - LOG_ERR("%s: coulnd't embed the image\n", __func__); + LOG_ERR("%s: couldn't embed the image\n", __func__); return NULL; } diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index 6b666de1b7..cbecec343c 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -97,7 +97,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 03cd63f3fe..2eef0867aa 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); for (int s = 1; s < W + G + 1; ++s) { llama_kv_cache_seq_cp(ctx, 0, s, -1, -1); diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index e2c8c3828f..8e993c7ed0 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv){ const auto t_enc_start = ggml_time_us(); - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); const auto t_enc_end = ggml_time_us(); diff --git a/examples/main/README.md b/examples/main/README.md index f0c3031ab1..145216938f 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -187,6 +187,30 @@ Use the `--no-penalize-nl` option to disable newline penalization when applying Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl` +### DRY Repetition Penalty + +DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)). + +- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled). +- `--dry-base N`: Set the DRY sampling base value (default: 1.75). +- `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2). +- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size). +- `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used. + +The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8. + +The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions. + +The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words. + +The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens. + +The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied. + +DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence. + +Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"` + ### Top-K Sampling - `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40). @@ -211,14 +235,6 @@ The Min-P sampling method was designed as an alternative to Top-P, and aims to e Example usage: `--min-p 0.05` -### Tail-Free Sampling (TFS) - -- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). - -Tail-free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks at how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens and thus disables the effect of TFS. - -Example usage: `--tfs 0.95` - ### Locally Typical Sampling - `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled). @@ -241,6 +257,19 @@ The `--mirostat-ent` option sets the Mirostat target entropy (tau), which repres Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0` +### XTC Sampling + +- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0). +- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1). + +Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one. + +By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models. + +Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`. + +Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1` + ### Logit Bias - `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion. @@ -284,10 +313,6 @@ These options help improve the performance and memory usage of the LLaMA models. These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. -### Memory Float 32 - -- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended. - ### Batch Size - `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations. @@ -308,6 +333,15 @@ These options help improve the performance and memory usage of the LLaMA models. For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). +## LoRA (Low-Rank Adaptation) adapters + +- `--lora FNAME`: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with `--lora-scaled` and can be repeated to use multiple adapters. +- `--lora-scaled FNAME`: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with `--lora` and can repeated to use multiple adapters. + +You can add LoRA adapters using `--lora` or `--lora-scaled`. For example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` or `--lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5`. + +LoRA adapters should be in GGUF format. To convert from Hugging Face format use the `convert-lora-to-gguf.py` script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old `--lora-base` flag has been removed now that merging is no longer performed. + ## Additional Options These options provide extra functionality and customization when running the LLaMA models: @@ -316,6 +350,4 @@ These options provide extra functionality and customization when running the LLa - `--verbose-prompt`: Print the prompt before generating text. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. -- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. - `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index fb10c20c5e..7c4ce4be2a 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -62,49 +62,6 @@ static bool file_is_empty(const std::string & path) { return f.tellg() == 0; } -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const std::vector & input_tokens, const std::string & output, - const std::vector & output_tokens -) { - if (params.logdir.empty()) { - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_ERR("%s: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: main\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Generation Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_string_multiline(logfile, "output", output.c_str()); - yaml_dump_vector_int(logfile, "output_tokens", output_tokens); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { @@ -115,7 +72,6 @@ static void sigint_handler(int signo) { console::cleanup(); LOG("\n"); common_perf_print(*g_ctx, *g_smpl); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); // make sure all logs are flushed LOG("Interrupted by user\n"); @@ -528,7 +484,7 @@ int main(int argc, char ** argv) { int enc_input_size = embd_inp.size(); llama_token * enc_input_buf = embd_inp.data(); - if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) { + if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } @@ -569,30 +525,30 @@ int main(int argc, char ** argv) { if (!params.ctx_shift){ LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__); break; - } else { - if (params.n_predict == -2) { - LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); - break; - } - - const int n_left = n_past - params.n_keep; - const int n_discard = n_left/2; - - LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", - n_past, n_left, n_ctx, params.n_keep, n_discard); - - llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); - llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); - - n_past -= n_discard; - - LOG_DBG("after swap: n_past = %d\n", n_past); - - LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); - - LOG_DBG("clear session path\n"); - path_session.clear(); } + + if (params.n_predict == -2) { + LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + break; + } + + const int n_left = n_past - params.n_keep; + const int n_discard = n_left/2; + + LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + n_past, n_left, n_ctx, params.n_keep, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); + + n_past -= n_discard; + + LOG_DBG("after swap: n_past = %d\n", n_past); + + LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); + + LOG_DBG("clear session path\n"); + path_session.clear(); } } else { // context extension via Self-Extend @@ -648,7 +604,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } @@ -926,7 +882,6 @@ int main(int argc, char ** argv) { LOG("\n\n"); common_perf_print(ctx, smpl); - write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); common_sampler_free(smpl); diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 20274c1479..43c8f3ed56 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -308,7 +308,6 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index efb41b80a3..64a84607c2 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -34,55 +34,6 @@ struct results_log_softmax { float prob; }; -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const struct results_perplexity & results -) { - if (params.logdir.empty()) { - return; - } - - if (params.hellaswag) { - LOG_WRN("%s: logging results is not implemented for HellaSwag. No files will be written.\n", __func__); - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_WRN("%s: failed to create logdir %s, cannot write logfile\n", - __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: main\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Perplexity Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_vector_float(logfile, "logits", results.logits); - fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - yaml_dump_vector_float(logfile, "probs", results.probs); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - static std::vector softmax(const std::vector& logits) { std::vector probs(logits.size()); float max_logit = logits[0]; @@ -408,14 +359,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + //LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + if (llama_decode(ctx, batch)) { //LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return {tokens, -1, logit_history, prob_history}; } @@ -435,6 +393,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { @@ -704,7 +664,6 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector< batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); @@ -1791,6 +1750,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -1803,9 +1764,14 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return; } @@ -1818,6 +1784,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { @@ -2055,8 +2023,6 @@ int main(int argc, char ** argv) { LOG("\n"); llama_perf_context_print(ctx); - write_logfile(ctx, params, model, results); - llama_free(ctx); llama_free_model(model); diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index e372856c6a..912caf346e 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -142,7 +142,7 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) { } static void test_roundtrip_on_chunk( - const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, bool use_reference, + const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference, float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats ) { if (layer->type == GGML_TYPE_F16) { @@ -156,7 +156,7 @@ static void test_roundtrip_on_chunk( if (use_reference) { qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size); } else { - qfns.from_float(input_scratch, quantized_scratch, chunk_size); + qfns_cpu.from_float(input_scratch, quantized_scratch, chunk_size); } qfns.to_float(quantized_scratch, output_scratch, chunk_size); @@ -166,7 +166,7 @@ static void test_roundtrip_on_chunk( // Run quantization function for a single layer and update error stats static void test_roundtrip_on_layer( - std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, bool use_reference, + std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference, const ggml_tensor * layer, std::vector & input_scratch, std::vector & quantized_scratch, std::vector & output_scratch, error_stats & total_error, int max_thread = 0 ) { @@ -187,13 +187,13 @@ static void test_roundtrip_on_layer( int num_chunks = (nelements + chunk_size - 1)/chunk_size; if (num_chunks < 2 || max_thread < 2) { - test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(), + test_roundtrip_on_chunk(layer, 0, nelements, qfns, qfns_cpu, use_reference, input_scratch_ptr, quantized_scratch.data(), output_scratch.data(), print_layer_stats ? layer_error : total_error); } else { auto & stats = print_layer_stats ? layer_error : total_error; std::mutex mutex; uint64_t counter = 0; - auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr, + auto compute = [&mutex, &counter, &stats, &qfns, &qfns_cpu, nelements, layer, use_reference, input_scratch_ptr, &quantized_scratch, &output_scratch, chunk_size] () { error_stats local_stats {}; while (true) { @@ -205,7 +205,7 @@ static void test_roundtrip_on_layer( } lock.unlock(); uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset; - test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset, + test_roundtrip_on_chunk(layer, offset, chunk, qfns, qfns_cpu, use_reference, input_scratch_ptr + offset, quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats); } }; @@ -371,8 +371,9 @@ int main(int argc, char ** argv) { if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) { continue; } - const auto * qfns = ggml_get_type_traits(type); - if (qfns->from_float && qfns->to_float) { + const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); + if (qfns_cpu->from_float && qfns->to_float) { if (params.verbose) { printf("testing %s ...\n", ggml_type_name(type)); } @@ -393,7 +394,7 @@ int main(int argc, char ** argv) { test_roundtrip_on_layer( layer_name, params.per_layer_stats, - *qfns, + *qfns, *qfns_cpu, params.reference, kv_tensor.second, input_scratch, diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 8354e37e56..5fe70dac7f 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -1,3 +1,5 @@ +#include "ggml-cpu.h" + #ifdef GGML_USE_CUDA #include "ggml-cuda.h" #endif diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 3866cfa27e..8c49a52a66 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -42,15 +42,21 @@ int main(int argc, char ** argv) { llama_sampler * smpl = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed)); // tokenize prompt auto tokens = common_tokenize(ctx, params.prompt, true); + // prepare the batch + llama_batch batch = llama_batch_init(tokens.size(), 0, 1); + for (size_t i = 0; i < tokens.size(); i++) { + common_batch_add(batch, tokens[i], i, {0}, false); + } + batch.logits[batch.n_tokens - 1] = true; // generate next token + // evaluate prompt - llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); - n_past += tokens.size(); + llama_decode(ctx, batch); + n_past += batch.n_tokens; // save state (rng, logits, embedding and kv_cache) to file { @@ -77,8 +83,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result0 += next_token_str; - if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); return 1; @@ -96,7 +106,6 @@ int main(int argc, char ** argv) { llama_sampler * smpl2 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl2, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed)); printf("\nsecond run: %s", params.prompt.c_str()); @@ -133,8 +142,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result1 += next_token_str; - if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx2, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx2); llama_free_model(model); return 1; @@ -156,7 +169,6 @@ int main(int argc, char ** argv) { llama_sampler * smpl3 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl3, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed)); printf("\nsingle seq run: %s", params.prompt.c_str()); @@ -221,8 +233,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result2 += next_token_str; - if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {1}, true); + + if (llama_decode(ctx3, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); return 1; @@ -236,6 +252,7 @@ int main(int argc, char ** argv) { llama_sampler_free(smpl2); llama_sampler_free(smpl3); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 3e717e882b..93e876f5a5 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -15,22 +15,13 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS - colorthemes.css - style.css - theme-beeninorder.css - theme-ketivah.css - theme-mangotango.css - theme-playground.css - theme-polarnight.css - theme-snowstorm.css index.html - index-new.html - index.js completion.js - system-prompts.js - prompt-formats.js - json-schema-to-grammar.mjs loading.html + deps_daisyui.min.css + deps_markdown-it.js + deps_tailwindcss.js + deps_vue.esm-browser.js ) foreach(asset ${PUBLIC_ASSETS}) diff --git a/examples/server/README.md b/examples/server/README.md index b5feeb77bd..0936e0b7ba 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -39,7 +39,7 @@ The project is under active development, and we are [looking for feedback and co | `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) | | `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)
| | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | -| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | +| `-c, --ctx-size N` | size of the prompt context (default: 4096, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | | `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
(env: LLAMA_ARG_N_PREDICT) | | `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | @@ -64,7 +64,7 @@ The project is under active development, and we are [looking for feedback and co | `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | | `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | -| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | +| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | @@ -85,7 +85,6 @@ The project is under active development, and we are [looking for feedback and co | `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)
(env: LLAMA_ARG_HF_REPO) | | `-hff, --hf-file FILE` | Hugging Face model file (default: unused)
(env: LLAMA_ARG_HF_FILE) | | `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | -| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) | | `--log-disable` | Log disable | | `--log-file FNAME` | Log to file | | `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | @@ -99,24 +98,30 @@ The project is under active development, and we are [looking for feedback and co | Argument | Explanation | | -------- | ----------- | -| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: dry;top_k;typ_p;top_p;min_p;xtc;temperature) | | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | -| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) | +| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | | `--penalize-nl` | penalize newline tokens (default: false) | | `--temp N` | temperature (default: 0.8) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled) | | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | | `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | -| `--tfs N` | tail free sampling, parameter z (default: 1.0, 1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) | | `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | | `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | | `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--dry-base N` | set DRY sampling base value (default: 1.75) | +| `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | +| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers
| | `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | | `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | -| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | +| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | | `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | | `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | | `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | @@ -147,6 +152,7 @@ The project is under active development, and we are [looking for feedback and co | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE) | | `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | | `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | +| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--slots` | enable slots monitoring endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | @@ -318,6 +324,18 @@ node index.js - The prompt is a string or an array with the first element given as a string - The model's `tokenizer.ggml.add_bos_token` metadata is `true` + These input shapes and data type are allowed for `prompt`: + + - Single string: `"string"` + - Single sequence of tokens: `[12, 34, 56]` + - Mixed tokens and strings: `[12, 34, "string", 56, 78]` + + Multiple prompts are also supported. In this case, the completion result will be an array. + + - Only strings: `["string1", "string2"]` + - Strings and sequences of tokens: `["string1", [12, 34, 56]]` + - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` + `temperature`: Adjust the randomness of the generated text. Default: `0.8` `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. @@ -332,6 +350,8 @@ node index.js `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. + `n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` + `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. @@ -340,8 +360,6 @@ node index.js `stop`: Specify a JSON array of stopping strings. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` - `tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled. - `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` @@ -354,6 +372,20 @@ node index.js `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. + `dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. + + `dry_base`: Set the DRY repetition penalty base value. Default: `1.75` + + `dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` + + `dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. + + `dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` + + `xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled. + + `xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC) + `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` @@ -382,7 +414,7 @@ node index.js `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false` - `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values. + `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values. **Response format** @@ -523,8 +555,31 @@ Takes a prefix and a suffix and returns the predicted completion as stream. - `input_prefix`: Set the prefix of the code to infill. - `input_suffix`: Set the suffix of the code to infill. +- `input_extra`: Additional context inserted before the FIM prefix. +- `prompt`: Added after the `FIM_MID` token -It also accepts all the options of `/completion`. +`input_extra` is array of `{"filename": string, "text": string}` objects. + +The endpoint also accepts all the options of `/completion`. + +If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used: + +```txt +myproject +{chunk 0 filename} +{chunk 0 text} +{chunk 1 filename} +{chunk 1 text} +... +filename +[input_prefix][input_suffix][prompt] +``` + +If the tokens are missing, then the extra context is simply prefixed at the start: + +```txt +[input_extra][input_prefix][input_suffix][prompt] +``` ### **GET** `/props`: Get server global properties. @@ -642,7 +697,10 @@ Given a ChatML-formatted json description in `messages`, it returns the predicte ### GET `/slots`: Returns the current slots processing state -This endpoint can be disabled with `--no-slots` +> [!WARNING] +> This endpoint is intended for debugging and may be modified in future versions. For security reasons, we strongly advise against enabling it in production environments. + +This endpoint is disabled by default and can be enabled with `--slots` If query param `?fail_on_no_slot=1` is set, this endpoint will respond with status code 503 if there is no available slots. @@ -659,6 +717,7 @@ Example: "grammar": "", "id": 0, "ignore_eos": false, + "is_processing": false, "logit_bias": [], "min_p": 0.05000000074505806, "mirostat": 0, @@ -685,21 +744,18 @@ Example: "repeat_penalty": 1.100000023841858, "samplers": [ "top_k", - "tfs_z", "typical_p", "top_p", "min_p", "temperature" ], "seed": 42, - "state": 1, "stop": [ "\n" ], "stream": false, "task_id": 0, "temperature": 0.0, - "tfs_z": 1.0, "top_k": 40, "top_p": 0.949999988079071, "typical_p": 1.0 @@ -707,10 +763,6 @@ Example: ] ``` -Possible values for `slot[i].state` are: -- `0`: SLOT_STATE_IDLE -- `1`: SLOT_STATE_PROCESSING - ### GET `/metrics`: Prometheus compatible metrics exporter This endpoint is only accessible if `--metrics` is set. @@ -881,6 +933,16 @@ Apart from error types supported by OAI, we also have custom types that are spec } ``` +### Legacy completion web UI + +A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggerganov/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy` + +For example: + +```sh +./llama-server -m my_model.gguf -c 8192 --path ./examples/server/public_legacy +``` + ### Extending or building alternative Web Front End You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index a79c8a3cd0..4fef5655a8 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -1,7 +1,7 @@ import * as readline from 'node:readline' import { stdin, stdout } from 'node:process' import { readFileSync } from 'node:fs' -import { SchemaConverter } from './public/json-schema-to-grammar.mjs' +import { SchemaConverter } from './public_legacy/json-schema-to-grammar.mjs' const args = process.argv.slice(2); const grammarJsonSchemaFile = args.find( diff --git a/examples/server/deps.sh b/examples/server/deps.sh index d28378901a..1ff80d0569 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -6,5 +6,20 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" PUBLIC=$DIR/public echo "download js bundle files" -curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js -echo >> $PUBLIC/index.js # add newline + +# Note for contributors: Always pin to a specific version "maj.min.patch" to avoid breaking the CI + +curl -L https://cdn.tailwindcss.com/3.4.14 > $PUBLIC/deps_tailwindcss.js +echo >> $PUBLIC/deps_tailwindcss.js # add newline + +curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/styled.min.css > $PUBLIC/deps_daisyui.min.css +curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/themes.min.css >> $PUBLIC/deps_daisyui.min.css +echo >> $PUBLIC/deps_daisyui.min.css # add newline + +curl -L https://unpkg.com/vue@3.5.12/dist/vue.esm-browser.js > $PUBLIC/deps_vue.esm-browser.js +echo >> $PUBLIC/deps_vue.esm-browser.js # add newline + +curl -L https://cdnjs.cloudflare.com/ajax/libs/markdown-it/13.0.2/markdown-it.js > $PUBLIC/deps_markdown-it.js +echo >> $PUBLIC/deps_markdown-it.js # add newline + +ls -lah $PUBLIC diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index 36818f7644..54a0f22f58 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -1,12 +1,16 @@ const paramDefaults = { stream: true, - n_predict: 500, temperature: 0.2, - stop: [""] }; let generation_settings = null; +export class CompletionError extends Error { + constructor(message, name, data) { + super(message); + this.name = name; + } +}; // Completes the prompt as a generator. Recommended for most use cases. // @@ -29,7 +33,7 @@ export async function* llama(prompt, params = {}, config = {}) { const completionParams = { ...paramDefaults, ...params, prompt }; - const response = await fetch(`${api_url}/completion`, { + const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { method: 'POST', body: JSON.stringify(completionParams), headers: { @@ -41,6 +45,18 @@ export async function* llama(prompt, params = {}, config = {}) { signal: controller.signal, }); + const status = response.status; + if (status !== 200) { + try { + const body = await response.json(); + if (body && body.error && body.error.message) { + throw new CompletionError(body.error.message, 'ServerError'); + } + } catch (err) { + throw new CompletionError(err.message, 'ServerError'); + } + } + const reader = response.body.getReader(); const decoder = new TextDecoder(); @@ -78,7 +94,12 @@ export async function* llama(prompt, params = {}, config = {}) { for (const line of lines) { const match = regex.exec(line); if (match) { - result[match[1]] = match[2] + result[match[1]] = match[2]; + if (result.data === '[DONE]') { + cont = false; + break; + } + // since we know this is llama.cpp, let's just decode the json in data if (result.data) { result.data = JSON.parse(result.data); diff --git a/examples/server/public/deps_daisyui.min.css b/examples/server/public/deps_daisyui.min.css new file mode 100644 index 0000000000..bc85296519 --- /dev/null +++ b/examples/server/public/deps_daisyui.min.css @@ -0,0 +1,13 @@ +.alert{display:grid;width:100%;grid-auto-flow:row;align-content:flex-start;align-items:center;justify-items:center;gap:1rem;text-align:center}@media (min-width:640px){.alert{grid-auto-flow:column;grid-template-columns:auto minmax(auto,1fr);justify-items:start;text-align:start}}.artboard{width:100%}.avatar{position:relative;display:inline-flex}.avatar>div{display:block;aspect-ratio:1/1;overflow:hidden}.avatar 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pre[data-prefix]:before{content:attr(data-prefix);width:2rem;opacity:.5}.mockup-window{display:flex;flex-direction:column;border-radius:var(--rounded-box,1rem);padding-top:1.25rem}.mockup-window:before{content:"";margin-bottom:1rem;display:block;aspect-ratio:1/1;height:.75rem;flex-shrink:0;align-self:flex-start;border-radius:9999px;opacity:.3}.mockup-window:where([dir=rtl],[dir=rtl]*):before{align-self:flex-end}.mockup-window:before{box-shadow:1.4em 0,2.8em 0,4.2em 0}.mockup-phone{display:inline-block;border:4px solid #444;border-radius:50px;background-color:#000;padding:10px;margin:0 auto;overflow:hidden}.mockup-phone .camera{position:relative;top:0;left:0;background:#000;height:25px;width:150px;margin:0 auto;border-bottom-left-radius:17px;border-bottom-right-radius:17px;z-index:11}.mockup-phone .camera:before{content:"";position:absolute;top:35%;left:50%;width:50px;height:4px;border-radius:5px;background-color:#0c0b0e;transform:translate(-50%,-50%)}.mockup-phone .camera:after{content:"";position:absolute;top:20%;left:70%;width:8px;height:8px;border-radius:5px;background-color:#0f0b25}.mockup-phone .display{overflow:hidden;border-radius:40px;margin-top:-25px}.mockup-browser{border-radius:var(--rounded-box,1rem)}.mockup-browser .mockup-browser-toolbar{margin-top:.75rem;margin-bottom:.75rem;display:inline-flex;width:100%;align-items:center;padding-right:1.4em}.mockup-browser .mockup-browser-toolbar:where([dir=rtl],[dir=rtl]*){flex-direction:row-reverse}.mockup-browser .mockup-browser-toolbar:before{content:"";margin-right:4.8rem;display:inline-block;aspect-ratio:1/1;height:.75rem;border-radius:9999px;opacity:.3;box-shadow:1.4em 0,2.8em 0,4.2em 0}.mockup-browser .mockup-browser-toolbar .input{position:relative;margin-left:auto;margin-right:auto;display:block;height:1.75rem;width:24rem;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;--tw-bg-opacity:1;background-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-bg-opacity)));padding-left:2rem;direction:ltr}.mockup-browser .mockup-browser-toolbar .input:before{content:"";position:absolute;left:.5rem;top:50%;aspect-ratio:1/1;height:.75rem;--tw-translate-y:-50%;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-radius:9999px;border-width:2px;border-color:currentColor;opacity:.6}.mockup-browser .mockup-browser-toolbar .input:after{content:"";position:absolute;left:1.25rem;top:50%;height:.5rem;--tw-translate-y:25%;--tw-rotate:-45deg;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-radius:9999px;border-width:1px;border-color:currentColor;opacity:.6}.modal{background-color:transparent;color:inherit;transition-duration:.2s;transition-timing-function:cubic-bezier(0,0,.2,1);transition-property:transform,opacity,visibility;overflow-y:hidden;overscroll-behavior:contain}.modal::backdrop,.modal:not(dialog:not(.modal-open)){background-color:#0006;animation:modal-pop .2s ease-out}.modal-backdrop{z-index:-1;grid-column-start:1;grid-row-start:1;display:grid;align-self:stretch;justify-self:stretch;color:transparent}.modal-box{grid-column-start:1;grid-row-start:1;width:91.666667%;max-width:32rem;--tw-scale-x:.9;--tw-scale-y:.9;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-bottom-right-radius:var(--rounded-box,1rem);border-bottom-left-radius:var(--rounded-box,1rem);border-top-left-radius:var(--rounded-box,1rem);border-top-right-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));padding:1.5rem;transition-property:color,background-color,border-color,text-decoration-color,fill,stroke,opacity,box-shadow,transform,filter,backdrop-filter;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-duration:.2s;transition-timing-function:cubic-bezier(0,0,.2,1);box-shadow:rgba(0,0,0,.25) 0 25px 50px -12px;overflow-y:auto;overscroll-behavior:contain}.modal-open .modal-box,.modal-toggle:checked+.modal .modal-box,.modal:target .modal-box,.modal[open] .modal-box{--tw-translate-y:0px;--tw-scale-x:1;--tw-scale-y:1;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y))}.modal-action{margin-top:1.5rem;justify-content:flex-end}.modal-action>:not([hidden])~:not([hidden]){--tw-space-x-reverse:0;margin-right:calc(.5rem * var(--tw-space-x-reverse));margin-left:calc(.5rem * calc(1 - var(--tw-space-x-reverse)))}@keyframes modal-pop{0%{opacity:0}}.navbar{padding:var(--navbar-padding,.5rem);min-height:4rem;width:100%}.progress{height:.5rem;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.2))}.progress::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)))}.progress-primary::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)))}.progress-secondary::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)))}.progress-accent::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)))}.progress-info::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)))}.progress-success::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)))}.progress-warning::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)))}.progress-error::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)))}.progress:indeterminate{--progress-color:var(--fallback-bc,oklch(var(--bc)/1))}.progress-primary:indeterminate{--progress-color:var(--fallback-p,oklch(var(--p)/1))}.progress-secondary:indeterminate{--progress-color:var(--fallback-s,oklch(var(--s)/1))}.progress-accent:indeterminate{--progress-color:var(--fallback-a,oklch(var(--a)/1))}.progress-info:indeterminate{--progress-color:var(--fallback-in,oklch(var(--in)/1))}.progress-success:indeterminate{--progress-color:var(--fallback-su,oklch(var(--su)/1))}.progress-warning:indeterminate{--progress-color:var(--fallback-wa,oklch(var(--wa)/1))}.progress-error:indeterminate{--progress-color:var(--fallback-er,oklch(var(--er)/1))}.progress::-webkit-progress-bar{border-radius:var(--rounded-box,1rem);background-color:transparent}.progress::-webkit-progress-value{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)))}.progress-primary::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)))}.progress-secondary::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)))}.progress-accent::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)))}.progress-info::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)))}.progress-success::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)))}.progress-warning::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)))}.progress-error::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)))}.progress:indeterminate{background-image:repeating-linear-gradient(90deg,var(--progress-color) -1%,var(--progress-color) 10%,transparent 10%,transparent 90%);background-size:200%;background-position-x:15%;animation:progress-loading 5s ease-in-out infinite}.progress:indeterminate::-moz-progress-bar{background-color:transparent;background-image:repeating-linear-gradient(90deg,var(--progress-color) -1%,var(--progress-color) 10%,transparent 10%,transparent 90%);background-size:200%;background-position-x:15%;animation:progress-loading 5s ease-in-out infinite}@keyframes progress-loading{50%{background-position-x:-115%}}.radial-progress{--value:0;--size:5rem;--thickness:calc(var(--size) / 10)}.radial-progress:after{background-color:currentColor}.radio{--chkbg:var(--bc);height:1.5rem;width:1.5rem;cursor:pointer;appearance:none;border-radius:9999px;border-width:1px;border-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-border-opacity)));--tw-border-opacity:0.2}.radio:focus{box-shadow:none}.radio:focus-visible{outline-style:solid;outline-width:2px;outline-offset:2px;outline-color:var(--fallback-bc,oklch(var(--bc)/1))}.radio:checked,.radio[aria-checked=true]{--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));background-image:none;animation:radiomark var(--animation-input,.2s) ease-out;box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}.radio-primary{--chkbg:var(--p);--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)))}@media(hover:hover){.radio-primary:hover{--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)))}}.radio-primary:focus-visible{outline-color:var(--fallback-p,oklch(var(--p)/1))}.radio-primary:checked,.radio-primary[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-pc,oklch(var(--pc)/var(--tw-text-opacity)))}.radio-secondary{--chkbg:var(--s);--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)))}@media(hover:hover){.radio-secondary:hover{--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)))}}.radio-secondary:focus-visible{outline-color:var(--fallback-s,oklch(var(--s)/1))}.radio-secondary:checked,.radio-secondary[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-sc,oklch(var(--sc)/var(--tw-text-opacity)))}.radio-accent{--chkbg:var(--a);--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)))}@media(hover:hover){.radio-accent:hover{--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)))}}.radio-accent:focus-visible{outline-color:var(--fallback-a,oklch(var(--a)/1))}.radio-accent:checked,.radio-accent[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-ac,oklch(var(--ac)/var(--tw-text-opacity)))}.radio-success{--chkbg:var(--su);--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)))}@media(hover:hover){.radio-success:hover{--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)))}}.radio-success:focus-visible{outline-color:var(--fallback-su,oklch(var(--su)/1))}.radio-success:checked,.radio-success[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-suc,oklch(var(--suc)/var(--tw-text-opacity)))}.radio-warning{--chkbg:var(--wa);--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)))}@media(hover:hover){.radio-warning:hover{--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)))}}.radio-warning:focus-visible{outline-color:var(--fallback-wa,oklch(var(--wa)/1))}.radio-warning:checked,.radio-warning[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-wac,oklch(var(--wac)/var(--tw-text-opacity)))}.radio-info{--chkbg:var(--in);--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)))}@media(hover:hover){.radio-info:hover{--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)))}}.radio-info:focus-visible{outline-color:var(--fallback-in,oklch(var(--in)/1))}.radio-info:checked,.radio-info[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-inc,oklch(var(--inc)/var(--tw-text-opacity)))}.radio-error{--chkbg:var(--er);--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)))}@media(hover:hover){.radio-error:hover{--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)))}}.radio-error:focus-visible{outline-color:var(--fallback-er,oklch(var(--er)/1))}.radio-error:checked,.radio-error[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-erc,oklch(var(--erc)/var(--tw-text-opacity)))}.radio:disabled{cursor:not-allowed;opacity:.2}@keyframes radiomark{0%{box-shadow:0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset}50%{box-shadow:0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset}100%{box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}}.radio-mark{display:none}.range{appearance:none;-webkit-appearance:none;--range-shdw:var(--fallback-bc,oklch(var(--bc)/1));overflow:hidden;border-radius:var(--rounded-box,1rem);background-color:transparent}.range:focus-visible::-webkit-slider-thumb{--focus-shadow:0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 2rem var(--range-shdw) inset}.range:focus-visible::-moz-range-thumb{--focus-shadow:0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 2rem var(--range-shdw) inset}.range::-webkit-slider-runnable-track{height:.5rem;width:100%;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-moz-range-track{height:.5rem;width:100%;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-webkit-slider-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box,1rem);border-style:none;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));appearance:none;-webkit-appearance:none;top:50%;color:var(--range-shdw);transform:translateY(-50%);--filler-size:100rem;--filler-offset:0.6rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow,0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}.range::-moz-range-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box,1rem);border-style:none;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));top:50%;color:var(--range-shdw);--filler-size:100rem;--filler-offset:0.5rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow,0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}.range-primary{--range-shdw:var(--fallback-p,oklch(var(--p)/1))}.range-secondary{--range-shdw:var(--fallback-s,oklch(var(--s)/1))}.range-accent{--range-shdw:var(--fallback-a,oklch(var(--a)/1))}.range-success{--range-shdw:var(--fallback-su,oklch(var(--su)/1))}.range-warning{--range-shdw:var(--fallback-wa,oklch(var(--wa)/1))}.range-info{--range-shdw:var(--fallback-in,oklch(var(--in)/1))}.range-error{--range-shdw:var(--fallback-er,oklch(var(--er)/1))}.rating input{appearance:none;-webkit-appearance:none}.rating :where(input){animation:rating-pop var(--animation-input,.25s) ease-out;height:1.5rem;width:1.5rem;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));--tw-bg-opacity:1}.rating .rating-hidden{width:.5rem;background-color:transparent}.rating input[type=radio]:checked{background-image:none}.rating input:checked~input,.rating input[aria-checked=true]~input{--tw-bg-opacity:0.2}.rating input:focus-visible{transition-property:transform;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-duration:.3s;transition-timing-function:cubic-bezier(0,0,.2,1);transform:translateY(-.125em)}.rating input:active:focus{animation:none;transform:translateY(-.125em)}.rating-half :where(input:not(.rating-hidden)){width:.75rem}@keyframes rating-pop{0%{transform:translateY(-.125em)}40%{transform:translateY(-.125em)}100%{transform:translateY(0)}}.select{border-radius:var(--rounded-btn,.5rem);border-width:1px;border-color:transparent;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));padding-inline-end:2.5rem}.select-bordered{border-color:var(--fallback-bc,oklch(var(--bc)/.2))}.select{background-image:linear-gradient(45deg,transparent 50%,currentColor 50%),linear-gradient(135deg,currentColor 50%,transparent 50%);background-position:calc(100% - 20px) calc(1px + 50%),calc(100% - 16.1px) calc(1px + 50%);background-size:4px 4px,4px 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|| self, + global.markdownit = factory()); +})(this, (function() { + "use strict"; + function createCommonjsModule(fn, basedir, module) { + return module = { + path: basedir, + exports: {}, + require: function(path, base) { + return commonjsRequire(path, base === undefined || base === null ? module.path : base); + } + }, fn(module, module.exports), module.exports; + } + function getAugmentedNamespace(n) { + if (n.__esModule) return n; + var a = Object.defineProperty({}, "__esModule", { + value: true + }); + Object.keys(n).forEach((function(k) { + var d = Object.getOwnPropertyDescriptor(n, k); + Object.defineProperty(a, k, d.get ? d : { + enumerable: true, + get: function() { + return n[k]; + } + }); + })); + return a; + } + function commonjsRequire() { + throw new Error("Dynamic requires are not currently supported by @rollup/plugin-commonjs"); + } + var require$$0 = { + Aacute: "\xc1", + aacute: "\xe1", + Abreve: "\u0102", + abreve: "\u0103", + ac: "\u223e", + acd: "\u223f", + acE: "\u223e\u0333", + Acirc: "\xc2", + acirc: "\xe2", + acute: "\xb4", + Acy: "\u0410", + acy: "\u0430", + AElig: "\xc6", + aelig: "\xe6", + af: "\u2061", + Afr: "\ud835\udd04", + afr: "\ud835\udd1e", + Agrave: "\xc0", + agrave: "\xe0", + alefsym: "\u2135", + aleph: "\u2135", + Alpha: "\u0391", + alpha: "\u03b1", + Amacr: "\u0100", + amacr: "\u0101", + amalg: "\u2a3f", + amp: "&", + AMP: "&", + andand: "\u2a55", + And: "\u2a53", + and: "\u2227", + andd: "\u2a5c", + andslope: "\u2a58", + andv: "\u2a5a", + ang: "\u2220", + ange: "\u29a4", + angle: "\u2220", + angmsdaa: "\u29a8", + angmsdab: "\u29a9", + angmsdac: "\u29aa", + angmsdad: "\u29ab", + angmsdae: "\u29ac", + angmsdaf: "\u29ad", + angmsdag: "\u29ae", + angmsdah: "\u29af", + angmsd: "\u2221", + angrt: "\u221f", + angrtvb: "\u22be", + angrtvbd: "\u299d", + angsph: "\u2222", + angst: "\xc5", + angzarr: "\u237c", + Aogon: "\u0104", + aogon: "\u0105", + Aopf: "\ud835\udd38", + aopf: "\ud835\udd52", + apacir: "\u2a6f", + ap: "\u2248", + apE: "\u2a70", + ape: "\u224a", + apid: "\u224b", + apos: "'", + ApplyFunction: "\u2061", + approx: "\u2248", + approxeq: "\u224a", + Aring: "\xc5", + aring: "\xe5", + Ascr: "\ud835\udc9c", + ascr: "\ud835\udcb6", + Assign: "\u2254", + ast: "*", + asymp: "\u2248", + asympeq: "\u224d", + Atilde: "\xc3", + atilde: "\xe3", + Auml: "\xc4", + auml: "\xe4", + awconint: "\u2233", + awint: "\u2a11", + backcong: "\u224c", + backepsilon: "\u03f6", + backprime: "\u2035", + backsim: "\u223d", + backsimeq: "\u22cd", + Backslash: "\u2216", + Barv: "\u2ae7", + barvee: "\u22bd", + barwed: "\u2305", + Barwed: "\u2306", + barwedge: "\u2305", + bbrk: "\u23b5", + bbrktbrk: "\u23b6", + bcong: "\u224c", + Bcy: "\u0411", + bcy: "\u0431", + bdquo: "\u201e", + becaus: "\u2235", + because: "\u2235", + Because: "\u2235", + bemptyv: "\u29b0", + bepsi: "\u03f6", + bernou: "\u212c", + Bernoullis: "\u212c", + Beta: "\u0392", + beta: "\u03b2", + beth: "\u2136", + between: "\u226c", + Bfr: "\ud835\udd05", + bfr: "\ud835\udd1f", + bigcap: "\u22c2", + bigcirc: "\u25ef", + bigcup: "\u22c3", + bigodot: "\u2a00", + bigoplus: "\u2a01", + bigotimes: "\u2a02", + bigsqcup: "\u2a06", + bigstar: "\u2605", + bigtriangledown: "\u25bd", + bigtriangleup: "\u25b3", + biguplus: "\u2a04", + bigvee: "\u22c1", + bigwedge: "\u22c0", + bkarow: "\u290d", + blacklozenge: "\u29eb", + blacksquare: "\u25aa", + blacktriangle: "\u25b4", + blacktriangledown: "\u25be", + blacktriangleleft: "\u25c2", + blacktriangleright: "\u25b8", + blank: "\u2423", + blk12: "\u2592", + blk14: "\u2591", + blk34: "\u2593", + block: "\u2588", + bne: "=\u20e5", + bnequiv: "\u2261\u20e5", + bNot: "\u2aed", + bnot: "\u2310", + Bopf: "\ud835\udd39", + bopf: "\ud835\udd53", + bot: "\u22a5", + bottom: "\u22a5", + bowtie: "\u22c8", + boxbox: "\u29c9", + boxdl: "\u2510", + boxdL: "\u2555", + boxDl: "\u2556", + boxDL: "\u2557", + boxdr: "\u250c", + boxdR: "\u2552", + boxDr: "\u2553", + boxDR: "\u2554", + boxh: "\u2500", + boxH: "\u2550", + boxhd: "\u252c", + boxHd: "\u2564", + boxhD: "\u2565", + boxHD: "\u2566", + boxhu: "\u2534", + boxHu: "\u2567", + boxhU: "\u2568", + boxHU: "\u2569", + boxminus: "\u229f", + boxplus: "\u229e", + boxtimes: "\u22a0", + boxul: "\u2518", + boxuL: "\u255b", + boxUl: "\u255c", + boxUL: "\u255d", + boxur: "\u2514", + boxuR: "\u2558", + boxUr: "\u2559", + boxUR: "\u255a", + boxv: "\u2502", + boxV: "\u2551", + boxvh: "\u253c", + boxvH: "\u256a", + boxVh: "\u256b", + boxVH: "\u256c", + boxvl: "\u2524", + boxvL: "\u2561", + boxVl: "\u2562", + boxVL: "\u2563", + boxvr: "\u251c", + boxvR: "\u255e", + boxVr: "\u255f", + boxVR: "\u2560", + bprime: "\u2035", + breve: "\u02d8", + Breve: "\u02d8", + brvbar: "\xa6", + bscr: "\ud835\udcb7", + Bscr: "\u212c", + bsemi: "\u204f", + bsim: "\u223d", + bsime: "\u22cd", + bsolb: "\u29c5", + bsol: "\\", + bsolhsub: "\u27c8", + bull: "\u2022", + bullet: "\u2022", + bump: "\u224e", + bumpE: "\u2aae", + bumpe: "\u224f", + Bumpeq: "\u224e", + bumpeq: "\u224f", + Cacute: "\u0106", + cacute: "\u0107", + capand: "\u2a44", + capbrcup: "\u2a49", + capcap: "\u2a4b", + cap: "\u2229", + Cap: "\u22d2", + capcup: "\u2a47", + capdot: "\u2a40", + CapitalDifferentialD: "\u2145", + caps: "\u2229\ufe00", + caret: "\u2041", + caron: "\u02c7", + Cayleys: "\u212d", + ccaps: "\u2a4d", + Ccaron: "\u010c", + ccaron: "\u010d", + Ccedil: "\xc7", + ccedil: "\xe7", + Ccirc: "\u0108", + ccirc: "\u0109", + Cconint: "\u2230", + ccups: "\u2a4c", + ccupssm: "\u2a50", + Cdot: "\u010a", + cdot: "\u010b", + cedil: "\xb8", + Cedilla: "\xb8", + cemptyv: "\u29b2", + cent: "\xa2", + centerdot: "\xb7", + CenterDot: "\xb7", + cfr: "\ud835\udd20", + Cfr: "\u212d", + CHcy: "\u0427", + chcy: "\u0447", + check: "\u2713", + checkmark: "\u2713", + Chi: "\u03a7", + chi: "\u03c7", + circ: "\u02c6", + circeq: "\u2257", + circlearrowleft: "\u21ba", + circlearrowright: "\u21bb", + circledast: "\u229b", + circledcirc: "\u229a", + circleddash: "\u229d", + CircleDot: "\u2299", + circledR: "\xae", + circledS: "\u24c8", + CircleMinus: "\u2296", + CirclePlus: "\u2295", + CircleTimes: "\u2297", + cir: "\u25cb", + cirE: "\u29c3", + cire: "\u2257", + cirfnint: "\u2a10", + cirmid: "\u2aef", + cirscir: "\u29c2", + ClockwiseContourIntegral: "\u2232", + CloseCurlyDoubleQuote: "\u201d", + CloseCurlyQuote: "\u2019", + clubs: "\u2663", + clubsuit: "\u2663", + colon: ":", + Colon: "\u2237", + Colone: "\u2a74", + colone: "\u2254", + coloneq: "\u2254", + comma: ",", + commat: "@", + comp: "\u2201", + compfn: "\u2218", + complement: "\u2201", + complexes: "\u2102", + cong: "\u2245", + congdot: "\u2a6d", + Congruent: "\u2261", + conint: "\u222e", + Conint: "\u222f", + ContourIntegral: "\u222e", + copf: "\ud835\udd54", + Copf: "\u2102", + coprod: "\u2210", + Coproduct: "\u2210", + copy: "\xa9", + COPY: "\xa9", + copysr: "\u2117", + CounterClockwiseContourIntegral: "\u2233", + crarr: "\u21b5", + cross: "\u2717", + Cross: "\u2a2f", + Cscr: "\ud835\udc9e", + cscr: "\ud835\udcb8", + csub: "\u2acf", + csube: "\u2ad1", + csup: "\u2ad0", + csupe: "\u2ad2", + ctdot: "\u22ef", + cudarrl: "\u2938", + cudarrr: "\u2935", + cuepr: "\u22de", + cuesc: "\u22df", + cularr: "\u21b6", + cularrp: "\u293d", + cupbrcap: "\u2a48", + cupcap: "\u2a46", + CupCap: "\u224d", + cup: "\u222a", + Cup: "\u22d3", + cupcup: "\u2a4a", + cupdot: "\u228d", + cupor: "\u2a45", + cups: "\u222a\ufe00", + curarr: "\u21b7", + curarrm: "\u293c", + curlyeqprec: "\u22de", + curlyeqsucc: "\u22df", + curlyvee: "\u22ce", + curlywedge: "\u22cf", + curren: "\xa4", + curvearrowleft: "\u21b6", + curvearrowright: "\u21b7", + cuvee: "\u22ce", + cuwed: "\u22cf", + cwconint: "\u2232", + cwint: "\u2231", + cylcty: "\u232d", + dagger: "\u2020", + Dagger: "\u2021", + daleth: "\u2138", + darr: "\u2193", + Darr: "\u21a1", + dArr: "\u21d3", + dash: "\u2010", + Dashv: "\u2ae4", + dashv: "\u22a3", + dbkarow: "\u290f", + dblac: "\u02dd", + Dcaron: "\u010e", + dcaron: "\u010f", + Dcy: "\u0414", + dcy: "\u0434", + ddagger: "\u2021", + ddarr: "\u21ca", + DD: "\u2145", + dd: "\u2146", + DDotrahd: "\u2911", + ddotseq: "\u2a77", + deg: "\xb0", + Del: "\u2207", + Delta: "\u0394", + delta: "\u03b4", + demptyv: "\u29b1", + dfisht: "\u297f", + Dfr: "\ud835\udd07", + dfr: "\ud835\udd21", + dHar: "\u2965", + dharl: "\u21c3", + dharr: "\u21c2", + DiacriticalAcute: "\xb4", + DiacriticalDot: "\u02d9", + DiacriticalDoubleAcute: "\u02dd", + DiacriticalGrave: "`", + DiacriticalTilde: "\u02dc", + diam: "\u22c4", + diamond: "\u22c4", + Diamond: "\u22c4", + diamondsuit: "\u2666", + diams: "\u2666", + die: "\xa8", + DifferentialD: "\u2146", + digamma: "\u03dd", + disin: "\u22f2", + div: "\xf7", + divide: "\xf7", + divideontimes: "\u22c7", + divonx: "\u22c7", + DJcy: "\u0402", + djcy: "\u0452", + dlcorn: "\u231e", + dlcrop: "\u230d", + dollar: "$", + Dopf: "\ud835\udd3b", + dopf: "\ud835\udd55", + Dot: "\xa8", + dot: "\u02d9", + DotDot: "\u20dc", + doteq: "\u2250", + doteqdot: "\u2251", + DotEqual: "\u2250", + dotminus: "\u2238", + dotplus: "\u2214", + dotsquare: "\u22a1", + doublebarwedge: "\u2306", + DoubleContourIntegral: "\u222f", + DoubleDot: "\xa8", + DoubleDownArrow: "\u21d3", + DoubleLeftArrow: "\u21d0", + DoubleLeftRightArrow: "\u21d4", + DoubleLeftTee: "\u2ae4", + DoubleLongLeftArrow: "\u27f8", + DoubleLongLeftRightArrow: "\u27fa", + DoubleLongRightArrow: "\u27f9", + DoubleRightArrow: "\u21d2", + DoubleRightTee: "\u22a8", + DoubleUpArrow: "\u21d1", + DoubleUpDownArrow: "\u21d5", + DoubleVerticalBar: "\u2225", + DownArrowBar: "\u2913", + downarrow: "\u2193", + DownArrow: "\u2193", + Downarrow: "\u21d3", + DownArrowUpArrow: "\u21f5", + DownBreve: "\u0311", + downdownarrows: "\u21ca", + downharpoonleft: "\u21c3", + downharpoonright: "\u21c2", + DownLeftRightVector: "\u2950", + DownLeftTeeVector: "\u295e", + DownLeftVectorBar: "\u2956", + DownLeftVector: "\u21bd", + DownRightTeeVector: "\u295f", + DownRightVectorBar: "\u2957", + DownRightVector: "\u21c1", + DownTeeArrow: "\u21a7", + DownTee: "\u22a4", + drbkarow: "\u2910", + drcorn: "\u231f", + drcrop: "\u230c", + Dscr: "\ud835\udc9f", + dscr: "\ud835\udcb9", + DScy: "\u0405", + dscy: "\u0455", + dsol: "\u29f6", + Dstrok: "\u0110", + dstrok: "\u0111", + dtdot: "\u22f1", + dtri: "\u25bf", + dtrif: "\u25be", + duarr: "\u21f5", + duhar: "\u296f", + dwangle: "\u29a6", + DZcy: "\u040f", + dzcy: "\u045f", + dzigrarr: "\u27ff", + Eacute: "\xc9", + eacute: "\xe9", + easter: "\u2a6e", + Ecaron: "\u011a", + ecaron: "\u011b", + Ecirc: "\xca", + ecirc: "\xea", + ecir: "\u2256", + ecolon: "\u2255", + Ecy: "\u042d", + ecy: "\u044d", + eDDot: "\u2a77", + Edot: "\u0116", + edot: "\u0117", + eDot: "\u2251", + ee: "\u2147", + efDot: "\u2252", + Efr: "\ud835\udd08", + efr: "\ud835\udd22", + eg: "\u2a9a", + Egrave: "\xc8", + egrave: "\xe8", + egs: "\u2a96", + egsdot: "\u2a98", + el: "\u2a99", + Element: "\u2208", + elinters: "\u23e7", + ell: "\u2113", + els: "\u2a95", + elsdot: "\u2a97", + Emacr: "\u0112", + emacr: "\u0113", + empty: "\u2205", + emptyset: "\u2205", + EmptySmallSquare: "\u25fb", + emptyv: "\u2205", + EmptyVerySmallSquare: "\u25ab", + emsp13: "\u2004", + emsp14: "\u2005", + emsp: "\u2003", + ENG: "\u014a", + eng: "\u014b", + ensp: "\u2002", + Eogon: "\u0118", + eogon: "\u0119", + Eopf: "\ud835\udd3c", + eopf: "\ud835\udd56", + epar: "\u22d5", + eparsl: "\u29e3", + eplus: "\u2a71", + epsi: "\u03b5", + Epsilon: "\u0395", + epsilon: "\u03b5", + epsiv: "\u03f5", + eqcirc: "\u2256", + eqcolon: "\u2255", + eqsim: "\u2242", + eqslantgtr: "\u2a96", + eqslantless: "\u2a95", + Equal: "\u2a75", + equals: "=", + EqualTilde: "\u2242", + equest: "\u225f", + Equilibrium: "\u21cc", + equiv: "\u2261", + equivDD: "\u2a78", + eqvparsl: "\u29e5", + erarr: "\u2971", + erDot: "\u2253", + escr: "\u212f", + Escr: "\u2130", + esdot: "\u2250", + Esim: "\u2a73", + esim: "\u2242", + Eta: "\u0397", + eta: "\u03b7", + ETH: "\xd0", + eth: "\xf0", + Euml: "\xcb", + euml: "\xeb", + euro: "\u20ac", + excl: "!", + exist: "\u2203", + Exists: "\u2203", + expectation: "\u2130", + exponentiale: "\u2147", + ExponentialE: "\u2147", + fallingdotseq: "\u2252", + Fcy: "\u0424", + fcy: "\u0444", + female: "\u2640", + ffilig: "\ufb03", + fflig: "\ufb00", + ffllig: "\ufb04", + Ffr: "\ud835\udd09", + ffr: "\ud835\udd23", + filig: "\ufb01", + FilledSmallSquare: "\u25fc", + FilledVerySmallSquare: "\u25aa", + fjlig: "fj", + flat: "\u266d", + fllig: "\ufb02", + fltns: "\u25b1", + fnof: "\u0192", + Fopf: "\ud835\udd3d", + fopf: "\ud835\udd57", + forall: "\u2200", + ForAll: "\u2200", + fork: "\u22d4", + forkv: "\u2ad9", + Fouriertrf: "\u2131", + fpartint: "\u2a0d", + frac12: "\xbd", + frac13: "\u2153", + frac14: "\xbc", + frac15: "\u2155", + frac16: "\u2159", + frac18: "\u215b", + frac23: "\u2154", + frac25: "\u2156", + frac34: "\xbe", + frac35: "\u2157", + frac38: "\u215c", + frac45: "\u2158", + frac56: "\u215a", + frac58: "\u215d", + frac78: "\u215e", + frasl: "\u2044", + frown: "\u2322", + fscr: "\ud835\udcbb", + Fscr: "\u2131", + gacute: "\u01f5", + Gamma: "\u0393", + gamma: "\u03b3", + Gammad: "\u03dc", + gammad: "\u03dd", + gap: "\u2a86", + Gbreve: "\u011e", + gbreve: "\u011f", + Gcedil: "\u0122", + Gcirc: "\u011c", + gcirc: "\u011d", + Gcy: "\u0413", + gcy: "\u0433", + Gdot: "\u0120", + gdot: "\u0121", + ge: "\u2265", + gE: "\u2267", + gEl: "\u2a8c", + gel: "\u22db", + geq: "\u2265", + geqq: "\u2267", + geqslant: "\u2a7e", + gescc: "\u2aa9", + ges: "\u2a7e", + gesdot: "\u2a80", + gesdoto: "\u2a82", + gesdotol: "\u2a84", + gesl: "\u22db\ufe00", + gesles: "\u2a94", + Gfr: "\ud835\udd0a", + gfr: "\ud835\udd24", + gg: "\u226b", + Gg: "\u22d9", + ggg: "\u22d9", + gimel: "\u2137", + GJcy: "\u0403", + gjcy: "\u0453", + gla: "\u2aa5", + gl: "\u2277", + glE: "\u2a92", + glj: "\u2aa4", + gnap: "\u2a8a", + gnapprox: "\u2a8a", + gne: "\u2a88", + gnE: "\u2269", + gneq: "\u2a88", + gneqq: "\u2269", + gnsim: "\u22e7", + Gopf: "\ud835\udd3e", + gopf: "\ud835\udd58", + grave: "`", + GreaterEqual: "\u2265", + GreaterEqualLess: "\u22db", + GreaterFullEqual: "\u2267", + GreaterGreater: "\u2aa2", + GreaterLess: "\u2277", + GreaterSlantEqual: "\u2a7e", + GreaterTilde: "\u2273", + Gscr: "\ud835\udca2", + gscr: "\u210a", + gsim: "\u2273", + gsime: "\u2a8e", + gsiml: "\u2a90", + gtcc: "\u2aa7", + gtcir: "\u2a7a", + gt: ">", + GT: ">", + Gt: "\u226b", + gtdot: "\u22d7", + gtlPar: "\u2995", + gtquest: "\u2a7c", + gtrapprox: "\u2a86", + gtrarr: "\u2978", + gtrdot: "\u22d7", + gtreqless: "\u22db", + gtreqqless: "\u2a8c", + gtrless: "\u2277", + gtrsim: "\u2273", + gvertneqq: "\u2269\ufe00", + gvnE: "\u2269\ufe00", + Hacek: "\u02c7", + hairsp: "\u200a", + half: "\xbd", + hamilt: "\u210b", + HARDcy: "\u042a", + hardcy: "\u044a", + harrcir: "\u2948", + harr: "\u2194", + hArr: "\u21d4", + harrw: "\u21ad", + Hat: "^", + hbar: "\u210f", + Hcirc: "\u0124", + hcirc: "\u0125", + hearts: "\u2665", + heartsuit: "\u2665", + hellip: "\u2026", + hercon: "\u22b9", + hfr: "\ud835\udd25", + Hfr: "\u210c", + HilbertSpace: "\u210b", + hksearow: "\u2925", + hkswarow: "\u2926", + hoarr: "\u21ff", + homtht: "\u223b", + hookleftarrow: "\u21a9", + hookrightarrow: "\u21aa", + hopf: "\ud835\udd59", + Hopf: "\u210d", + horbar: "\u2015", + HorizontalLine: "\u2500", + hscr: "\ud835\udcbd", + Hscr: "\u210b", + hslash: "\u210f", + Hstrok: "\u0126", + hstrok: "\u0127", + HumpDownHump: "\u224e", + HumpEqual: "\u224f", + hybull: "\u2043", + hyphen: "\u2010", + Iacute: "\xcd", + iacute: "\xed", + ic: "\u2063", + Icirc: "\xce", + icirc: "\xee", + Icy: "\u0418", + icy: "\u0438", + Idot: "\u0130", + IEcy: "\u0415", + iecy: "\u0435", + iexcl: "\xa1", + iff: "\u21d4", + ifr: "\ud835\udd26", + Ifr: "\u2111", + Igrave: "\xcc", + igrave: "\xec", + ii: "\u2148", + iiiint: "\u2a0c", + iiint: "\u222d", + iinfin: "\u29dc", + iiota: "\u2129", + IJlig: "\u0132", + ijlig: "\u0133", + Imacr: "\u012a", + imacr: "\u012b", + image: "\u2111", + ImaginaryI: "\u2148", + imagline: "\u2110", + imagpart: "\u2111", + imath: "\u0131", + Im: "\u2111", + imof: "\u22b7", + imped: "\u01b5", + Implies: "\u21d2", + incare: "\u2105", + in: "\u2208", + infin: "\u221e", + infintie: "\u29dd", + inodot: "\u0131", + intcal: "\u22ba", + int: "\u222b", + Int: "\u222c", + integers: "\u2124", + Integral: "\u222b", + intercal: "\u22ba", + Intersection: "\u22c2", + intlarhk: "\u2a17", + intprod: "\u2a3c", + InvisibleComma: "\u2063", + InvisibleTimes: "\u2062", + IOcy: "\u0401", + iocy: "\u0451", + Iogon: "\u012e", + iogon: "\u012f", + Iopf: "\ud835\udd40", + iopf: "\ud835\udd5a", + Iota: "\u0399", + iota: "\u03b9", + iprod: "\u2a3c", + iquest: "\xbf", + iscr: "\ud835\udcbe", + Iscr: "\u2110", + isin: "\u2208", + isindot: "\u22f5", + isinE: "\u22f9", + isins: "\u22f4", + isinsv: "\u22f3", + isinv: "\u2208", + it: "\u2062", + Itilde: "\u0128", + itilde: "\u0129", + Iukcy: "\u0406", + iukcy: "\u0456", + Iuml: "\xcf", + iuml: "\xef", + Jcirc: "\u0134", + jcirc: "\u0135", + Jcy: "\u0419", + jcy: "\u0439", + Jfr: "\ud835\udd0d", + jfr: "\ud835\udd27", + jmath: "\u0237", + Jopf: "\ud835\udd41", + jopf: "\ud835\udd5b", + Jscr: "\ud835\udca5", + jscr: "\ud835\udcbf", + Jsercy: "\u0408", + jsercy: "\u0458", + Jukcy: "\u0404", + jukcy: "\u0454", + Kappa: "\u039a", + kappa: "\u03ba", + kappav: "\u03f0", + Kcedil: "\u0136", + kcedil: "\u0137", + Kcy: "\u041a", + kcy: "\u043a", + Kfr: "\ud835\udd0e", + kfr: "\ud835\udd28", + kgreen: "\u0138", + KHcy: "\u0425", + khcy: "\u0445", + KJcy: "\u040c", + kjcy: "\u045c", + Kopf: "\ud835\udd42", + kopf: "\ud835\udd5c", + Kscr: "\ud835\udca6", + kscr: "\ud835\udcc0", + lAarr: "\u21da", + Lacute: "\u0139", + lacute: "\u013a", + laemptyv: "\u29b4", + lagran: "\u2112", + Lambda: "\u039b", + lambda: "\u03bb", + lang: "\u27e8", + Lang: "\u27ea", + langd: "\u2991", + langle: "\u27e8", + lap: "\u2a85", + Laplacetrf: "\u2112", + laquo: "\xab", + larrb: "\u21e4", + larrbfs: "\u291f", + larr: "\u2190", + Larr: "\u219e", + lArr: "\u21d0", + larrfs: "\u291d", + larrhk: "\u21a9", + larrlp: "\u21ab", + larrpl: "\u2939", + larrsim: "\u2973", + larrtl: "\u21a2", + latail: "\u2919", + lAtail: "\u291b", + lat: "\u2aab", + late: "\u2aad", + lates: "\u2aad\ufe00", + lbarr: "\u290c", + lBarr: "\u290e", + lbbrk: "\u2772", + lbrace: "{", + lbrack: "[", + lbrke: "\u298b", + lbrksld: "\u298f", + lbrkslu: "\u298d", + Lcaron: "\u013d", + lcaron: "\u013e", + Lcedil: "\u013b", + lcedil: "\u013c", + lceil: "\u2308", + lcub: "{", + Lcy: "\u041b", + lcy: "\u043b", + ldca: "\u2936", + ldquo: "\u201c", + ldquor: "\u201e", + ldrdhar: "\u2967", + ldrushar: "\u294b", + ldsh: "\u21b2", + le: "\u2264", + lE: "\u2266", + LeftAngleBracket: "\u27e8", + LeftArrowBar: "\u21e4", + leftarrow: "\u2190", + LeftArrow: "\u2190", + Leftarrow: "\u21d0", + LeftArrowRightArrow: "\u21c6", + leftarrowtail: "\u21a2", + LeftCeiling: "\u2308", + LeftDoubleBracket: "\u27e6", + LeftDownTeeVector: "\u2961", + LeftDownVectorBar: "\u2959", + LeftDownVector: "\u21c3", + LeftFloor: "\u230a", + leftharpoondown: "\u21bd", + leftharpoonup: "\u21bc", + leftleftarrows: "\u21c7", + leftrightarrow: "\u2194", + LeftRightArrow: "\u2194", + Leftrightarrow: "\u21d4", + leftrightarrows: "\u21c6", + leftrightharpoons: "\u21cb", + leftrightsquigarrow: "\u21ad", + LeftRightVector: "\u294e", + LeftTeeArrow: "\u21a4", + LeftTee: "\u22a3", + LeftTeeVector: "\u295a", + leftthreetimes: "\u22cb", + LeftTriangleBar: "\u29cf", + LeftTriangle: "\u22b2", + LeftTriangleEqual: "\u22b4", + LeftUpDownVector: "\u2951", + LeftUpTeeVector: "\u2960", + LeftUpVectorBar: "\u2958", + LeftUpVector: "\u21bf", + LeftVectorBar: "\u2952", + LeftVector: "\u21bc", + lEg: "\u2a8b", + leg: "\u22da", + leq: "\u2264", + leqq: "\u2266", + leqslant: "\u2a7d", + lescc: "\u2aa8", + les: "\u2a7d", + lesdot: "\u2a7f", + lesdoto: "\u2a81", + lesdotor: "\u2a83", + lesg: "\u22da\ufe00", + lesges: "\u2a93", + lessapprox: "\u2a85", + lessdot: "\u22d6", + lesseqgtr: "\u22da", + lesseqqgtr: "\u2a8b", + LessEqualGreater: "\u22da", + LessFullEqual: "\u2266", + LessGreater: "\u2276", + lessgtr: "\u2276", + LessLess: "\u2aa1", + lesssim: "\u2272", + LessSlantEqual: "\u2a7d", + LessTilde: "\u2272", + lfisht: "\u297c", + lfloor: "\u230a", + Lfr: "\ud835\udd0f", + lfr: "\ud835\udd29", + lg: "\u2276", + lgE: "\u2a91", + lHar: "\u2962", + lhard: "\u21bd", + lharu: "\u21bc", + lharul: "\u296a", + lhblk: "\u2584", + LJcy: "\u0409", + ljcy: "\u0459", + llarr: "\u21c7", + ll: "\u226a", + Ll: "\u22d8", + llcorner: "\u231e", + Lleftarrow: "\u21da", + llhard: "\u296b", + lltri: "\u25fa", + Lmidot: "\u013f", + lmidot: "\u0140", + lmoustache: "\u23b0", + lmoust: "\u23b0", + lnap: "\u2a89", + lnapprox: "\u2a89", + lne: "\u2a87", + lnE: "\u2268", + lneq: "\u2a87", + lneqq: "\u2268", + lnsim: "\u22e6", + loang: "\u27ec", + loarr: "\u21fd", + lobrk: "\u27e6", + longleftarrow: "\u27f5", + LongLeftArrow: "\u27f5", + Longleftarrow: "\u27f8", + longleftrightarrow: "\u27f7", + LongLeftRightArrow: "\u27f7", + Longleftrightarrow: "\u27fa", + longmapsto: "\u27fc", + longrightarrow: "\u27f6", + LongRightArrow: "\u27f6", + Longrightarrow: "\u27f9", + looparrowleft: "\u21ab", + looparrowright: "\u21ac", + lopar: "\u2985", + Lopf: "\ud835\udd43", + lopf: "\ud835\udd5d", + loplus: "\u2a2d", + lotimes: "\u2a34", + lowast: "\u2217", + lowbar: "_", + LowerLeftArrow: "\u2199", + LowerRightArrow: "\u2198", + loz: "\u25ca", + lozenge: "\u25ca", + lozf: "\u29eb", + lpar: "(", + lparlt: "\u2993", + lrarr: "\u21c6", + lrcorner: "\u231f", + lrhar: "\u21cb", + lrhard: "\u296d", + lrm: "\u200e", + lrtri: "\u22bf", + lsaquo: "\u2039", + lscr: "\ud835\udcc1", + Lscr: "\u2112", + lsh: "\u21b0", + Lsh: "\u21b0", + lsim: "\u2272", + lsime: "\u2a8d", + lsimg: "\u2a8f", + lsqb: "[", + lsquo: "\u2018", + lsquor: "\u201a", + Lstrok: "\u0141", + lstrok: "\u0142", + ltcc: "\u2aa6", + ltcir: "\u2a79", + lt: "<", + LT: "<", + Lt: "\u226a", + ltdot: "\u22d6", + lthree: "\u22cb", + ltimes: "\u22c9", + ltlarr: "\u2976", + ltquest: "\u2a7b", + ltri: "\u25c3", + ltrie: "\u22b4", + ltrif: "\u25c2", + ltrPar: "\u2996", + lurdshar: "\u294a", + luruhar: "\u2966", + lvertneqq: "\u2268\ufe00", + lvnE: "\u2268\ufe00", + macr: "\xaf", + male: "\u2642", + malt: "\u2720", + maltese: "\u2720", + Map: "\u2905", + map: "\u21a6", + mapsto: "\u21a6", + mapstodown: "\u21a7", + mapstoleft: "\u21a4", + mapstoup: "\u21a5", + marker: "\u25ae", + mcomma: "\u2a29", + Mcy: "\u041c", + mcy: "\u043c", + mdash: "\u2014", + mDDot: "\u223a", + measuredangle: "\u2221", + MediumSpace: "\u205f", + Mellintrf: "\u2133", + Mfr: "\ud835\udd10", + mfr: "\ud835\udd2a", + mho: "\u2127", + micro: "\xb5", + midast: "*", + midcir: "\u2af0", + mid: "\u2223", + middot: "\xb7", + minusb: "\u229f", + minus: "\u2212", + minusd: "\u2238", + minusdu: "\u2a2a", + MinusPlus: "\u2213", + mlcp: "\u2adb", + mldr: "\u2026", + mnplus: "\u2213", + models: "\u22a7", + Mopf: "\ud835\udd44", + mopf: "\ud835\udd5e", + mp: "\u2213", + mscr: "\ud835\udcc2", + Mscr: "\u2133", + mstpos: "\u223e", + Mu: "\u039c", + mu: "\u03bc", + multimap: "\u22b8", + mumap: "\u22b8", + nabla: "\u2207", + Nacute: "\u0143", + nacute: "\u0144", + nang: "\u2220\u20d2", + nap: "\u2249", + napE: "\u2a70\u0338", + napid: "\u224b\u0338", + napos: "\u0149", + napprox: "\u2249", + natural: "\u266e", + naturals: "\u2115", + natur: "\u266e", + nbsp: "\xa0", + nbump: "\u224e\u0338", + nbumpe: "\u224f\u0338", + ncap: "\u2a43", + Ncaron: "\u0147", + ncaron: "\u0148", + Ncedil: "\u0145", + ncedil: "\u0146", + ncong: "\u2247", + ncongdot: "\u2a6d\u0338", + ncup: "\u2a42", + Ncy: "\u041d", + ncy: "\u043d", + ndash: "\u2013", + nearhk: "\u2924", + nearr: "\u2197", + neArr: "\u21d7", + nearrow: "\u2197", + ne: "\u2260", + nedot: "\u2250\u0338", + NegativeMediumSpace: "\u200b", + NegativeThickSpace: "\u200b", + NegativeThinSpace: "\u200b", + NegativeVeryThinSpace: "\u200b", + nequiv: "\u2262", + nesear: "\u2928", + nesim: "\u2242\u0338", + NestedGreaterGreater: "\u226b", + NestedLessLess: "\u226a", + NewLine: "\n", + nexist: "\u2204", + nexists: "\u2204", + Nfr: "\ud835\udd11", + nfr: "\ud835\udd2b", + ngE: "\u2267\u0338", + nge: "\u2271", + ngeq: "\u2271", + ngeqq: "\u2267\u0338", + ngeqslant: "\u2a7e\u0338", + nges: "\u2a7e\u0338", + nGg: "\u22d9\u0338", + ngsim: "\u2275", + nGt: "\u226b\u20d2", + ngt: "\u226f", + ngtr: "\u226f", + nGtv: "\u226b\u0338", + nharr: "\u21ae", + nhArr: "\u21ce", + nhpar: "\u2af2", + ni: "\u220b", + nis: "\u22fc", + nisd: "\u22fa", + niv: "\u220b", + NJcy: "\u040a", + njcy: "\u045a", + nlarr: "\u219a", + nlArr: "\u21cd", + nldr: "\u2025", + nlE: "\u2266\u0338", + nle: "\u2270", + nleftarrow: "\u219a", + nLeftarrow: "\u21cd", + nleftrightarrow: "\u21ae", + nLeftrightarrow: "\u21ce", + nleq: "\u2270", + nleqq: "\u2266\u0338", + nleqslant: "\u2a7d\u0338", + nles: "\u2a7d\u0338", + nless: "\u226e", + nLl: "\u22d8\u0338", + nlsim: "\u2274", + nLt: "\u226a\u20d2", + nlt: "\u226e", + nltri: "\u22ea", + nltrie: "\u22ec", + nLtv: "\u226a\u0338", + nmid: "\u2224", + NoBreak: "\u2060", + NonBreakingSpace: "\xa0", + nopf: "\ud835\udd5f", + Nopf: "\u2115", + Not: "\u2aec", + not: "\xac", + NotCongruent: "\u2262", + NotCupCap: "\u226d", + NotDoubleVerticalBar: "\u2226", + NotElement: "\u2209", + NotEqual: "\u2260", + NotEqualTilde: "\u2242\u0338", + NotExists: "\u2204", + NotGreater: "\u226f", + NotGreaterEqual: "\u2271", + NotGreaterFullEqual: "\u2267\u0338", + NotGreaterGreater: "\u226b\u0338", + NotGreaterLess: "\u2279", + NotGreaterSlantEqual: "\u2a7e\u0338", + NotGreaterTilde: "\u2275", + NotHumpDownHump: "\u224e\u0338", + NotHumpEqual: "\u224f\u0338", + notin: "\u2209", + notindot: "\u22f5\u0338", + notinE: "\u22f9\u0338", + notinva: "\u2209", + notinvb: "\u22f7", + notinvc: "\u22f6", + NotLeftTriangleBar: "\u29cf\u0338", + NotLeftTriangle: "\u22ea", + NotLeftTriangleEqual: "\u22ec", + NotLess: "\u226e", + NotLessEqual: "\u2270", + NotLessGreater: "\u2278", + NotLessLess: "\u226a\u0338", + NotLessSlantEqual: "\u2a7d\u0338", + NotLessTilde: "\u2274", + NotNestedGreaterGreater: "\u2aa2\u0338", + NotNestedLessLess: "\u2aa1\u0338", + notni: "\u220c", + notniva: "\u220c", + notnivb: "\u22fe", + notnivc: "\u22fd", + NotPrecedes: "\u2280", + NotPrecedesEqual: "\u2aaf\u0338", + NotPrecedesSlantEqual: "\u22e0", + NotReverseElement: "\u220c", + NotRightTriangleBar: "\u29d0\u0338", + NotRightTriangle: "\u22eb", + NotRightTriangleEqual: "\u22ed", + NotSquareSubset: "\u228f\u0338", + NotSquareSubsetEqual: "\u22e2", + NotSquareSuperset: "\u2290\u0338", + NotSquareSupersetEqual: "\u22e3", + NotSubset: "\u2282\u20d2", + NotSubsetEqual: "\u2288", + NotSucceeds: "\u2281", + NotSucceedsEqual: "\u2ab0\u0338", + NotSucceedsSlantEqual: "\u22e1", + NotSucceedsTilde: "\u227f\u0338", + NotSuperset: "\u2283\u20d2", + NotSupersetEqual: "\u2289", + NotTilde: "\u2241", + NotTildeEqual: "\u2244", + NotTildeFullEqual: "\u2247", + NotTildeTilde: "\u2249", + NotVerticalBar: "\u2224", + nparallel: "\u2226", + npar: "\u2226", + nparsl: "\u2afd\u20e5", + npart: "\u2202\u0338", + npolint: "\u2a14", + npr: "\u2280", + nprcue: "\u22e0", + nprec: "\u2280", + npreceq: "\u2aaf\u0338", + npre: "\u2aaf\u0338", + nrarrc: "\u2933\u0338", + nrarr: "\u219b", + nrArr: "\u21cf", + nrarrw: "\u219d\u0338", + nrightarrow: "\u219b", + nRightarrow: "\u21cf", + nrtri: "\u22eb", + nrtrie: "\u22ed", + nsc: "\u2281", + nsccue: "\u22e1", + nsce: "\u2ab0\u0338", + Nscr: "\ud835\udca9", + nscr: "\ud835\udcc3", + nshortmid: "\u2224", + nshortparallel: "\u2226", + nsim: "\u2241", + nsime: "\u2244", + nsimeq: "\u2244", + nsmid: "\u2224", + nspar: "\u2226", + nsqsube: "\u22e2", + nsqsupe: "\u22e3", + nsub: "\u2284", + nsubE: "\u2ac5\u0338", + nsube: "\u2288", + nsubset: "\u2282\u20d2", + nsubseteq: "\u2288", + nsubseteqq: "\u2ac5\u0338", + nsucc: "\u2281", + nsucceq: "\u2ab0\u0338", + nsup: "\u2285", + nsupE: "\u2ac6\u0338", + nsupe: "\u2289", + nsupset: "\u2283\u20d2", + nsupseteq: "\u2289", + nsupseteqq: "\u2ac6\u0338", + ntgl: "\u2279", + Ntilde: "\xd1", + ntilde: "\xf1", + ntlg: "\u2278", + ntriangleleft: "\u22ea", + ntrianglelefteq: "\u22ec", + ntriangleright: "\u22eb", + ntrianglerighteq: "\u22ed", + Nu: "\u039d", + nu: "\u03bd", + num: "#", + numero: "\u2116", + numsp: "\u2007", + nvap: "\u224d\u20d2", + nvdash: "\u22ac", + nvDash: "\u22ad", + nVdash: "\u22ae", + nVDash: "\u22af", + nvge: "\u2265\u20d2", + nvgt: ">\u20d2", + nvHarr: "\u2904", + nvinfin: "\u29de", + nvlArr: "\u2902", + nvle: "\u2264\u20d2", + nvlt: "<\u20d2", + nvltrie: "\u22b4\u20d2", + nvrArr: "\u2903", + nvrtrie: "\u22b5\u20d2", + nvsim: "\u223c\u20d2", + nwarhk: "\u2923", + nwarr: "\u2196", + nwArr: "\u21d6", + nwarrow: "\u2196", + nwnear: "\u2927", + Oacute: "\xd3", + oacute: "\xf3", + oast: "\u229b", + Ocirc: "\xd4", + ocirc: "\xf4", + ocir: "\u229a", + Ocy: "\u041e", + ocy: "\u043e", + odash: "\u229d", + Odblac: "\u0150", + odblac: "\u0151", + odiv: "\u2a38", + odot: "\u2299", + odsold: "\u29bc", + OElig: "\u0152", + oelig: "\u0153", + ofcir: "\u29bf", + Ofr: "\ud835\udd12", + ofr: "\ud835\udd2c", + ogon: "\u02db", + Ograve: "\xd2", + ograve: "\xf2", + ogt: "\u29c1", + ohbar: "\u29b5", + ohm: "\u03a9", + oint: "\u222e", + olarr: "\u21ba", + olcir: "\u29be", + olcross: "\u29bb", + oline: "\u203e", + olt: "\u29c0", + Omacr: "\u014c", + omacr: "\u014d", + Omega: "\u03a9", + omega: "\u03c9", + Omicron: "\u039f", + omicron: "\u03bf", + omid: "\u29b6", + ominus: "\u2296", + Oopf: "\ud835\udd46", + oopf: "\ud835\udd60", + opar: "\u29b7", + OpenCurlyDoubleQuote: "\u201c", + OpenCurlyQuote: "\u2018", + operp: "\u29b9", + oplus: "\u2295", + orarr: "\u21bb", + Or: "\u2a54", + or: "\u2228", + ord: "\u2a5d", + order: "\u2134", + orderof: "\u2134", + ordf: "\xaa", + ordm: "\xba", + origof: "\u22b6", + oror: "\u2a56", + orslope: "\u2a57", + orv: "\u2a5b", + oS: "\u24c8", + Oscr: "\ud835\udcaa", + oscr: "\u2134", + Oslash: "\xd8", + oslash: "\xf8", + osol: "\u2298", + Otilde: "\xd5", + otilde: "\xf5", + otimesas: "\u2a36", + Otimes: "\u2a37", + otimes: "\u2297", + Ouml: "\xd6", + ouml: "\xf6", + ovbar: "\u233d", + OverBar: "\u203e", + OverBrace: "\u23de", + OverBracket: "\u23b4", + OverParenthesis: "\u23dc", + para: "\xb6", + parallel: "\u2225", + par: "\u2225", + parsim: "\u2af3", + parsl: "\u2afd", + part: "\u2202", + PartialD: "\u2202", + Pcy: "\u041f", + pcy: "\u043f", + percnt: "%", + period: ".", + permil: "\u2030", + perp: "\u22a5", + pertenk: "\u2031", + Pfr: "\ud835\udd13", + pfr: "\ud835\udd2d", + Phi: "\u03a6", + phi: "\u03c6", + phiv: "\u03d5", + phmmat: "\u2133", + phone: "\u260e", + Pi: "\u03a0", + pi: "\u03c0", + pitchfork: "\u22d4", + piv: "\u03d6", + planck: "\u210f", + planckh: "\u210e", + plankv: "\u210f", + plusacir: "\u2a23", + plusb: "\u229e", + pluscir: "\u2a22", + plus: "+", + plusdo: "\u2214", + plusdu: "\u2a25", + pluse: "\u2a72", + PlusMinus: "\xb1", + plusmn: "\xb1", + plussim: "\u2a26", + plustwo: "\u2a27", + pm: "\xb1", + Poincareplane: "\u210c", + pointint: "\u2a15", + popf: "\ud835\udd61", + Popf: "\u2119", + pound: "\xa3", + prap: "\u2ab7", + Pr: "\u2abb", + pr: "\u227a", + prcue: "\u227c", + precapprox: "\u2ab7", + prec: "\u227a", + preccurlyeq: "\u227c", + Precedes: "\u227a", + PrecedesEqual: "\u2aaf", + PrecedesSlantEqual: "\u227c", + PrecedesTilde: "\u227e", + preceq: "\u2aaf", + precnapprox: "\u2ab9", + precneqq: "\u2ab5", + precnsim: "\u22e8", + pre: "\u2aaf", + prE: "\u2ab3", + precsim: "\u227e", + prime: "\u2032", + Prime: "\u2033", + primes: "\u2119", + prnap: "\u2ab9", + prnE: "\u2ab5", + prnsim: "\u22e8", + prod: "\u220f", + Product: "\u220f", + profalar: "\u232e", + profline: "\u2312", + profsurf: "\u2313", + prop: "\u221d", + Proportional: "\u221d", + Proportion: "\u2237", + propto: "\u221d", + prsim: "\u227e", + prurel: "\u22b0", + Pscr: "\ud835\udcab", + pscr: "\ud835\udcc5", + Psi: "\u03a8", + psi: "\u03c8", + puncsp: "\u2008", + Qfr: "\ud835\udd14", + qfr: "\ud835\udd2e", + qint: "\u2a0c", + qopf: "\ud835\udd62", + Qopf: "\u211a", + qprime: "\u2057", + Qscr: "\ud835\udcac", + qscr: "\ud835\udcc6", + quaternions: "\u210d", + quatint: "\u2a16", + quest: "?", + questeq: "\u225f", + quot: '"', + QUOT: '"', + rAarr: "\u21db", + race: "\u223d\u0331", + Racute: "\u0154", + racute: "\u0155", + radic: "\u221a", + raemptyv: "\u29b3", + rang: "\u27e9", + Rang: "\u27eb", + rangd: "\u2992", + range: "\u29a5", + rangle: "\u27e9", + raquo: "\xbb", + rarrap: "\u2975", + rarrb: "\u21e5", + rarrbfs: "\u2920", + rarrc: "\u2933", + rarr: "\u2192", + Rarr: "\u21a0", + rArr: "\u21d2", + rarrfs: "\u291e", + rarrhk: "\u21aa", + rarrlp: "\u21ac", + rarrpl: "\u2945", + rarrsim: "\u2974", + Rarrtl: "\u2916", + rarrtl: "\u21a3", + rarrw: "\u219d", + ratail: "\u291a", + rAtail: "\u291c", + ratio: "\u2236", + rationals: "\u211a", + rbarr: "\u290d", + rBarr: "\u290f", + RBarr: "\u2910", + rbbrk: "\u2773", + rbrace: "}", + rbrack: "]", + rbrke: "\u298c", + rbrksld: "\u298e", + rbrkslu: "\u2990", + Rcaron: "\u0158", + rcaron: "\u0159", + Rcedil: "\u0156", + rcedil: "\u0157", + rceil: "\u2309", + rcub: "}", + Rcy: "\u0420", + rcy: "\u0440", + rdca: "\u2937", + rdldhar: "\u2969", + rdquo: "\u201d", + rdquor: "\u201d", + rdsh: "\u21b3", + real: "\u211c", + realine: "\u211b", + realpart: "\u211c", + reals: "\u211d", + Re: "\u211c", + rect: "\u25ad", + reg: "\xae", + REG: "\xae", + ReverseElement: "\u220b", + ReverseEquilibrium: "\u21cb", + ReverseUpEquilibrium: "\u296f", + rfisht: "\u297d", + rfloor: "\u230b", + rfr: "\ud835\udd2f", + Rfr: "\u211c", + rHar: "\u2964", + rhard: "\u21c1", + rharu: "\u21c0", + rharul: "\u296c", + Rho: "\u03a1", + rho: "\u03c1", + rhov: "\u03f1", + RightAngleBracket: "\u27e9", + RightArrowBar: "\u21e5", + rightarrow: "\u2192", + RightArrow: "\u2192", + Rightarrow: "\u21d2", + RightArrowLeftArrow: "\u21c4", + rightarrowtail: "\u21a3", + RightCeiling: "\u2309", + RightDoubleBracket: "\u27e7", + RightDownTeeVector: "\u295d", + RightDownVectorBar: "\u2955", + RightDownVector: "\u21c2", + RightFloor: "\u230b", + rightharpoondown: "\u21c1", + rightharpoonup: "\u21c0", + rightleftarrows: "\u21c4", + rightleftharpoons: "\u21cc", + rightrightarrows: "\u21c9", + rightsquigarrow: "\u219d", + RightTeeArrow: "\u21a6", + RightTee: "\u22a2", + RightTeeVector: "\u295b", + rightthreetimes: "\u22cc", + RightTriangleBar: "\u29d0", + RightTriangle: "\u22b3", + RightTriangleEqual: "\u22b5", + RightUpDownVector: "\u294f", + RightUpTeeVector: "\u295c", + RightUpVectorBar: "\u2954", + RightUpVector: "\u21be", + RightVectorBar: "\u2953", + RightVector: "\u21c0", + ring: "\u02da", + risingdotseq: "\u2253", + rlarr: "\u21c4", + rlhar: "\u21cc", + rlm: "\u200f", + rmoustache: "\u23b1", + rmoust: "\u23b1", + rnmid: "\u2aee", + roang: "\u27ed", + roarr: "\u21fe", + robrk: "\u27e7", + ropar: "\u2986", + ropf: "\ud835\udd63", + Ropf: "\u211d", + roplus: "\u2a2e", + rotimes: "\u2a35", + RoundImplies: "\u2970", + rpar: ")", + rpargt: "\u2994", + rppolint: "\u2a12", + rrarr: "\u21c9", + Rrightarrow: "\u21db", + rsaquo: "\u203a", + rscr: "\ud835\udcc7", + Rscr: "\u211b", + rsh: "\u21b1", + Rsh: "\u21b1", + rsqb: "]", + rsquo: "\u2019", + rsquor: "\u2019", + rthree: "\u22cc", + rtimes: "\u22ca", + rtri: "\u25b9", + rtrie: "\u22b5", + rtrif: "\u25b8", + rtriltri: "\u29ce", + RuleDelayed: "\u29f4", + ruluhar: "\u2968", + rx: "\u211e", + Sacute: "\u015a", + sacute: "\u015b", + sbquo: "\u201a", + scap: "\u2ab8", + Scaron: "\u0160", + scaron: "\u0161", + Sc: "\u2abc", + sc: "\u227b", + sccue: "\u227d", + sce: "\u2ab0", + scE: "\u2ab4", + Scedil: "\u015e", + scedil: "\u015f", + Scirc: "\u015c", + scirc: "\u015d", + scnap: "\u2aba", + scnE: "\u2ab6", + scnsim: "\u22e9", + scpolint: "\u2a13", + scsim: "\u227f", + Scy: "\u0421", + scy: "\u0441", + sdotb: "\u22a1", + sdot: "\u22c5", + sdote: "\u2a66", + searhk: "\u2925", + searr: "\u2198", + seArr: "\u21d8", + searrow: "\u2198", + sect: "\xa7", + semi: ";", + seswar: "\u2929", + setminus: "\u2216", + setmn: "\u2216", + sext: "\u2736", + Sfr: "\ud835\udd16", + sfr: "\ud835\udd30", + sfrown: "\u2322", + sharp: "\u266f", + SHCHcy: "\u0429", + shchcy: "\u0449", + SHcy: "\u0428", + shcy: "\u0448", + ShortDownArrow: "\u2193", + ShortLeftArrow: "\u2190", + shortmid: "\u2223", + shortparallel: "\u2225", + ShortRightArrow: "\u2192", + ShortUpArrow: "\u2191", + shy: "\xad", + Sigma: "\u03a3", + sigma: "\u03c3", + sigmaf: "\u03c2", + sigmav: "\u03c2", + sim: "\u223c", + simdot: "\u2a6a", + sime: "\u2243", + simeq: "\u2243", + simg: "\u2a9e", + simgE: "\u2aa0", + siml: "\u2a9d", + simlE: "\u2a9f", + simne: "\u2246", + simplus: "\u2a24", + simrarr: "\u2972", + slarr: "\u2190", + SmallCircle: "\u2218", + smallsetminus: "\u2216", + smashp: "\u2a33", + smeparsl: "\u29e4", + smid: "\u2223", + smile: "\u2323", + smt: "\u2aaa", + smte: "\u2aac", + smtes: "\u2aac\ufe00", + SOFTcy: "\u042c", + softcy: "\u044c", + solbar: "\u233f", + solb: "\u29c4", + sol: "/", + Sopf: "\ud835\udd4a", + sopf: "\ud835\udd64", + spades: "\u2660", + spadesuit: "\u2660", + spar: "\u2225", + sqcap: "\u2293", + sqcaps: "\u2293\ufe00", + sqcup: "\u2294", + sqcups: "\u2294\ufe00", + Sqrt: "\u221a", + sqsub: "\u228f", + sqsube: "\u2291", + sqsubset: "\u228f", + sqsubseteq: "\u2291", + sqsup: "\u2290", + sqsupe: "\u2292", + sqsupset: "\u2290", + sqsupseteq: "\u2292", + square: "\u25a1", + Square: "\u25a1", + SquareIntersection: "\u2293", + SquareSubset: "\u228f", + SquareSubsetEqual: "\u2291", + SquareSuperset: "\u2290", + SquareSupersetEqual: "\u2292", + SquareUnion: "\u2294", + squarf: "\u25aa", + squ: "\u25a1", + squf: "\u25aa", + srarr: "\u2192", + Sscr: "\ud835\udcae", + sscr: "\ud835\udcc8", + ssetmn: "\u2216", + ssmile: "\u2323", + sstarf: "\u22c6", + Star: "\u22c6", + star: "\u2606", + starf: "\u2605", + straightepsilon: "\u03f5", + straightphi: "\u03d5", + strns: "\xaf", + sub: "\u2282", + Sub: "\u22d0", + subdot: "\u2abd", + subE: "\u2ac5", + sube: "\u2286", + subedot: "\u2ac3", + submult: "\u2ac1", + subnE: "\u2acb", + subne: "\u228a", + subplus: "\u2abf", + subrarr: "\u2979", + subset: "\u2282", + Subset: "\u22d0", + subseteq: "\u2286", + subseteqq: "\u2ac5", + SubsetEqual: "\u2286", + subsetneq: "\u228a", + subsetneqq: "\u2acb", + subsim: "\u2ac7", + subsub: "\u2ad5", + subsup: "\u2ad3", + succapprox: "\u2ab8", + succ: "\u227b", + succcurlyeq: "\u227d", + Succeeds: "\u227b", + SucceedsEqual: "\u2ab0", + SucceedsSlantEqual: "\u227d", + SucceedsTilde: "\u227f", + succeq: "\u2ab0", + succnapprox: "\u2aba", + succneqq: "\u2ab6", + succnsim: "\u22e9", + succsim: "\u227f", + SuchThat: "\u220b", + sum: "\u2211", + Sum: "\u2211", + sung: "\u266a", + sup1: "\xb9", + sup2: "\xb2", + sup3: "\xb3", + sup: "\u2283", + Sup: "\u22d1", + supdot: "\u2abe", + supdsub: "\u2ad8", + supE: "\u2ac6", + supe: "\u2287", + supedot: "\u2ac4", + Superset: "\u2283", + SupersetEqual: "\u2287", + suphsol: "\u27c9", + suphsub: "\u2ad7", + suplarr: "\u297b", + supmult: "\u2ac2", + supnE: "\u2acc", + supne: "\u228b", + supplus: "\u2ac0", + supset: "\u2283", + Supset: "\u22d1", + supseteq: "\u2287", + supseteqq: "\u2ac6", + supsetneq: "\u228b", + supsetneqq: "\u2acc", + supsim: "\u2ac8", + supsub: "\u2ad4", + supsup: "\u2ad6", + swarhk: "\u2926", + swarr: "\u2199", + swArr: "\u21d9", + swarrow: "\u2199", + swnwar: "\u292a", + szlig: "\xdf", + Tab: "\t", + target: "\u2316", + Tau: "\u03a4", + tau: "\u03c4", + tbrk: "\u23b4", + Tcaron: "\u0164", + tcaron: "\u0165", + Tcedil: "\u0162", + tcedil: "\u0163", + Tcy: "\u0422", + tcy: "\u0442", + tdot: "\u20db", + telrec: "\u2315", + Tfr: "\ud835\udd17", + tfr: "\ud835\udd31", + there4: "\u2234", + therefore: "\u2234", + Therefore: "\u2234", + Theta: "\u0398", + theta: "\u03b8", + thetasym: "\u03d1", + thetav: "\u03d1", + thickapprox: "\u2248", + thicksim: "\u223c", + ThickSpace: "\u205f\u200a", + ThinSpace: "\u2009", + thinsp: "\u2009", + thkap: "\u2248", + thksim: "\u223c", + THORN: "\xde", + thorn: "\xfe", + tilde: "\u02dc", + Tilde: "\u223c", + TildeEqual: "\u2243", + TildeFullEqual: "\u2245", + TildeTilde: "\u2248", + timesbar: "\u2a31", + timesb: "\u22a0", + times: "\xd7", + timesd: "\u2a30", + tint: "\u222d", + toea: "\u2928", + topbot: "\u2336", + topcir: "\u2af1", + top: "\u22a4", + Topf: "\ud835\udd4b", + topf: "\ud835\udd65", + topfork: "\u2ada", + tosa: "\u2929", + tprime: "\u2034", + trade: "\u2122", + TRADE: "\u2122", + triangle: "\u25b5", + triangledown: "\u25bf", + triangleleft: "\u25c3", + trianglelefteq: "\u22b4", + triangleq: "\u225c", + triangleright: "\u25b9", + trianglerighteq: "\u22b5", + tridot: "\u25ec", + trie: "\u225c", + triminus: "\u2a3a", + TripleDot: "\u20db", + triplus: "\u2a39", + trisb: "\u29cd", + tritime: "\u2a3b", + trpezium: "\u23e2", + Tscr: "\ud835\udcaf", + tscr: "\ud835\udcc9", + TScy: "\u0426", + tscy: "\u0446", + TSHcy: "\u040b", + tshcy: "\u045b", + Tstrok: "\u0166", + tstrok: "\u0167", + twixt: "\u226c", + twoheadleftarrow: "\u219e", + twoheadrightarrow: "\u21a0", + Uacute: "\xda", + uacute: "\xfa", + uarr: "\u2191", + Uarr: "\u219f", + uArr: "\u21d1", + Uarrocir: "\u2949", + Ubrcy: "\u040e", + ubrcy: "\u045e", + Ubreve: "\u016c", + ubreve: "\u016d", + Ucirc: "\xdb", + ucirc: "\xfb", + Ucy: "\u0423", + ucy: "\u0443", + udarr: "\u21c5", + Udblac: "\u0170", + udblac: "\u0171", + udhar: "\u296e", + ufisht: "\u297e", + Ufr: "\ud835\udd18", + ufr: "\ud835\udd32", + Ugrave: "\xd9", + ugrave: "\xf9", + uHar: "\u2963", + uharl: "\u21bf", + uharr: "\u21be", + uhblk: "\u2580", + ulcorn: "\u231c", + ulcorner: "\u231c", + ulcrop: "\u230f", + ultri: "\u25f8", + Umacr: "\u016a", + umacr: "\u016b", + uml: "\xa8", + UnderBar: "_", + UnderBrace: "\u23df", + UnderBracket: "\u23b5", + UnderParenthesis: "\u23dd", + Union: "\u22c3", + UnionPlus: "\u228e", + Uogon: "\u0172", + uogon: "\u0173", + Uopf: "\ud835\udd4c", + uopf: "\ud835\udd66", + UpArrowBar: "\u2912", + uparrow: "\u2191", + UpArrow: "\u2191", + Uparrow: "\u21d1", + UpArrowDownArrow: "\u21c5", + updownarrow: "\u2195", + UpDownArrow: "\u2195", + Updownarrow: "\u21d5", + UpEquilibrium: "\u296e", + upharpoonleft: "\u21bf", + upharpoonright: "\u21be", + uplus: "\u228e", + UpperLeftArrow: "\u2196", + UpperRightArrow: "\u2197", + upsi: "\u03c5", + Upsi: "\u03d2", + upsih: "\u03d2", + Upsilon: "\u03a5", + upsilon: "\u03c5", + UpTeeArrow: "\u21a5", + UpTee: "\u22a5", + upuparrows: "\u21c8", + urcorn: "\u231d", + urcorner: "\u231d", + urcrop: "\u230e", + Uring: "\u016e", + uring: "\u016f", + urtri: "\u25f9", + Uscr: "\ud835\udcb0", + uscr: "\ud835\udcca", + utdot: "\u22f0", + Utilde: "\u0168", + utilde: "\u0169", + utri: "\u25b5", + utrif: "\u25b4", + uuarr: "\u21c8", + Uuml: "\xdc", + uuml: "\xfc", + uwangle: "\u29a7", + vangrt: "\u299c", + varepsilon: "\u03f5", + varkappa: "\u03f0", + varnothing: "\u2205", + varphi: "\u03d5", + varpi: "\u03d6", + varpropto: "\u221d", + varr: "\u2195", + vArr: "\u21d5", + varrho: "\u03f1", + varsigma: "\u03c2", + varsubsetneq: "\u228a\ufe00", + varsubsetneqq: "\u2acb\ufe00", + varsupsetneq: "\u228b\ufe00", + varsupsetneqq: "\u2acc\ufe00", + vartheta: "\u03d1", + vartriangleleft: "\u22b2", + vartriangleright: "\u22b3", + vBar: "\u2ae8", + Vbar: "\u2aeb", + vBarv: "\u2ae9", + Vcy: "\u0412", + vcy: "\u0432", + vdash: "\u22a2", + vDash: "\u22a8", + Vdash: "\u22a9", + VDash: "\u22ab", + Vdashl: "\u2ae6", + veebar: "\u22bb", + vee: "\u2228", + Vee: "\u22c1", + veeeq: "\u225a", + vellip: "\u22ee", + verbar: "|", + Verbar: "\u2016", + vert: "|", + Vert: "\u2016", + VerticalBar: "\u2223", + VerticalLine: "|", + VerticalSeparator: "\u2758", + VerticalTilde: "\u2240", + VeryThinSpace: "\u200a", + Vfr: "\ud835\udd19", + vfr: "\ud835\udd33", + vltri: "\u22b2", + vnsub: "\u2282\u20d2", + vnsup: "\u2283\u20d2", + Vopf: "\ud835\udd4d", + vopf: "\ud835\udd67", + vprop: "\u221d", + vrtri: "\u22b3", + Vscr: "\ud835\udcb1", + vscr: "\ud835\udccb", + vsubnE: "\u2acb\ufe00", + vsubne: "\u228a\ufe00", + vsupnE: "\u2acc\ufe00", + vsupne: "\u228b\ufe00", + Vvdash: "\u22aa", + vzigzag: "\u299a", + Wcirc: "\u0174", + wcirc: "\u0175", + wedbar: "\u2a5f", + wedge: "\u2227", + Wedge: "\u22c0", + wedgeq: "\u2259", + weierp: "\u2118", + Wfr: "\ud835\udd1a", + wfr: "\ud835\udd34", + Wopf: "\ud835\udd4e", + wopf: "\ud835\udd68", + wp: "\u2118", + wr: "\u2240", + wreath: "\u2240", + Wscr: "\ud835\udcb2", + wscr: "\ud835\udccc", + xcap: "\u22c2", + xcirc: "\u25ef", + xcup: "\u22c3", + xdtri: "\u25bd", + Xfr: "\ud835\udd1b", + xfr: "\ud835\udd35", + xharr: "\u27f7", + xhArr: "\u27fa", + Xi: "\u039e", + xi: "\u03be", + xlarr: "\u27f5", + xlArr: "\u27f8", + xmap: "\u27fc", + xnis: "\u22fb", + xodot: "\u2a00", + Xopf: "\ud835\udd4f", + xopf: "\ud835\udd69", + xoplus: "\u2a01", + xotime: "\u2a02", + xrarr: "\u27f6", + xrArr: "\u27f9", + Xscr: "\ud835\udcb3", + xscr: "\ud835\udccd", + xsqcup: "\u2a06", + xuplus: "\u2a04", + xutri: "\u25b3", + xvee: "\u22c1", + xwedge: "\u22c0", + Yacute: "\xdd", + yacute: "\xfd", + YAcy: "\u042f", + yacy: "\u044f", + Ycirc: "\u0176", + ycirc: "\u0177", + Ycy: "\u042b", + ycy: "\u044b", + yen: "\xa5", + Yfr: "\ud835\udd1c", + yfr: "\ud835\udd36", + YIcy: "\u0407", + yicy: "\u0457", + Yopf: "\ud835\udd50", + yopf: "\ud835\udd6a", + Yscr: "\ud835\udcb4", + yscr: "\ud835\udcce", + YUcy: "\u042e", + yucy: "\u044e", + yuml: "\xff", + Yuml: "\u0178", + Zacute: "\u0179", + zacute: "\u017a", + Zcaron: "\u017d", + zcaron: "\u017e", + Zcy: "\u0417", + zcy: "\u0437", + Zdot: "\u017b", + zdot: "\u017c", + zeetrf: "\u2128", + ZeroWidthSpace: "\u200b", + Zeta: "\u0396", + zeta: "\u03b6", + zfr: "\ud835\udd37", + Zfr: "\u2128", + ZHcy: "\u0416", + zhcy: "\u0436", + zigrarr: "\u21dd", + zopf: "\ud835\udd6b", + Zopf: "\u2124", + Zscr: "\ud835\udcb5", + zscr: "\ud835\udccf", + zwj: "\u200d", + zwnj: "\u200c" + }; + /*eslint quotes:0*/ var entities = require$$0; + var regex$4 = /[!-#%-\*,-\/:;\?@\[-\]_\{\}\xA1\xA7\xAB\xB6\xB7\xBB\xBF\u037E\u0387\u055A-\u055F\u0589\u058A\u05BE\u05C0\u05C3\u05C6\u05F3\u05F4\u0609\u060A\u060C\u060D\u061B\u061E\u061F\u066A-\u066D\u06D4\u0700-\u070D\u07F7-\u07F9\u0830-\u083E\u085E\u0964\u0965\u0970\u09FD\u0A76\u0AF0\u0C84\u0DF4\u0E4F\u0E5A\u0E5B\u0F04-\u0F12\u0F14\u0F3A-\u0F3D\u0F85\u0FD0-\u0FD4\u0FD9\u0FDA\u104A-\u104F\u10FB\u1360-\u1368\u1400\u166D\u166E\u169B\u169C\u16EB-\u16ED\u1735\u1736\u17D4-\u17D6\u17D8-\u17DA\u1800-\u180A\u1944\u1945\u1A1E\u1A1F\u1AA0-\u1AA6\u1AA8-\u1AAD\u1B5A-\u1B60\u1BFC-\u1BFF\u1C3B-\u1C3F\u1C7E\u1C7F\u1CC0-\u1CC7\u1CD3\u2010-\u2027\u2030-\u2043\u2045-\u2051\u2053-\u205E\u207D\u207E\u208D\u208E\u2308-\u230B\u2329\u232A\u2768-\u2775\u27C5\u27C6\u27E6-\u27EF\u2983-\u2998\u29D8-\u29DB\u29FC\u29FD\u2CF9-\u2CFC\u2CFE\u2CFF\u2D70\u2E00-\u2E2E\u2E30-\u2E4E\u3001-\u3003\u3008-\u3011\u3014-\u301F\u3030\u303D\u30A0\u30FB\uA4FE\uA4FF\uA60D-\uA60F\uA673\uA67E\uA6F2-\uA6F7\uA874-\uA877\uA8CE\uA8CF\uA8F8-\uA8FA\uA8FC\uA92E\uA92F\uA95F\uA9C1-\uA9CD\uA9DE\uA9DF\uAA5C-\uAA5F\uAADE\uAADF\uAAF0\uAAF1\uABEB\uFD3E\uFD3F\uFE10-\uFE19\uFE30-\uFE52\uFE54-\uFE61\uFE63\uFE68\uFE6A\uFE6B\uFF01-\uFF03\uFF05-\uFF0A\uFF0C-\uFF0F\uFF1A\uFF1B\uFF1F\uFF20\uFF3B-\uFF3D\uFF3F\uFF5B\uFF5D\uFF5F-\uFF65]|\uD800[\uDD00-\uDD02\uDF9F\uDFD0]|\uD801\uDD6F|\uD802[\uDC57\uDD1F\uDD3F\uDE50-\uDE58\uDE7F\uDEF0-\uDEF6\uDF39-\uDF3F\uDF99-\uDF9C]|\uD803[\uDF55-\uDF59]|\uD804[\uDC47-\uDC4D\uDCBB\uDCBC\uDCBE-\uDCC1\uDD40-\uDD43\uDD74\uDD75\uDDC5-\uDDC8\uDDCD\uDDDB\uDDDD-\uDDDF\uDE38-\uDE3D\uDEA9]|\uD805[\uDC4B-\uDC4F\uDC5B\uDC5D\uDCC6\uDDC1-\uDDD7\uDE41-\uDE43\uDE60-\uDE6C\uDF3C-\uDF3E]|\uD806[\uDC3B\uDE3F-\uDE46\uDE9A-\uDE9C\uDE9E-\uDEA2]|\uD807[\uDC41-\uDC45\uDC70\uDC71\uDEF7\uDEF8]|\uD809[\uDC70-\uDC74]|\uD81A[\uDE6E\uDE6F\uDEF5\uDF37-\uDF3B\uDF44]|\uD81B[\uDE97-\uDE9A]|\uD82F\uDC9F|\uD836[\uDE87-\uDE8B]|\uD83A[\uDD5E\uDD5F]/; + var encodeCache = {}; + // Create a lookup array where anything but characters in `chars` string + // and alphanumeric chars is percent-encoded. + + function getEncodeCache(exclude) { + var i, ch, cache = encodeCache[exclude]; + if (cache) { + return cache; + } + cache = encodeCache[exclude] = []; + for (i = 0; i < 128; i++) { + ch = String.fromCharCode(i); + if (/^[0-9a-z]$/i.test(ch)) { + // always allow unencoded alphanumeric characters + cache.push(ch); + } else { + cache.push("%" + ("0" + i.toString(16).toUpperCase()).slice(-2)); + } + } + for (i = 0; i < exclude.length; i++) { + cache[exclude.charCodeAt(i)] = exclude[i]; + } + return cache; + } + // Encode unsafe characters with percent-encoding, skipping already + // encoded sequences. + + // - string - string to encode + // - exclude - list of characters to ignore (in addition to a-zA-Z0-9) + // - keepEscaped - don't encode '%' in a correct escape sequence (default: true) + + function encode$2(string, exclude, keepEscaped) { + var i, l, code, nextCode, cache, result = ""; + if (typeof exclude !== "string") { + // encode(string, keepEscaped) + keepEscaped = exclude; + exclude = encode$2.defaultChars; + } + if (typeof keepEscaped === "undefined") { + keepEscaped = true; + } + cache = getEncodeCache(exclude); + for (i = 0, l = string.length; i < l; i++) { + code = string.charCodeAt(i); + if (keepEscaped && code === 37 /* % */ && i + 2 < l) { + if (/^[0-9a-f]{2}$/i.test(string.slice(i + 1, i + 3))) { + result += string.slice(i, i + 3); + i += 2; + continue; + } + } + if (code < 128) { + result += cache[code]; + continue; + } + if (code >= 55296 && code <= 57343) { + if (code >= 55296 && code <= 56319 && i + 1 < l) { + nextCode = string.charCodeAt(i + 1); + if (nextCode >= 56320 && nextCode <= 57343) { + result += encodeURIComponent(string[i] + string[i + 1]); + i++; + continue; + } + } + result += "%EF%BF%BD"; + continue; + } + result += encodeURIComponent(string[i]); + } + return result; + } + encode$2.defaultChars = ";/?:@&=+$,-_.!~*'()#"; + encode$2.componentChars = "-_.!~*'()"; + var encode_1 = encode$2; + /* eslint-disable no-bitwise */ var decodeCache = {}; + function getDecodeCache(exclude) { + var i, ch, cache = decodeCache[exclude]; + if (cache) { + return cache; + } + cache = decodeCache[exclude] = []; + for (i = 0; i < 128; i++) { + ch = String.fromCharCode(i); + cache.push(ch); + } + for (i = 0; i < exclude.length; i++) { + ch = exclude.charCodeAt(i); + cache[ch] = "%" + ("0" + ch.toString(16).toUpperCase()).slice(-2); + } + return cache; + } + // Decode percent-encoded string. + + function decode$2(string, exclude) { + var cache; + if (typeof exclude !== "string") { + exclude = decode$2.defaultChars; + } + cache = getDecodeCache(exclude); + return string.replace(/(%[a-f0-9]{2})+/gi, (function(seq) { + var i, l, b1, b2, b3, b4, chr, result = ""; + for (i = 0, l = seq.length; i < l; i += 3) { + b1 = parseInt(seq.slice(i + 1, i + 3), 16); + if (b1 < 128) { + result += cache[b1]; + continue; + } + if ((b1 & 224) === 192 && i + 3 < l) { + // 110xxxxx 10xxxxxx + b2 = parseInt(seq.slice(i + 4, i + 6), 16); + if ((b2 & 192) === 128) { + chr = b1 << 6 & 1984 | b2 & 63; + if (chr < 128) { + result += "\ufffd\ufffd"; + } else { + result += String.fromCharCode(chr); + } + i += 3; + continue; + } + } + if ((b1 & 240) === 224 && i + 6 < l) { + // 1110xxxx 10xxxxxx 10xxxxxx + b2 = parseInt(seq.slice(i + 4, i + 6), 16); + b3 = parseInt(seq.slice(i + 7, i + 9), 16); + if ((b2 & 192) === 128 && (b3 & 192) === 128) { + chr = b1 << 12 & 61440 | b2 << 6 & 4032 | b3 & 63; + if (chr < 2048 || chr >= 55296 && chr <= 57343) { + result += "\ufffd\ufffd\ufffd"; + } else { + result += String.fromCharCode(chr); + } + i += 6; + continue; + } + } + if ((b1 & 248) === 240 && i + 9 < l) { + // 111110xx 10xxxxxx 10xxxxxx 10xxxxxx + b2 = parseInt(seq.slice(i + 4, i + 6), 16); + b3 = parseInt(seq.slice(i + 7, i + 9), 16); + b4 = parseInt(seq.slice(i + 10, i + 12), 16); + if ((b2 & 192) === 128 && (b3 & 192) === 128 && (b4 & 192) === 128) { + chr = b1 << 18 & 1835008 | b2 << 12 & 258048 | b3 << 6 & 4032 | b4 & 63; + if (chr < 65536 || chr > 1114111) { + result += "\ufffd\ufffd\ufffd\ufffd"; + } else { + chr -= 65536; + result += String.fromCharCode(55296 + (chr >> 10), 56320 + (chr & 1023)); + } + i += 9; + continue; + } + } + result += "\ufffd"; + } + return result; + })); + } + decode$2.defaultChars = ";/?:@&=+$,#"; + decode$2.componentChars = ""; + var decode_1 = decode$2; + var format$1 = function format(url) { + var result = ""; + result += url.protocol || ""; + result += url.slashes ? "//" : ""; + result += url.auth ? url.auth + "@" : ""; + if (url.hostname && url.hostname.indexOf(":") !== -1) { + // ipv6 address + result += "[" + url.hostname + "]"; + } else { + result += url.hostname || ""; + } + result += url.port ? ":" + url.port : ""; + result += url.pathname || ""; + result += url.search || ""; + result += url.hash || ""; + return result; + }; + // Copyright Joyent, Inc. and other Node contributors. + + // Changes from joyent/node: + + // 1. No leading slash in paths, + // e.g. in `url.parse('http://foo?bar')` pathname is ``, not `/` + + // 2. Backslashes are not replaced with slashes, + // so `http:\\example.org\` is treated like a relative path + + // 3. Trailing colon is treated like a part of the path, + // i.e. in `http://example.org:foo` pathname is `:foo` + + // 4. Nothing is URL-encoded in the resulting object, + // (in joyent/node some chars in auth and paths are encoded) + + // 5. `url.parse()` does not have `parseQueryString` argument + + // 6. Removed extraneous result properties: `host`, `path`, `query`, etc., + // which can be constructed using other parts of the url. + + function Url() { + this.protocol = null; + this.slashes = null; + this.auth = null; + this.port = null; + this.hostname = null; + this.hash = null; + this.search = null; + this.pathname = null; + } + // Reference: RFC 3986, RFC 1808, RFC 2396 + // define these here so at least they only have to be + // compiled once on the first module load. + var protocolPattern = /^([a-z0-9.+-]+:)/i, portPattern = /:[0-9]*$/, + // Special case for a simple path URL + simplePathPattern = /^(\/\/?(?!\/)[^\?\s]*)(\?[^\s]*)?$/, + // RFC 2396: characters reserved for delimiting URLs. + // We actually just auto-escape these. + delims = [ "<", ">", '"', "`", " ", "\r", "\n", "\t" ], + // RFC 2396: characters not allowed for various reasons. + unwise = [ "{", "}", "|", "\\", "^", "`" ].concat(delims), + // Allowed by RFCs, but cause of XSS attacks. Always escape these. + autoEscape = [ "'" ].concat(unwise), + // Characters that are never ever allowed in a hostname. + // Note that any invalid chars are also handled, but these + // are the ones that are *expected* to be seen, so we fast-path + // them. + nonHostChars = [ "%", "/", "?", ";", "#" ].concat(autoEscape), hostEndingChars = [ "/", "?", "#" ], hostnameMaxLen = 255, hostnamePartPattern = /^[+a-z0-9A-Z_-]{0,63}$/, hostnamePartStart = /^([+a-z0-9A-Z_-]{0,63})(.*)$/, + // protocols that can allow "unsafe" and "unwise" chars. + /* eslint-disable no-script-url */ + // protocols that never have a hostname. + hostlessProtocol = { + javascript: true, + "javascript:": true + }, + // protocols that always contain a // bit. + slashedProtocol = { + http: true, + https: true, + ftp: true, + gopher: true, + file: true, + "http:": true, + "https:": true, + "ftp:": true, + "gopher:": true, + "file:": true + }; + /* eslint-enable no-script-url */ function urlParse(url, slashesDenoteHost) { + if (url && url instanceof Url) { + return url; + } + var u = new Url; + u.parse(url, slashesDenoteHost); + return u; + } + Url.prototype.parse = function(url, slashesDenoteHost) { + var i, l, lowerProto, hec, slashes, rest = url; + // trim before proceeding. + // This is to support parse stuff like " http://foo.com \n" + rest = rest.trim(); + if (!slashesDenoteHost && url.split("#").length === 1) { + // Try fast path regexp + var simplePath = simplePathPattern.exec(rest); + if (simplePath) { + this.pathname = simplePath[1]; + if (simplePath[2]) { + this.search = simplePath[2]; + } + return this; + } + } + var proto = protocolPattern.exec(rest); + if (proto) { + proto = proto[0]; + lowerProto = proto.toLowerCase(); + this.protocol = proto; + rest = rest.substr(proto.length); + } + // figure out if it's got a host + // user@server is *always* interpreted as a hostname, and url + // resolution will treat //foo/bar as host=foo,path=bar because that's + // how the browser resolves relative URLs. + if (slashesDenoteHost || proto || rest.match(/^\/\/[^@\/]+@[^@\/]+/)) { + slashes = rest.substr(0, 2) === "//"; + if (slashes && !(proto && hostlessProtocol[proto])) { + rest = rest.substr(2); + this.slashes = true; + } + } + if (!hostlessProtocol[proto] && (slashes || proto && !slashedProtocol[proto])) { + // there's a hostname. + // the first instance of /, ?, ;, or # ends the host. + // If there is an @ in the hostname, then non-host chars *are* allowed + // to the left of the last @ sign, unless some host-ending character + // comes *before* the @-sign. + // URLs are obnoxious. + // ex: + // http://a@b@c/ => user:a@b host:c + // http://a@b?@c => user:a host:c path:/?@c + // v0.12 TODO(isaacs): This is not quite how Chrome does things. + // Review our test case against browsers more comprehensively. + // find the first instance of any hostEndingChars + var hostEnd = -1; + for (i = 0; i < hostEndingChars.length; i++) { + hec = rest.indexOf(hostEndingChars[i]); + if (hec !== -1 && (hostEnd === -1 || hec < hostEnd)) { + hostEnd = hec; + } + } + // at this point, either we have an explicit point where the + // auth portion cannot go past, or the last @ char is the decider. + var auth, atSign; + if (hostEnd === -1) { + // atSign can be anywhere. + atSign = rest.lastIndexOf("@"); + } else { + // atSign must be in auth portion. + // http://a@b/c@d => host:b auth:a path:/c@d + atSign = rest.lastIndexOf("@", hostEnd); + } + // Now we have a portion which is definitely the auth. + // Pull that off. + if (atSign !== -1) { + auth = rest.slice(0, atSign); + rest = rest.slice(atSign + 1); + this.auth = auth; + } + // the host is the remaining to the left of the first non-host char + hostEnd = -1; + for (i = 0; i < nonHostChars.length; i++) { + hec = rest.indexOf(nonHostChars[i]); + if (hec !== -1 && (hostEnd === -1 || hec < hostEnd)) { + hostEnd = hec; + } + } + // if we still have not hit it, then the entire thing is a host. + if (hostEnd === -1) { + hostEnd = rest.length; + } + if (rest[hostEnd - 1] === ":") { + hostEnd--; + } + var host = rest.slice(0, hostEnd); + rest = rest.slice(hostEnd); + // pull out port. + this.parseHost(host); + // we've indicated that there is a hostname, + // so even if it's empty, it has to be present. + this.hostname = this.hostname || ""; + // if hostname begins with [ and ends with ] + // assume that it's an IPv6 address. + var ipv6Hostname = this.hostname[0] === "[" && this.hostname[this.hostname.length - 1] === "]"; + // validate a little. + if (!ipv6Hostname) { + var hostparts = this.hostname.split(/\./); + for (i = 0, l = hostparts.length; i < l; i++) { + var part = hostparts[i]; + if (!part) { + continue; + } + if (!part.match(hostnamePartPattern)) { + var newpart = ""; + for (var j = 0, k = part.length; j < k; j++) { + if (part.charCodeAt(j) > 127) { + // we replace non-ASCII char with a temporary placeholder + // we need this to make sure size of hostname is not + // broken by replacing non-ASCII by nothing + newpart += "x"; + } else { + newpart += part[j]; + } + } + // we test again with ASCII char only + if (!newpart.match(hostnamePartPattern)) { + var validParts = hostparts.slice(0, i); + var notHost = hostparts.slice(i + 1); + var bit = part.match(hostnamePartStart); + if (bit) { + validParts.push(bit[1]); + notHost.unshift(bit[2]); + } + if (notHost.length) { + rest = notHost.join(".") + rest; + } + this.hostname = validParts.join("."); + break; + } + } + } + } + if (this.hostname.length > hostnameMaxLen) { + this.hostname = ""; + } + // strip [ and ] from the hostname + // the host field still retains them, though + if (ipv6Hostname) { + this.hostname = this.hostname.substr(1, this.hostname.length - 2); + } + } + // chop off from the tail first. + var hash = rest.indexOf("#"); + if (hash !== -1) { + // got a fragment string. + this.hash = rest.substr(hash); + rest = rest.slice(0, hash); + } + var qm = rest.indexOf("?"); + if (qm !== -1) { + this.search = rest.substr(qm); + rest = rest.slice(0, qm); + } + if (rest) { + this.pathname = rest; + } + if (slashedProtocol[lowerProto] && this.hostname && !this.pathname) { + this.pathname = ""; + } + return this; + }; + Url.prototype.parseHost = function(host) { + var port = portPattern.exec(host); + if (port) { + port = port[0]; + if (port !== ":") { + this.port = port.substr(1); + } + host = host.substr(0, host.length - port.length); + } + if (host) { + this.hostname = host; + } + }; + var parse$1 = urlParse; + var encode$1 = encode_1; + var decode$1 = decode_1; + var format = format$1; + var parse = parse$1; + var mdurl = { + encode: encode$1, + decode: decode$1, + format: format, + parse: parse + }; + var regex$3 = /[\0-\uD7FF\uE000-\uFFFF]|[\uD800-\uDBFF][\uDC00-\uDFFF]|[\uD800-\uDBFF](?![\uDC00-\uDFFF])|(?:[^\uD800-\uDBFF]|^)[\uDC00-\uDFFF]/; + var regex$2 = /[\0-\x1F\x7F-\x9F]/; + var regex$1 = /[\xAD\u0600-\u0605\u061C\u06DD\u070F\u08E2\u180E\u200B-\u200F\u202A-\u202E\u2060-\u2064\u2066-\u206F\uFEFF\uFFF9-\uFFFB]|\uD804[\uDCBD\uDCCD]|\uD82F[\uDCA0-\uDCA3]|\uD834[\uDD73-\uDD7A]|\uDB40[\uDC01\uDC20-\uDC7F]/; + var regex = /[ \xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/; + var Any = regex$3; + var Cc = regex$2; + var Cf = regex$1; + var P = regex$4; + var Z = regex; + var uc_micro = { + Any: Any, + Cc: Cc, + Cf: Cf, + P: P, + Z: Z + }; + var utils = createCommonjsModule((function(module, exports) { + function _class(obj) { + return Object.prototype.toString.call(obj); + } + function isString(obj) { + return _class(obj) === "[object String]"; + } + var _hasOwnProperty = Object.prototype.hasOwnProperty; + function has(object, key) { + return _hasOwnProperty.call(object, key); + } + // Merge objects + + function assign(obj /*from1, from2, from3, ...*/) { + var sources = Array.prototype.slice.call(arguments, 1); + sources.forEach((function(source) { + if (!source) { + return; + } + if (typeof source !== "object") { + throw new TypeError(source + "must be object"); + } + Object.keys(source).forEach((function(key) { + obj[key] = source[key]; + })); + })); + return obj; + } + // Remove element from array and put another array at those position. + // Useful for some operations with tokens + function arrayReplaceAt(src, pos, newElements) { + return [].concat(src.slice(0, pos), newElements, src.slice(pos + 1)); + } + //////////////////////////////////////////////////////////////////////////////// + function isValidEntityCode(c) { + /*eslint no-bitwise:0*/ + // broken sequence + if (c >= 55296 && c <= 57343) { + return false; + } + // never used + if (c >= 64976 && c <= 65007) { + return false; + } + if ((c & 65535) === 65535 || (c & 65535) === 65534) { + return false; + } + // control codes + if (c >= 0 && c <= 8) { + return false; + } + if (c === 11) { + return false; + } + if (c >= 14 && c <= 31) { + return false; + } + if (c >= 127 && c <= 159) { + return false; + } + // out of range + if (c > 1114111) { + return false; + } + return true; + } + function fromCodePoint(c) { + /*eslint no-bitwise:0*/ + if (c > 65535) { + c -= 65536; + var surrogate1 = 55296 + (c >> 10), surrogate2 = 56320 + (c & 1023); + return String.fromCharCode(surrogate1, surrogate2); + } + return String.fromCharCode(c); + } + var UNESCAPE_MD_RE = /\\([!"#$%&'()*+,\-.\/:;<=>?@[\\\]^_`{|}~])/g; + var ENTITY_RE = /&([a-z#][a-z0-9]{1,31});/gi; + var UNESCAPE_ALL_RE = new RegExp(UNESCAPE_MD_RE.source + "|" + ENTITY_RE.source, "gi"); + var DIGITAL_ENTITY_TEST_RE = /^#((?:x[a-f0-9]{1,8}|[0-9]{1,8}))$/i; + function replaceEntityPattern(match, name) { + var code; + if (has(entities, name)) { + return entities[name]; + } + if (name.charCodeAt(0) === 35 /* # */ && DIGITAL_ENTITY_TEST_RE.test(name)) { + code = name[1].toLowerCase() === "x" ? parseInt(name.slice(2), 16) : parseInt(name.slice(1), 10); + if (isValidEntityCode(code)) { + return fromCodePoint(code); + } + } + return match; + } + /*function replaceEntities(str) { + if (str.indexOf('&') < 0) { return str; } + + return str.replace(ENTITY_RE, replaceEntityPattern); + }*/ function unescapeMd(str) { + if (str.indexOf("\\") < 0) { + return str; + } + return str.replace(UNESCAPE_MD_RE, "$1"); + } + function unescapeAll(str) { + if (str.indexOf("\\") < 0 && str.indexOf("&") < 0) { + return str; + } + return str.replace(UNESCAPE_ALL_RE, (function(match, escaped, entity) { + if (escaped) { + return escaped; + } + return replaceEntityPattern(match, entity); + })); + } + //////////////////////////////////////////////////////////////////////////////// + var HTML_ESCAPE_TEST_RE = /[&<>"]/; + var HTML_ESCAPE_REPLACE_RE = /[&<>"]/g; + var HTML_REPLACEMENTS = { + "&": "&", + "<": "<", + ">": ">", + '"': """ + }; + function replaceUnsafeChar(ch) { + return HTML_REPLACEMENTS[ch]; + } + function escapeHtml(str) { + if (HTML_ESCAPE_TEST_RE.test(str)) { + return str.replace(HTML_ESCAPE_REPLACE_RE, replaceUnsafeChar); + } + return str; + } + //////////////////////////////////////////////////////////////////////////////// + var REGEXP_ESCAPE_RE = /[.?*+^$[\]\\(){}|-]/g; + function escapeRE(str) { + return str.replace(REGEXP_ESCAPE_RE, "\\$&"); + } + //////////////////////////////////////////////////////////////////////////////// + function isSpace(code) { + switch (code) { + case 9: + case 32: + return true; + } + return false; + } + // Zs (unicode class) || [\t\f\v\r\n] + function isWhiteSpace(code) { + if (code >= 8192 && code <= 8202) { + return true; + } + switch (code) { + case 9: + // \t + case 10: + // \n + case 11: + // \v + case 12: + // \f + case 13: + // \r + case 32: + case 160: + case 5760: + case 8239: + case 8287: + case 12288: + return true; + } + return false; + } + //////////////////////////////////////////////////////////////////////////////// + /*eslint-disable max-len*/ + // Currently without astral characters support. + function isPunctChar(ch) { + return regex$4.test(ch); + } + // Markdown ASCII punctuation characters. + + // !, ", #, $, %, &, ', (, ), *, +, ,, -, ., /, :, ;, <, =, >, ?, @, [, \, ], ^, _, `, {, |, }, or ~ + // http://spec.commonmark.org/0.15/#ascii-punctuation-character + + // Don't confuse with unicode punctuation !!! It lacks some chars in ascii range. + + function isMdAsciiPunct(ch) { + switch (ch) { + case 33 /* ! */ : + case 34 /* " */ : + case 35 /* # */ : + case 36 /* $ */ : + case 37 /* % */ : + case 38 /* & */ : + case 39 /* ' */ : + case 40 /* ( */ : + case 41 /* ) */ : + case 42 /* * */ : + case 43 /* + */ : + case 44 /* , */ : + case 45 /* - */ : + case 46 /* . */ : + case 47 /* / */ : + case 58 /* : */ : + case 59 /* ; */ : + case 60 /* < */ : + case 61 /* = */ : + case 62 /* > */ : + case 63 /* ? */ : + case 64 /* @ */ : + case 91 /* [ */ : + case 92 /* \ */ : + case 93 /* ] */ : + case 94 /* ^ */ : + case 95 /* _ */ : + case 96 /* ` */ : + case 123 /* { */ : + case 124 /* | */ : + case 125 /* } */ : + case 126 /* ~ */ : + return true; + + default: + return false; + } + } + // Hepler to unify [reference labels]. + + function normalizeReference(str) { + // Trim and collapse whitespace + str = str.trim().replace(/\s+/g, " "); + // In node v10 'ẞ'.toLowerCase() === 'Ṿ', which is presumed to be a bug + // fixed in v12 (couldn't find any details). + + // So treat this one as a special case + // (remove this when node v10 is no longer supported). + + if ("\u1e9e".toLowerCase() === "\u1e7e") { + str = str.replace(/\u1e9e/g, "\xdf"); + } + // .toLowerCase().toUpperCase() should get rid of all differences + // between letter variants. + + // Simple .toLowerCase() doesn't normalize 125 code points correctly, + // and .toUpperCase doesn't normalize 6 of them (list of exceptions: + // İ, ϴ, ẞ, Ω, K, Å - those are already uppercased, but have differently + // uppercased versions). + + // Here's an example showing how it happens. Lets take greek letter omega: + // uppercase U+0398 (Θ), U+03f4 (ϴ) and lowercase U+03b8 (θ), U+03d1 (ϑ) + + // Unicode entries: + // 0398;GREEK CAPITAL LETTER THETA;Lu;0;L;;;;;N;;;;03B8; + // 03B8;GREEK SMALL LETTER THETA;Ll;0;L;;;;;N;;;0398;;0398 + // 03D1;GREEK THETA SYMBOL;Ll;0;L; 03B8;;;;N;GREEK SMALL LETTER SCRIPT THETA;;0398;;0398 + // 03F4;GREEK CAPITAL THETA SYMBOL;Lu;0;L; 0398;;;;N;;;;03B8; + + // Case-insensitive comparison should treat all of them as equivalent. + + // But .toLowerCase() doesn't change ϑ (it's already lowercase), + // and .toUpperCase() doesn't change ϴ (already uppercase). + + // Applying first lower then upper case normalizes any character: + // '\u0398\u03f4\u03b8\u03d1'.toLowerCase().toUpperCase() === '\u0398\u0398\u0398\u0398' + + // Note: this is equivalent to unicode case folding; unicode normalization + // is a different step that is not required here. + + // Final result should be uppercased, because it's later stored in an object + // (this avoid a conflict with Object.prototype members, + // most notably, `__proto__`) + + return str.toLowerCase().toUpperCase(); + } + //////////////////////////////////////////////////////////////////////////////// + // Re-export libraries commonly used in both markdown-it and its plugins, + // so plugins won't have to depend on them explicitly, which reduces their + // bundled size (e.g. a browser build). + + exports.lib = {}; + exports.lib.mdurl = mdurl; + exports.lib.ucmicro = uc_micro; + exports.assign = assign; + exports.isString = isString; + exports.has = has; + exports.unescapeMd = unescapeMd; + exports.unescapeAll = unescapeAll; + exports.isValidEntityCode = isValidEntityCode; + exports.fromCodePoint = fromCodePoint; + // exports.replaceEntities = replaceEntities; + exports.escapeHtml = escapeHtml; + exports.arrayReplaceAt = arrayReplaceAt; + exports.isSpace = isSpace; + exports.isWhiteSpace = isWhiteSpace; + exports.isMdAsciiPunct = isMdAsciiPunct; + exports.isPunctChar = isPunctChar; + exports.escapeRE = escapeRE; + exports.normalizeReference = normalizeReference; + })); + // Parse link label + var parse_link_label = function parseLinkLabel(state, start, disableNested) { + var level, found, marker, prevPos, labelEnd = -1, max = state.posMax, oldPos = state.pos; + state.pos = start + 1; + level = 1; + while (state.pos < max) { + marker = state.src.charCodeAt(state.pos); + if (marker === 93 /* ] */) { + level--; + if (level === 0) { + found = true; + break; + } + } + prevPos = state.pos; + state.md.inline.skipToken(state); + if (marker === 91 /* [ */) { + if (prevPos === state.pos - 1) { + // increase level if we find text `[`, which is not a part of any token + level++; + } else if (disableNested) { + state.pos = oldPos; + return -1; + } + } + } + if (found) { + labelEnd = state.pos; + } + // restore old state + state.pos = oldPos; + return labelEnd; + }; + var unescapeAll$2 = utils.unescapeAll; + var parse_link_destination = function parseLinkDestination(str, start, max) { + var code, level, pos = start, result = { + ok: false, + pos: 0, + lines: 0, + str: "" + }; + if (str.charCodeAt(pos) === 60 /* < */) { + pos++; + while (pos < max) { + code = str.charCodeAt(pos); + if (code === 10 /* \n */) { + return result; + } + if (code === 60 /* < */) { + return result; + } + if (code === 62 /* > */) { + result.pos = pos + 1; + result.str = unescapeAll$2(str.slice(start + 1, pos)); + result.ok = true; + return result; + } + if (code === 92 /* \ */ && pos + 1 < max) { + pos += 2; + continue; + } + pos++; + } + // no closing '>' + return result; + } + // this should be ... } else { ... branch + level = 0; + while (pos < max) { + code = str.charCodeAt(pos); + if (code === 32) { + break; + } + // ascii control characters + if (code < 32 || code === 127) { + break; + } + if (code === 92 /* \ */ && pos + 1 < max) { + if (str.charCodeAt(pos + 1) === 32) { + break; + } + pos += 2; + continue; + } + if (code === 40 /* ( */) { + level++; + if (level > 32) { + return result; + } + } + if (code === 41 /* ) */) { + if (level === 0) { + break; + } + level--; + } + pos++; + } + if (start === pos) { + return result; + } + if (level !== 0) { + return result; + } + result.str = unescapeAll$2(str.slice(start, pos)); + result.pos = pos; + result.ok = true; + return result; + }; + var unescapeAll$1 = utils.unescapeAll; + var parse_link_title = function parseLinkTitle(str, start, max) { + var code, marker, lines = 0, pos = start, result = { + ok: false, + pos: 0, + lines: 0, + str: "" + }; + if (pos >= max) { + return result; + } + marker = str.charCodeAt(pos); + if (marker !== 34 /* " */ && marker !== 39 /* ' */ && marker !== 40 /* ( */) { + return result; + } + pos++; + // if opening marker is "(", switch it to closing marker ")" + if (marker === 40) { + marker = 41; + } + while (pos < max) { + code = str.charCodeAt(pos); + if (code === marker) { + result.pos = pos + 1; + result.lines = lines; + result.str = unescapeAll$1(str.slice(start + 1, pos)); + result.ok = true; + return result; + } else if (code === 40 /* ( */ && marker === 41 /* ) */) { + return result; + } else if (code === 10) { + lines++; + } else if (code === 92 /* \ */ && pos + 1 < max) { + pos++; + if (str.charCodeAt(pos) === 10) { + lines++; + } + } + pos++; + } + return result; + }; + var parseLinkLabel = parse_link_label; + var parseLinkDestination = parse_link_destination; + var parseLinkTitle = parse_link_title; + var helpers = { + parseLinkLabel: parseLinkLabel, + parseLinkDestination: parseLinkDestination, + parseLinkTitle: parseLinkTitle + }; + var assign$1 = utils.assign; + var unescapeAll = utils.unescapeAll; + var escapeHtml = utils.escapeHtml; + //////////////////////////////////////////////////////////////////////////////// + var default_rules = {}; + default_rules.code_inline = function(tokens, idx, options, env, slf) { + var token = tokens[idx]; + return "" + escapeHtml(token.content) + ""; + }; + default_rules.code_block = function(tokens, idx, options, env, slf) { + var token = tokens[idx]; + return "" + escapeHtml(tokens[idx].content) + "\n"; + }; + default_rules.fence = function(tokens, idx, options, env, slf) { + var token = tokens[idx], info = token.info ? unescapeAll(token.info).trim() : "", langName = "", langAttrs = "", highlighted, i, arr, tmpAttrs, tmpToken; + if (info) { + arr = info.split(/(\s+)/g); + langName = arr[0]; + langAttrs = arr.slice(2).join(""); + } + if (options.highlight) { + highlighted = options.highlight(token.content, langName, langAttrs) || escapeHtml(token.content); + } else { + highlighted = escapeHtml(token.content); + } + if (highlighted.indexOf("" + highlighted + "\n"; + } + return "
" + highlighted + "
\n"; + }; + default_rules.image = function(tokens, idx, options, env, slf) { + var token = tokens[idx]; + // "alt" attr MUST be set, even if empty. Because it's mandatory and + // should be placed on proper position for tests. + + // Replace content with actual value + token.attrs[token.attrIndex("alt")][1] = slf.renderInlineAsText(token.children, options, env); + return slf.renderToken(tokens, idx, options); + }; + default_rules.hardbreak = function(tokens, idx, options /*, env */) { + return options.xhtmlOut ? "
\n" : "
\n"; + }; + default_rules.softbreak = function(tokens, idx, options /*, env */) { + return options.breaks ? options.xhtmlOut ? "
\n" : "
\n" : "\n"; + }; + default_rules.text = function(tokens, idx /*, options, env */) { + return escapeHtml(tokens[idx].content); + }; + default_rules.html_block = function(tokens, idx /*, options, env */) { + return tokens[idx].content; + }; + default_rules.html_inline = function(tokens, idx /*, options, env */) { + return tokens[idx].content; + }; + /** + * new Renderer() + * + * Creates new [[Renderer]] instance and fill [[Renderer#rules]] with defaults. + **/ function Renderer() { + /** + * Renderer#rules -> Object + * + * Contains render rules for tokens. Can be updated and extended. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')(); + * + * md.renderer.rules.strong_open = function () { return ''; }; + * md.renderer.rules.strong_close = function () { return ''; }; + * + * var result = md.renderInline(...); + * ``` + * + * Each rule is called as independent static function with fixed signature: + * + * ```javascript + * function my_token_render(tokens, idx, options, env, renderer) { + * // ... + * return renderedHTML; + * } + * ``` + * + * See [source code](https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js) + * for more details and examples. + **/ + this.rules = assign$1({}, default_rules); + } + /** + * Renderer.renderAttrs(token) -> String + * + * Render token attributes to string. + **/ Renderer.prototype.renderAttrs = function renderAttrs(token) { + var i, l, result; + if (!token.attrs) { + return ""; + } + result = ""; + for (i = 0, l = token.attrs.length; i < l; i++) { + result += " " + escapeHtml(token.attrs[i][0]) + '="' + escapeHtml(token.attrs[i][1]) + '"'; + } + return result; + }; + /** + * Renderer.renderToken(tokens, idx, options) -> String + * - tokens (Array): list of tokens + * - idx (Numbed): token index to render + * - options (Object): params of parser instance + * + * Default token renderer. Can be overriden by custom function + * in [[Renderer#rules]]. + **/ Renderer.prototype.renderToken = function renderToken(tokens, idx, options) { + var nextToken, result = "", needLf = false, token = tokens[idx]; + // Tight list paragraphs + if (token.hidden) { + return ""; + } + // Insert a newline between hidden paragraph and subsequent opening + // block-level tag. + + // For example, here we should insert a newline before blockquote: + // - a + // > + + if (token.block && token.nesting !== -1 && idx && tokens[idx - 1].hidden) { + result += "\n"; + } + // Add token name, e.g. ``. + needLf = false; + } + } + } + } + result += needLf ? ">\n" : ">"; + return result; + }; + /** + * Renderer.renderInline(tokens, options, env) -> String + * - tokens (Array): list on block tokens to render + * - options (Object): params of parser instance + * - env (Object): additional data from parsed input (references, for example) + * + * The same as [[Renderer.render]], but for single token of `inline` type. + **/ Renderer.prototype.renderInline = function(tokens, options, env) { + var type, result = "", rules = this.rules; + for (var i = 0, len = tokens.length; i < len; i++) { + type = tokens[i].type; + if (typeof rules[type] !== "undefined") { + result += rules[type](tokens, i, options, env, this); + } else { + result += this.renderToken(tokens, i, options); + } + } + return result; + }; + /** internal + * Renderer.renderInlineAsText(tokens, options, env) -> String + * - tokens (Array): list on block tokens to render + * - options (Object): params of parser instance + * - env (Object): additional data from parsed input (references, for example) + * + * Special kludge for image `alt` attributes to conform CommonMark spec. + * Don't try to use it! Spec requires to show `alt` content with stripped markup, + * instead of simple escaping. + **/ Renderer.prototype.renderInlineAsText = function(tokens, options, env) { + var result = ""; + for (var i = 0, len = tokens.length; i < len; i++) { + if (tokens[i].type === "text") { + result += tokens[i].content; + } else if (tokens[i].type === "image") { + result += this.renderInlineAsText(tokens[i].children, options, env); + } else if (tokens[i].type === "softbreak") { + result += "\n"; + } + } + return result; + }; + /** + * Renderer.render(tokens, options, env) -> String + * - tokens (Array): list on block tokens to render + * - options (Object): params of parser instance + * - env (Object): additional data from parsed input (references, for example) + * + * Takes token stream and generates HTML. Probably, you will never need to call + * this method directly. + **/ Renderer.prototype.render = function(tokens, options, env) { + var i, len, type, result = "", rules = this.rules; + for (i = 0, len = tokens.length; i < len; i++) { + type = tokens[i].type; + if (type === "inline") { + result += this.renderInline(tokens[i].children, options, env); + } else if (typeof rules[type] !== "undefined") { + result += rules[type](tokens, i, options, env, this); + } else { + result += this.renderToken(tokens, i, options, env); + } + } + return result; + }; + var renderer = Renderer; + /** + * class Ruler + * + * Helper class, used by [[MarkdownIt#core]], [[MarkdownIt#block]] and + * [[MarkdownIt#inline]] to manage sequences of functions (rules): + * + * - keep rules in defined order + * - assign the name to each rule + * - enable/disable rules + * - add/replace rules + * - allow assign rules to additional named chains (in the same) + * - cacheing lists of active rules + * + * You will not need use this class directly until write plugins. For simple + * rules control use [[MarkdownIt.disable]], [[MarkdownIt.enable]] and + * [[MarkdownIt.use]]. + **/ + /** + * new Ruler() + **/ function Ruler() { + // List of added rules. Each element is: + // { + // name: XXX, + // enabled: Boolean, + // fn: Function(), + // alt: [ name2, name3 ] + // } + this.__rules__ = []; + // Cached rule chains. + + // First level - chain name, '' for default. + // Second level - diginal anchor for fast filtering by charcodes. + + this.__cache__ = null; + } + //////////////////////////////////////////////////////////////////////////////// + // Helper methods, should not be used directly + // Find rule index by name + + Ruler.prototype.__find__ = function(name) { + for (var i = 0; i < this.__rules__.length; i++) { + if (this.__rules__[i].name === name) { + return i; + } + } + return -1; + }; + // Build rules lookup cache + + Ruler.prototype.__compile__ = function() { + var self = this; + var chains = [ "" ]; + // collect unique names + self.__rules__.forEach((function(rule) { + if (!rule.enabled) { + return; + } + rule.alt.forEach((function(altName) { + if (chains.indexOf(altName) < 0) { + chains.push(altName); + } + })); + })); + self.__cache__ = {}; + chains.forEach((function(chain) { + self.__cache__[chain] = []; + self.__rules__.forEach((function(rule) { + if (!rule.enabled) { + return; + } + if (chain && rule.alt.indexOf(chain) < 0) { + return; + } + self.__cache__[chain].push(rule.fn); + })); + })); + }; + /** + * Ruler.at(name, fn [, options]) + * - name (String): rule name to replace. + * - fn (Function): new rule function. + * - options (Object): new rule options (not mandatory). + * + * Replace rule by name with new function & options. Throws error if name not + * found. + * + * ##### Options: + * + * - __alt__ - array with names of "alternate" chains. + * + * ##### Example + * + * Replace existing typographer replacement rule with new one: + * + * ```javascript + * var md = require('markdown-it')(); + * + * md.core.ruler.at('replacements', function replace(state) { + * //... + * }); + * ``` + **/ Ruler.prototype.at = function(name, fn, options) { + var index = this.__find__(name); + var opt = options || {}; + if (index === -1) { + throw new Error("Parser rule not found: " + name); + } + this.__rules__[index].fn = fn; + this.__rules__[index].alt = opt.alt || []; + this.__cache__ = null; + }; + /** + * Ruler.before(beforeName, ruleName, fn [, options]) + * - beforeName (String): new rule will be added before this one. + * - ruleName (String): name of added rule. + * - fn (Function): rule function. + * - options (Object): rule options (not mandatory). + * + * Add new rule to chain before one with given name. See also + * [[Ruler.after]], [[Ruler.push]]. + * + * ##### Options: + * + * - __alt__ - array with names of "alternate" chains. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')(); + * + * md.block.ruler.before('paragraph', 'my_rule', function replace(state) { + * //... + * }); + * ``` + **/ Ruler.prototype.before = function(beforeName, ruleName, fn, options) { + var index = this.__find__(beforeName); + var opt = options || {}; + if (index === -1) { + throw new Error("Parser rule not found: " + beforeName); + } + this.__rules__.splice(index, 0, { + name: ruleName, + enabled: true, + fn: fn, + alt: opt.alt || [] + }); + this.__cache__ = null; + }; + /** + * Ruler.after(afterName, ruleName, fn [, options]) + * - afterName (String): new rule will be added after this one. + * - ruleName (String): name of added rule. + * - fn (Function): rule function. + * - options (Object): rule options (not mandatory). + * + * Add new rule to chain after one with given name. See also + * [[Ruler.before]], [[Ruler.push]]. + * + * ##### Options: + * + * - __alt__ - array with names of "alternate" chains. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')(); + * + * md.inline.ruler.after('text', 'my_rule', function replace(state) { + * //... + * }); + * ``` + **/ Ruler.prototype.after = function(afterName, ruleName, fn, options) { + var index = this.__find__(afterName); + var opt = options || {}; + if (index === -1) { + throw new Error("Parser rule not found: " + afterName); + } + this.__rules__.splice(index + 1, 0, { + name: ruleName, + enabled: true, + fn: fn, + alt: opt.alt || [] + }); + this.__cache__ = null; + }; + /** + * Ruler.push(ruleName, fn [, options]) + * - ruleName (String): name of added rule. + * - fn (Function): rule function. + * - options (Object): rule options (not mandatory). + * + * Push new rule to the end of chain. See also + * [[Ruler.before]], [[Ruler.after]]. + * + * ##### Options: + * + * - __alt__ - array with names of "alternate" chains. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')(); + * + * md.core.ruler.push('my_rule', function replace(state) { + * //... + * }); + * ``` + **/ Ruler.prototype.push = function(ruleName, fn, options) { + var opt = options || {}; + this.__rules__.push({ + name: ruleName, + enabled: true, + fn: fn, + alt: opt.alt || [] + }); + this.__cache__ = null; + }; + /** + * Ruler.enable(list [, ignoreInvalid]) -> Array + * - list (String|Array): list of rule names to enable. + * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. + * + * Enable rules with given names. If any rule name not found - throw Error. + * Errors can be disabled by second param. + * + * Returns list of found rule names (if no exception happened). + * + * See also [[Ruler.disable]], [[Ruler.enableOnly]]. + **/ Ruler.prototype.enable = function(list, ignoreInvalid) { + if (!Array.isArray(list)) { + list = [ list ]; + } + var result = []; + // Search by name and enable + list.forEach((function(name) { + var idx = this.__find__(name); + if (idx < 0) { + if (ignoreInvalid) { + return; + } + throw new Error("Rules manager: invalid rule name " + name); + } + this.__rules__[idx].enabled = true; + result.push(name); + }), this); + this.__cache__ = null; + return result; + }; + /** + * Ruler.enableOnly(list [, ignoreInvalid]) + * - list (String|Array): list of rule names to enable (whitelist). + * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. + * + * Enable rules with given names, and disable everything else. If any rule name + * not found - throw Error. Errors can be disabled by second param. + * + * See also [[Ruler.disable]], [[Ruler.enable]]. + **/ Ruler.prototype.enableOnly = function(list, ignoreInvalid) { + if (!Array.isArray(list)) { + list = [ list ]; + } + this.__rules__.forEach((function(rule) { + rule.enabled = false; + })); + this.enable(list, ignoreInvalid); + }; + /** + * Ruler.disable(list [, ignoreInvalid]) -> Array + * - list (String|Array): list of rule names to disable. + * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. + * + * Disable rules with given names. If any rule name not found - throw Error. + * Errors can be disabled by second param. + * + * Returns list of found rule names (if no exception happened). + * + * See also [[Ruler.enable]], [[Ruler.enableOnly]]. + **/ Ruler.prototype.disable = function(list, ignoreInvalid) { + if (!Array.isArray(list)) { + list = [ list ]; + } + var result = []; + // Search by name and disable + list.forEach((function(name) { + var idx = this.__find__(name); + if (idx < 0) { + if (ignoreInvalid) { + return; + } + throw new Error("Rules manager: invalid rule name " + name); + } + this.__rules__[idx].enabled = false; + result.push(name); + }), this); + this.__cache__ = null; + return result; + }; + /** + * Ruler.getRules(chainName) -> Array + * + * Return array of active functions (rules) for given chain name. It analyzes + * rules configuration, compiles caches if not exists and returns result. + * + * Default chain name is `''` (empty string). It can't be skipped. That's + * done intentionally, to keep signature monomorphic for high speed. + **/ Ruler.prototype.getRules = function(chainName) { + if (this.__cache__ === null) { + this.__compile__(); + } + // Chain can be empty, if rules disabled. But we still have to return Array. + return this.__cache__[chainName] || []; + }; + var ruler = Ruler; + // Normalize input string + // https://spec.commonmark.org/0.29/#line-ending + var NEWLINES_RE = /\r\n?|\n/g; + var NULL_RE = /\0/g; + var normalize = function normalize(state) { + var str; + // Normalize newlines + str = state.src.replace(NEWLINES_RE, "\n"); + // Replace NULL characters + str = str.replace(NULL_RE, "\ufffd"); + state.src = str; + }; + var block = function block(state) { + var token; + if (state.inlineMode) { + token = new state.Token("inline", "", 0); + token.content = state.src; + token.map = [ 0, 1 ]; + token.children = []; + state.tokens.push(token); + } else { + state.md.block.parse(state.src, state.md, state.env, state.tokens); + } + }; + var inline = function inline(state) { + var tokens = state.tokens, tok, i, l; + // Parse inlines + for (i = 0, l = tokens.length; i < l; i++) { + tok = tokens[i]; + if (tok.type === "inline") { + state.md.inline.parse(tok.content, state.md, state.env, tok.children); + } + } + }; + var arrayReplaceAt = utils.arrayReplaceAt; + function isLinkOpen$1(str) { + return /^\s]/i.test(str); + } + function isLinkClose$1(str) { + return /^<\/a\s*>/i.test(str); + } + var linkify$1 = function linkify(state) { + var i, j, l, tokens, token, currentToken, nodes, ln, text, pos, lastPos, level, htmlLinkLevel, url, fullUrl, urlText, blockTokens = state.tokens, links; + if (!state.md.options.linkify) { + return; + } + for (j = 0, l = blockTokens.length; j < l; j++) { + if (blockTokens[j].type !== "inline" || !state.md.linkify.pretest(blockTokens[j].content)) { + continue; + } + tokens = blockTokens[j].children; + htmlLinkLevel = 0; + // We scan from the end, to keep position when new tags added. + // Use reversed logic in links start/end match + for (i = tokens.length - 1; i >= 0; i--) { + currentToken = tokens[i]; + // Skip content of markdown links + if (currentToken.type === "link_close") { + i--; + while (tokens[i].level !== currentToken.level && tokens[i].type !== "link_open") { + i--; + } + continue; + } + // Skip content of html tag links + if (currentToken.type === "html_inline") { + if (isLinkOpen$1(currentToken.content) && htmlLinkLevel > 0) { + htmlLinkLevel--; + } + if (isLinkClose$1(currentToken.content)) { + htmlLinkLevel++; + } + } + if (htmlLinkLevel > 0) { + continue; + } + if (currentToken.type === "text" && state.md.linkify.test(currentToken.content)) { + text = currentToken.content; + links = state.md.linkify.match(text); + // Now split string to nodes + nodes = []; + level = currentToken.level; + lastPos = 0; + // forbid escape sequence at the start of the string, + // this avoids http\://example.com/ from being linkified as + // http://example.com/ + if (links.length > 0 && links[0].index === 0 && i > 0 && tokens[i - 1].type === "text_special") { + links = links.slice(1); + } + for (ln = 0; ln < links.length; ln++) { + url = links[ln].url; + fullUrl = state.md.normalizeLink(url); + if (!state.md.validateLink(fullUrl)) { + continue; + } + urlText = links[ln].text; + // Linkifier might send raw hostnames like "example.com", where url + // starts with domain name. So we prepend http:// in those cases, + // and remove it afterwards. + + if (!links[ln].schema) { + urlText = state.md.normalizeLinkText("http://" + urlText).replace(/^http:\/\//, ""); + } else if (links[ln].schema === "mailto:" && !/^mailto:/i.test(urlText)) { + urlText = state.md.normalizeLinkText("mailto:" + urlText).replace(/^mailto:/, ""); + } else { + urlText = state.md.normalizeLinkText(urlText); + } + pos = links[ln].index; + if (pos > lastPos) { + token = new state.Token("text", "", 0); + token.content = text.slice(lastPos, pos); + token.level = level; + nodes.push(token); + } + token = new state.Token("link_open", "a", 1); + token.attrs = [ [ "href", fullUrl ] ]; + token.level = level++; + token.markup = "linkify"; + token.info = "auto"; + nodes.push(token); + token = new state.Token("text", "", 0); + token.content = urlText; + token.level = level; + nodes.push(token); + token = new state.Token("link_close", "a", -1); + token.level = --level; + token.markup = "linkify"; + token.info = "auto"; + nodes.push(token); + lastPos = links[ln].lastIndex; + } + if (lastPos < text.length) { + token = new state.Token("text", "", 0); + token.content = text.slice(lastPos); + token.level = level; + nodes.push(token); + } + // replace current node + blockTokens[j].children = tokens = arrayReplaceAt(tokens, i, nodes); + } + } + } + }; + // Simple typographic replacements + // TODO: + // - fractionals 1/2, 1/4, 3/4 -> ½, ¼, ¾ + // - multiplications 2 x 4 -> 2 × 4 + var RARE_RE = /\+-|\.\.|\?\?\?\?|!!!!|,,|--/; + // Workaround for phantomjs - need regex without /g flag, + // or root check will fail every second time + var SCOPED_ABBR_TEST_RE = /\((c|tm|r)\)/i; + var SCOPED_ABBR_RE = /\((c|tm|r)\)/gi; + var SCOPED_ABBR = { + c: "\xa9", + r: "\xae", + tm: "\u2122" + }; + function replaceFn(match, name) { + return SCOPED_ABBR[name.toLowerCase()]; + } + function replace_scoped(inlineTokens) { + var i, token, inside_autolink = 0; + for (i = inlineTokens.length - 1; i >= 0; i--) { + token = inlineTokens[i]; + if (token.type === "text" && !inside_autolink) { + token.content = token.content.replace(SCOPED_ABBR_RE, replaceFn); + } + if (token.type === "link_open" && token.info === "auto") { + inside_autolink--; + } + if (token.type === "link_close" && token.info === "auto") { + inside_autolink++; + } + } + } + function replace_rare(inlineTokens) { + var i, token, inside_autolink = 0; + for (i = inlineTokens.length - 1; i >= 0; i--) { + token = inlineTokens[i]; + if (token.type === "text" && !inside_autolink) { + if (RARE_RE.test(token.content)) { + token.content = token.content.replace(/\+-/g, "\xb1").replace(/\.{2,}/g, "\u2026").replace(/([?!])\u2026/g, "$1..").replace(/([?!]){4,}/g, "$1$1$1").replace(/,{2,}/g, ",").replace(/(^|[^-])---(?=[^-]|$)/gm, "$1\u2014").replace(/(^|\s)--(?=\s|$)/gm, "$1\u2013").replace(/(^|[^-\s])--(?=[^-\s]|$)/gm, "$1\u2013"); + } + } + if (token.type === "link_open" && token.info === "auto") { + inside_autolink--; + } + if (token.type === "link_close" && token.info === "auto") { + inside_autolink++; + } + } + } + var replacements = function replace(state) { + var blkIdx; + if (!state.md.options.typographer) { + return; + } + for (blkIdx = state.tokens.length - 1; blkIdx >= 0; blkIdx--) { + if (state.tokens[blkIdx].type !== "inline") { + continue; + } + if (SCOPED_ABBR_TEST_RE.test(state.tokens[blkIdx].content)) { + replace_scoped(state.tokens[blkIdx].children); + } + if (RARE_RE.test(state.tokens[blkIdx].content)) { + replace_rare(state.tokens[blkIdx].children); + } + } + }; + var isWhiteSpace$1 = utils.isWhiteSpace; + var isPunctChar$1 = utils.isPunctChar; + var isMdAsciiPunct$1 = utils.isMdAsciiPunct; + var QUOTE_TEST_RE = /['"]/; + var QUOTE_RE = /['"]/g; + var APOSTROPHE = "\u2019"; + /* ’ */ function replaceAt(str, index, ch) { + return str.slice(0, index) + ch + str.slice(index + 1); + } + function process_inlines(tokens, state) { + var i, token, text, t, pos, max, thisLevel, item, lastChar, nextChar, isLastPunctChar, isNextPunctChar, isLastWhiteSpace, isNextWhiteSpace, canOpen, canClose, j, isSingle, stack, openQuote, closeQuote; + stack = []; + for (i = 0; i < tokens.length; i++) { + token = tokens[i]; + thisLevel = tokens[i].level; + for (j = stack.length - 1; j >= 0; j--) { + if (stack[j].level <= thisLevel) { + break; + } + } + stack.length = j + 1; + if (token.type !== "text") { + continue; + } + text = token.content; + pos = 0; + max = text.length; + /*eslint no-labels:0,block-scoped-var:0*/ OUTER: while (pos < max) { + QUOTE_RE.lastIndex = pos; + t = QUOTE_RE.exec(text); + if (!t) { + break; + } + canOpen = canClose = true; + pos = t.index + 1; + isSingle = t[0] === "'"; + // Find previous character, + // default to space if it's the beginning of the line + + lastChar = 32; + if (t.index - 1 >= 0) { + lastChar = text.charCodeAt(t.index - 1); + } else { + for (j = i - 1; j >= 0; j--) { + if (tokens[j].type === "softbreak" || tokens[j].type === "hardbreak") break; + // lastChar defaults to 0x20 + if (!tokens[j].content) continue; + // should skip all tokens except 'text', 'html_inline' or 'code_inline' + lastChar = tokens[j].content.charCodeAt(tokens[j].content.length - 1); + break; + } + } + // Find next character, + // default to space if it's the end of the line + + nextChar = 32; + if (pos < max) { + nextChar = text.charCodeAt(pos); + } else { + for (j = i + 1; j < tokens.length; j++) { + if (tokens[j].type === "softbreak" || tokens[j].type === "hardbreak") break; + // nextChar defaults to 0x20 + if (!tokens[j].content) continue; + // should skip all tokens except 'text', 'html_inline' or 'code_inline' + nextChar = tokens[j].content.charCodeAt(0); + break; + } + } + isLastPunctChar = isMdAsciiPunct$1(lastChar) || isPunctChar$1(String.fromCharCode(lastChar)); + isNextPunctChar = isMdAsciiPunct$1(nextChar) || isPunctChar$1(String.fromCharCode(nextChar)); + isLastWhiteSpace = isWhiteSpace$1(lastChar); + isNextWhiteSpace = isWhiteSpace$1(nextChar); + if (isNextWhiteSpace) { + canOpen = false; + } else if (isNextPunctChar) { + if (!(isLastWhiteSpace || isLastPunctChar)) { + canOpen = false; + } + } + if (isLastWhiteSpace) { + canClose = false; + } else if (isLastPunctChar) { + if (!(isNextWhiteSpace || isNextPunctChar)) { + canClose = false; + } + } + if (nextChar === 34 /* " */ && t[0] === '"') { + if (lastChar >= 48 /* 0 */ && lastChar <= 57 /* 9 */) { + // special case: 1"" - count first quote as an inch + canClose = canOpen = false; + } + } + if (canOpen && canClose) { + // Replace quotes in the middle of punctuation sequence, but not + // in the middle of the words, i.e.: + // 1. foo " bar " baz - not replaced + // 2. foo-"-bar-"-baz - replaced + // 3. foo"bar"baz - not replaced + canOpen = isLastPunctChar; + canClose = isNextPunctChar; + } + if (!canOpen && !canClose) { + // middle of word + if (isSingle) { + token.content = replaceAt(token.content, t.index, APOSTROPHE); + } + continue; + } + if (canClose) { + // this could be a closing quote, rewind the stack to get a match + for (j = stack.length - 1; j >= 0; j--) { + item = stack[j]; + if (stack[j].level < thisLevel) { + break; + } + if (item.single === isSingle && stack[j].level === thisLevel) { + item = stack[j]; + if (isSingle) { + openQuote = state.md.options.quotes[2]; + closeQuote = state.md.options.quotes[3]; + } else { + openQuote = state.md.options.quotes[0]; + closeQuote = state.md.options.quotes[1]; + } + // replace token.content *before* tokens[item.token].content, + // because, if they are pointing at the same token, replaceAt + // could mess up indices when quote length != 1 + token.content = replaceAt(token.content, t.index, closeQuote); + tokens[item.token].content = replaceAt(tokens[item.token].content, item.pos, openQuote); + pos += closeQuote.length - 1; + if (item.token === i) { + pos += openQuote.length - 1; + } + text = token.content; + max = text.length; + stack.length = j; + continue OUTER; + } + } + } + if (canOpen) { + stack.push({ + token: i, + pos: t.index, + single: isSingle, + level: thisLevel + }); + } else if (canClose && isSingle) { + token.content = replaceAt(token.content, t.index, APOSTROPHE); + } + } + } + } + var smartquotes = function smartquotes(state) { + /*eslint max-depth:0*/ + var blkIdx; + if (!state.md.options.typographer) { + return; + } + for (blkIdx = state.tokens.length - 1; blkIdx >= 0; blkIdx--) { + if (state.tokens[blkIdx].type !== "inline" || !QUOTE_TEST_RE.test(state.tokens[blkIdx].content)) { + continue; + } + process_inlines(state.tokens[blkIdx].children, state); + } + }; + // Join raw text tokens with the rest of the text + var text_join = function text_join(state) { + var j, l, tokens, curr, max, last, blockTokens = state.tokens; + for (j = 0, l = blockTokens.length; j < l; j++) { + if (blockTokens[j].type !== "inline") continue; + tokens = blockTokens[j].children; + max = tokens.length; + for (curr = 0; curr < max; curr++) { + if (tokens[curr].type === "text_special") { + tokens[curr].type = "text"; + } + } + for (curr = last = 0; curr < max; curr++) { + if (tokens[curr].type === "text" && curr + 1 < max && tokens[curr + 1].type === "text") { + // collapse two adjacent text nodes + tokens[curr + 1].content = tokens[curr].content + tokens[curr + 1].content; + } else { + if (curr !== last) { + tokens[last] = tokens[curr]; + } + last++; + } + } + if (curr !== last) { + tokens.length = last; + } + } + }; + // Token class + /** + * class Token + **/ + /** + * new Token(type, tag, nesting) + * + * Create new token and fill passed properties. + **/ function Token(type, tag, nesting) { + /** + * Token#type -> String + * + * Type of the token (string, e.g. "paragraph_open") + **/ + this.type = type; + /** + * Token#tag -> String + * + * html tag name, e.g. "p" + **/ this.tag = tag; + /** + * Token#attrs -> Array + * + * Html attributes. Format: `[ [ name1, value1 ], [ name2, value2 ] ]` + **/ this.attrs = null; + /** + * Token#map -> Array + * + * Source map info. Format: `[ line_begin, line_end ]` + **/ this.map = null; + /** + * Token#nesting -> Number + * + * Level change (number in {-1, 0, 1} set), where: + * + * - `1` means the tag is opening + * - `0` means the tag is self-closing + * - `-1` means the tag is closing + **/ this.nesting = nesting; + /** + * Token#level -> Number + * + * nesting level, the same as `state.level` + **/ this.level = 0; + /** + * Token#children -> Array + * + * An array of child nodes (inline and img tokens) + **/ this.children = null; + /** + * Token#content -> String + * + * In a case of self-closing tag (code, html, fence, etc.), + * it has contents of this tag. + **/ this.content = ""; + /** + * Token#markup -> String + * + * '*' or '_' for emphasis, fence string for fence, etc. + **/ this.markup = ""; + /** + * Token#info -> String + * + * Additional information: + * + * - Info string for "fence" tokens + * - The value "auto" for autolink "link_open" and "link_close" tokens + * - The string value of the item marker for ordered-list "list_item_open" tokens + **/ this.info = ""; + /** + * Token#meta -> Object + * + * A place for plugins to store an arbitrary data + **/ this.meta = null; + /** + * Token#block -> Boolean + * + * True for block-level tokens, false for inline tokens. + * Used in renderer to calculate line breaks + **/ this.block = false; + /** + * Token#hidden -> Boolean + * + * If it's true, ignore this element when rendering. Used for tight lists + * to hide paragraphs. + **/ this.hidden = false; + } + /** + * Token.attrIndex(name) -> Number + * + * Search attribute index by name. + **/ Token.prototype.attrIndex = function attrIndex(name) { + var attrs, i, len; + if (!this.attrs) { + return -1; + } + attrs = this.attrs; + for (i = 0, len = attrs.length; i < len; i++) { + if (attrs[i][0] === name) { + return i; + } + } + return -1; + }; + /** + * Token.attrPush(attrData) + * + * Add `[ name, value ]` attribute to list. Init attrs if necessary + **/ Token.prototype.attrPush = function attrPush(attrData) { + if (this.attrs) { + this.attrs.push(attrData); + } else { + this.attrs = [ attrData ]; + } + }; + /** + * Token.attrSet(name, value) + * + * Set `name` attribute to `value`. Override old value if exists. + **/ Token.prototype.attrSet = function attrSet(name, value) { + var idx = this.attrIndex(name), attrData = [ name, value ]; + if (idx < 0) { + this.attrPush(attrData); + } else { + this.attrs[idx] = attrData; + } + }; + /** + * Token.attrGet(name) + * + * Get the value of attribute `name`, or null if it does not exist. + **/ Token.prototype.attrGet = function attrGet(name) { + var idx = this.attrIndex(name), value = null; + if (idx >= 0) { + value = this.attrs[idx][1]; + } + return value; + }; + /** + * Token.attrJoin(name, value) + * + * Join value to existing attribute via space. Or create new attribute if not + * exists. Useful to operate with token classes. + **/ Token.prototype.attrJoin = function attrJoin(name, value) { + var idx = this.attrIndex(name); + if (idx < 0) { + this.attrPush([ name, value ]); + } else { + this.attrs[idx][1] = this.attrs[idx][1] + " " + value; + } + }; + var token = Token; + function StateCore(src, md, env) { + this.src = src; + this.env = env; + this.tokens = []; + this.inlineMode = false; + this.md = md; + // link to parser instance + } + // re-export Token class to use in core rules + StateCore.prototype.Token = token; + var state_core = StateCore; + var _rules$2 = [ [ "normalize", normalize ], [ "block", block ], [ "inline", inline ], [ "linkify", linkify$1 ], [ "replacements", replacements ], [ "smartquotes", smartquotes ], + // `text_join` finds `text_special` tokens (for escape sequences) + // and joins them with the rest of the text + [ "text_join", text_join ] ]; + /** + * new Core() + **/ function Core() { + /** + * Core#ruler -> Ruler + * + * [[Ruler]] instance. Keep configuration of core rules. + **/ + this.ruler = new ruler; + for (var i = 0; i < _rules$2.length; i++) { + this.ruler.push(_rules$2[i][0], _rules$2[i][1]); + } + } + /** + * Core.process(state) + * + * Executes core chain rules. + **/ Core.prototype.process = function(state) { + var i, l, rules; + rules = this.ruler.getRules(""); + for (i = 0, l = rules.length; i < l; i++) { + rules[i](state); + } + }; + Core.prototype.State = state_core; + var parser_core = Core; + var isSpace$a = utils.isSpace; + function getLine(state, line) { + var pos = state.bMarks[line] + state.tShift[line], max = state.eMarks[line]; + return state.src.slice(pos, max); + } + function escapedSplit(str) { + var result = [], pos = 0, max = str.length, ch, isEscaped = false, lastPos = 0, current = ""; + ch = str.charCodeAt(pos); + while (pos < max) { + if (ch === 124 /* | */) { + if (!isEscaped) { + // pipe separating cells, '|' + result.push(current + str.substring(lastPos, pos)); + current = ""; + lastPos = pos + 1; + } else { + // escaped pipe, '\|' + current += str.substring(lastPos, pos - 1); + lastPos = pos; + } + } + isEscaped = ch === 92 /* \ */; + pos++; + ch = str.charCodeAt(pos); + } + result.push(current + str.substring(lastPos)); + return result; + } + var table = function table(state, startLine, endLine, silent) { + var ch, lineText, pos, i, l, nextLine, columns, columnCount, token, aligns, t, tableLines, tbodyLines, oldParentType, terminate, terminatorRules, firstCh, secondCh; + // should have at least two lines + if (startLine + 2 > endLine) { + return false; + } + nextLine = startLine + 1; + if (state.sCount[nextLine] < state.blkIndent) { + return false; + } + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[nextLine] - state.blkIndent >= 4) { + return false; + } + // first character of the second line should be '|', '-', ':', + // and no other characters are allowed but spaces; + // basically, this is the equivalent of /^[-:|][-:|\s]*$/ regexp + pos = state.bMarks[nextLine] + state.tShift[nextLine]; + if (pos >= state.eMarks[nextLine]) { + return false; + } + firstCh = state.src.charCodeAt(pos++); + if (firstCh !== 124 /* | */ && firstCh !== 45 /* - */ && firstCh !== 58 /* : */) { + return false; + } + if (pos >= state.eMarks[nextLine]) { + return false; + } + secondCh = state.src.charCodeAt(pos++); + if (secondCh !== 124 /* | */ && secondCh !== 45 /* - */ && secondCh !== 58 /* : */ && !isSpace$a(secondCh)) { + return false; + } + // if first character is '-', then second character must not be a space + // (due to parsing ambiguity with list) + if (firstCh === 45 /* - */ && isSpace$a(secondCh)) { + return false; + } + while (pos < state.eMarks[nextLine]) { + ch = state.src.charCodeAt(pos); + if (ch !== 124 /* | */ && ch !== 45 /* - */ && ch !== 58 /* : */ && !isSpace$a(ch)) { + return false; + } + pos++; + } + lineText = getLine(state, startLine + 1); + columns = lineText.split("|"); + aligns = []; + for (i = 0; i < columns.length; i++) { + t = columns[i].trim(); + if (!t) { + // allow empty columns before and after table, but not in between columns; + // e.g. allow ` |---| `, disallow ` ---||--- ` + if (i === 0 || i === columns.length - 1) { + continue; + } else { + return false; + } + } + if (!/^:?-+:?$/.test(t)) { + return false; + } + if (t.charCodeAt(t.length - 1) === 58 /* : */) { + aligns.push(t.charCodeAt(0) === 58 /* : */ ? "center" : "right"); + } else if (t.charCodeAt(0) === 58 /* : */) { + aligns.push("left"); + } else { + aligns.push(""); + } + } + lineText = getLine(state, startLine).trim(); + if (lineText.indexOf("|") === -1) { + return false; + } + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + columns = escapedSplit(lineText); + if (columns.length && columns[0] === "") columns.shift(); + if (columns.length && columns[columns.length - 1] === "") columns.pop(); + // header row will define an amount of columns in the entire table, + // and align row should be exactly the same (the rest of the rows can differ) + columnCount = columns.length; + if (columnCount === 0 || columnCount !== aligns.length) { + return false; + } + if (silent) { + return true; + } + oldParentType = state.parentType; + state.parentType = "table"; + // use 'blockquote' lists for termination because it's + // the most similar to tables + terminatorRules = state.md.block.ruler.getRules("blockquote"); + token = state.push("table_open", "table", 1); + token.map = tableLines = [ startLine, 0 ]; + token = state.push("thead_open", "thead", 1); + token.map = [ startLine, startLine + 1 ]; + token = state.push("tr_open", "tr", 1); + token.map = [ startLine, startLine + 1 ]; + for (i = 0; i < columns.length; i++) { + token = state.push("th_open", "th", 1); + if (aligns[i]) { + token.attrs = [ [ "style", "text-align:" + aligns[i] ] ]; + } + token = state.push("inline", "", 0); + token.content = columns[i].trim(); + token.children = []; + token = state.push("th_close", "th", -1); + } + token = state.push("tr_close", "tr", -1); + token = state.push("thead_close", "thead", -1); + for (nextLine = startLine + 2; nextLine < endLine; nextLine++) { + if (state.sCount[nextLine] < state.blkIndent) { + break; + } + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + break; + } + lineText = getLine(state, nextLine).trim(); + if (!lineText) { + break; + } + if (state.sCount[nextLine] - state.blkIndent >= 4) { + break; + } + columns = escapedSplit(lineText); + if (columns.length && columns[0] === "") columns.shift(); + if (columns.length && columns[columns.length - 1] === "") columns.pop(); + if (nextLine === startLine + 2) { + token = state.push("tbody_open", "tbody", 1); + token.map = tbodyLines = [ startLine + 2, 0 ]; + } + token = state.push("tr_open", "tr", 1); + token.map = [ nextLine, nextLine + 1 ]; + for (i = 0; i < columnCount; i++) { + token = state.push("td_open", "td", 1); + if (aligns[i]) { + token.attrs = [ [ "style", "text-align:" + aligns[i] ] ]; + } + token = state.push("inline", "", 0); + token.content = columns[i] ? columns[i].trim() : ""; + token.children = []; + token = state.push("td_close", "td", -1); + } + token = state.push("tr_close", "tr", -1); + } + if (tbodyLines) { + token = state.push("tbody_close", "tbody", -1); + tbodyLines[1] = nextLine; + } + token = state.push("table_close", "table", -1); + tableLines[1] = nextLine; + state.parentType = oldParentType; + state.line = nextLine; + return true; + }; + // Code block (4 spaces padded) + var code = function code(state, startLine, endLine /*, silent*/) { + var nextLine, last, token; + if (state.sCount[startLine] - state.blkIndent < 4) { + return false; + } + last = nextLine = startLine + 1; + while (nextLine < endLine) { + if (state.isEmpty(nextLine)) { + nextLine++; + continue; + } + if (state.sCount[nextLine] - state.blkIndent >= 4) { + nextLine++; + last = nextLine; + continue; + } + break; + } + state.line = last; + token = state.push("code_block", "code", 0); + token.content = state.getLines(startLine, last, 4 + state.blkIndent, false) + "\n"; + token.map = [ startLine, state.line ]; + return true; + }; + // fences (``` lang, ~~~ lang) + var fence = function fence(state, startLine, endLine, silent) { + var marker, len, params, nextLine, mem, token, markup, haveEndMarker = false, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + if (pos + 3 > max) { + return false; + } + marker = state.src.charCodeAt(pos); + if (marker !== 126 /* ~ */ && marker !== 96 /* ` */) { + return false; + } + // scan marker length + mem = pos; + pos = state.skipChars(pos, marker); + len = pos - mem; + if (len < 3) { + return false; + } + markup = state.src.slice(mem, pos); + params = state.src.slice(pos, max); + if (marker === 96 /* ` */) { + if (params.indexOf(String.fromCharCode(marker)) >= 0) { + return false; + } + } + // Since start is found, we can report success here in validation mode + if (silent) { + return true; + } + // search end of block + nextLine = startLine; + for (;;) { + nextLine++; + if (nextLine >= endLine) { + // unclosed block should be autoclosed by end of document. + // also block seems to be autoclosed by end of parent + break; + } + pos = mem = state.bMarks[nextLine] + state.tShift[nextLine]; + max = state.eMarks[nextLine]; + if (pos < max && state.sCount[nextLine] < state.blkIndent) { + // non-empty line with negative indent should stop the list: + // - ``` + // test + break; + } + if (state.src.charCodeAt(pos) !== marker) { + continue; + } + if (state.sCount[nextLine] - state.blkIndent >= 4) { + // closing fence should be indented less than 4 spaces + continue; + } + pos = state.skipChars(pos, marker); + // closing code fence must be at least as long as the opening one + if (pos - mem < len) { + continue; + } + // make sure tail has spaces only + pos = state.skipSpaces(pos); + if (pos < max) { + continue; + } + haveEndMarker = true; + // found! + break; + } + // If a fence has heading spaces, they should be removed from its inner block + len = state.sCount[startLine]; + state.line = nextLine + (haveEndMarker ? 1 : 0); + token = state.push("fence", "code", 0); + token.info = params; + token.content = state.getLines(startLine + 1, nextLine, len, true); + token.markup = markup; + token.map = [ startLine, state.line ]; + return true; + }; + var isSpace$9 = utils.isSpace; + var blockquote = function blockquote(state, startLine, endLine, silent) { + var adjustTab, ch, i, initial, l, lastLineEmpty, lines, nextLine, offset, oldBMarks, oldBSCount, oldIndent, oldParentType, oldSCount, oldTShift, spaceAfterMarker, terminate, terminatorRules, token, isOutdented, oldLineMax = state.lineMax, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + // check the block quote marker + if (state.src.charCodeAt(pos) !== 62 /* > */) { + return false; + } + // we know that it's going to be a valid blockquote, + // so no point trying to find the end of it in silent mode + if (silent) { + return true; + } + oldBMarks = []; + oldBSCount = []; + oldSCount = []; + oldTShift = []; + terminatorRules = state.md.block.ruler.getRules("blockquote"); + oldParentType = state.parentType; + state.parentType = "blockquote"; + // Search the end of the block + + // Block ends with either: + // 1. an empty line outside: + // ``` + // > test + + // ``` + // 2. an empty line inside: + // ``` + // > + // test + // ``` + // 3. another tag: + // ``` + // > test + // - - - + // ``` + for (nextLine = startLine; nextLine < endLine; nextLine++) { + // check if it's outdented, i.e. it's inside list item and indented + // less than said list item: + // ``` + // 1. anything + // > current blockquote + // 2. checking this line + // ``` + isOutdented = state.sCount[nextLine] < state.blkIndent; + pos = state.bMarks[nextLine] + state.tShift[nextLine]; + max = state.eMarks[nextLine]; + if (pos >= max) { + // Case 1: line is not inside the blockquote, and this line is empty. + break; + } + if (state.src.charCodeAt(pos++) === 62 /* > */ && !isOutdented) { + // This line is inside the blockquote. + // set offset past spaces and ">" + initial = state.sCount[nextLine] + 1; + // skip one optional space after '>' + if (state.src.charCodeAt(pos) === 32 /* space */) { + // ' > test ' + // ^ -- position start of line here: + pos++; + initial++; + adjustTab = false; + spaceAfterMarker = true; + } else if (state.src.charCodeAt(pos) === 9 /* tab */) { + spaceAfterMarker = true; + if ((state.bsCount[nextLine] + initial) % 4 === 3) { + // ' >\t test ' + // ^ -- position start of line here (tab has width===1) + pos++; + initial++; + adjustTab = false; + } else { + // ' >\t test ' + // ^ -- position start of line here + shift bsCount slightly + // to make extra space appear + adjustTab = true; + } + } else { + spaceAfterMarker = false; + } + offset = initial; + oldBMarks.push(state.bMarks[nextLine]); + state.bMarks[nextLine] = pos; + while (pos < max) { + ch = state.src.charCodeAt(pos); + if (isSpace$9(ch)) { + if (ch === 9) { + offset += 4 - (offset + state.bsCount[nextLine] + (adjustTab ? 1 : 0)) % 4; + } else { + offset++; + } + } else { + break; + } + pos++; + } + lastLineEmpty = pos >= max; + oldBSCount.push(state.bsCount[nextLine]); + state.bsCount[nextLine] = state.sCount[nextLine] + 1 + (spaceAfterMarker ? 1 : 0); + oldSCount.push(state.sCount[nextLine]); + state.sCount[nextLine] = offset - initial; + oldTShift.push(state.tShift[nextLine]); + state.tShift[nextLine] = pos - state.bMarks[nextLine]; + continue; + } + // Case 2: line is not inside the blockquote, and the last line was empty. + if (lastLineEmpty) { + break; + } + // Case 3: another tag found. + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + // Quirk to enforce "hard termination mode" for paragraphs; + // normally if you call `tokenize(state, startLine, nextLine)`, + // paragraphs will look below nextLine for paragraph continuation, + // but if blockquote is terminated by another tag, they shouldn't + state.lineMax = nextLine; + if (state.blkIndent !== 0) { + // state.blkIndent was non-zero, we now set it to zero, + // so we need to re-calculate all offsets to appear as + // if indent wasn't changed + oldBMarks.push(state.bMarks[nextLine]); + oldBSCount.push(state.bsCount[nextLine]); + oldTShift.push(state.tShift[nextLine]); + oldSCount.push(state.sCount[nextLine]); + state.sCount[nextLine] -= state.blkIndent; + } + break; + } + oldBMarks.push(state.bMarks[nextLine]); + oldBSCount.push(state.bsCount[nextLine]); + oldTShift.push(state.tShift[nextLine]); + oldSCount.push(state.sCount[nextLine]); + // A negative indentation means that this is a paragraph continuation + + state.sCount[nextLine] = -1; + } + oldIndent = state.blkIndent; + state.blkIndent = 0; + token = state.push("blockquote_open", "blockquote", 1); + token.markup = ">"; + token.map = lines = [ startLine, 0 ]; + state.md.block.tokenize(state, startLine, nextLine); + token = state.push("blockquote_close", "blockquote", -1); + token.markup = ">"; + state.lineMax = oldLineMax; + state.parentType = oldParentType; + lines[1] = state.line; + // Restore original tShift; this might not be necessary since the parser + // has already been here, but just to make sure we can do that. + for (i = 0; i < oldTShift.length; i++) { + state.bMarks[i + startLine] = oldBMarks[i]; + state.tShift[i + startLine] = oldTShift[i]; + state.sCount[i + startLine] = oldSCount[i]; + state.bsCount[i + startLine] = oldBSCount[i]; + } + state.blkIndent = oldIndent; + return true; + }; + var isSpace$8 = utils.isSpace; + var hr = function hr(state, startLine, endLine, silent) { + var marker, cnt, ch, token, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + marker = state.src.charCodeAt(pos++); + // Check hr marker + if (marker !== 42 /* * */ && marker !== 45 /* - */ && marker !== 95 /* _ */) { + return false; + } + // markers can be mixed with spaces, but there should be at least 3 of them + cnt = 1; + while (pos < max) { + ch = state.src.charCodeAt(pos++); + if (ch !== marker && !isSpace$8(ch)) { + return false; + } + if (ch === marker) { + cnt++; + } + } + if (cnt < 3) { + return false; + } + if (silent) { + return true; + } + state.line = startLine + 1; + token = state.push("hr", "hr", 0); + token.map = [ startLine, state.line ]; + token.markup = Array(cnt + 1).join(String.fromCharCode(marker)); + return true; + }; + var isSpace$7 = utils.isSpace; + // Search `[-+*][\n ]`, returns next pos after marker on success + // or -1 on fail. + function skipBulletListMarker(state, startLine) { + var marker, pos, max, ch; + pos = state.bMarks[startLine] + state.tShift[startLine]; + max = state.eMarks[startLine]; + marker = state.src.charCodeAt(pos++); + // Check bullet + if (marker !== 42 /* * */ && marker !== 45 /* - */ && marker !== 43 /* + */) { + return -1; + } + if (pos < max) { + ch = state.src.charCodeAt(pos); + if (!isSpace$7(ch)) { + // " -test " - is not a list item + return -1; + } + } + return pos; + } + // Search `\d+[.)][\n ]`, returns next pos after marker on success + // or -1 on fail. + function skipOrderedListMarker(state, startLine) { + var ch, start = state.bMarks[startLine] + state.tShift[startLine], pos = start, max = state.eMarks[startLine]; + // List marker should have at least 2 chars (digit + dot) + if (pos + 1 >= max) { + return -1; + } + ch = state.src.charCodeAt(pos++); + if (ch < 48 /* 0 */ || ch > 57 /* 9 */) { + return -1; + } + for (;;) { + // EOL -> fail + if (pos >= max) { + return -1; + } + ch = state.src.charCodeAt(pos++); + if (ch >= 48 /* 0 */ && ch <= 57 /* 9 */) { + // List marker should have no more than 9 digits + // (prevents integer overflow in browsers) + if (pos - start >= 10) { + return -1; + } + continue; + } + // found valid marker + if (ch === 41 /* ) */ || ch === 46 /* . */) { + break; + } + return -1; + } + if (pos < max) { + ch = state.src.charCodeAt(pos); + if (!isSpace$7(ch)) { + // " 1.test " - is not a list item + return -1; + } + } + return pos; + } + function markTightParagraphs(state, idx) { + var i, l, level = state.level + 2; + for (i = idx + 2, l = state.tokens.length - 2; i < l; i++) { + if (state.tokens[i].level === level && state.tokens[i].type === "paragraph_open") { + state.tokens[i + 2].hidden = true; + state.tokens[i].hidden = true; + i += 2; + } + } + } + var list = function list(state, startLine, endLine, silent) { + var ch, contentStart, i, indent, indentAfterMarker, initial, isOrdered, itemLines, l, listLines, listTokIdx, markerCharCode, markerValue, max, offset, oldListIndent, oldParentType, oldSCount, oldTShift, oldTight, pos, posAfterMarker, prevEmptyEnd, start, terminate, terminatorRules, token, nextLine = startLine, isTerminatingParagraph = false, tight = true; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[nextLine] - state.blkIndent >= 4) { + return false; + } + // Special case: + // - item 1 + // - item 2 + // - item 3 + // - item 4 + // - this one is a paragraph continuation + if (state.listIndent >= 0 && state.sCount[nextLine] - state.listIndent >= 4 && state.sCount[nextLine] < state.blkIndent) { + return false; + } + // limit conditions when list can interrupt + // a paragraph (validation mode only) + if (silent && state.parentType === "paragraph") { + // Next list item should still terminate previous list item; + // This code can fail if plugins use blkIndent as well as lists, + // but I hope the spec gets fixed long before that happens. + if (state.sCount[nextLine] >= state.blkIndent) { + isTerminatingParagraph = true; + } + } + // Detect list type and position after marker + if ((posAfterMarker = skipOrderedListMarker(state, nextLine)) >= 0) { + isOrdered = true; + start = state.bMarks[nextLine] + state.tShift[nextLine]; + markerValue = Number(state.src.slice(start, posAfterMarker - 1)); + // If we're starting a new ordered list right after + // a paragraph, it should start with 1. + if (isTerminatingParagraph && markerValue !== 1) return false; + } else if ((posAfterMarker = skipBulletListMarker(state, nextLine)) >= 0) { + isOrdered = false; + } else { + return false; + } + // If we're starting a new unordered list right after + // a paragraph, first line should not be empty. + if (isTerminatingParagraph) { + if (state.skipSpaces(posAfterMarker) >= state.eMarks[nextLine]) return false; + } + // For validation mode we can terminate immediately + if (silent) { + return true; + } + // We should terminate list on style change. Remember first one to compare. + markerCharCode = state.src.charCodeAt(posAfterMarker - 1); + // Start list + listTokIdx = state.tokens.length; + if (isOrdered) { + token = state.push("ordered_list_open", "ol", 1); + if (markerValue !== 1) { + token.attrs = [ [ "start", markerValue ] ]; + } + } else { + token = state.push("bullet_list_open", "ul", 1); + } + token.map = listLines = [ nextLine, 0 ]; + token.markup = String.fromCharCode(markerCharCode); + + // Iterate list items + + prevEmptyEnd = false; + terminatorRules = state.md.block.ruler.getRules("list"); + oldParentType = state.parentType; + state.parentType = "list"; + while (nextLine < endLine) { + pos = posAfterMarker; + max = state.eMarks[nextLine]; + initial = offset = state.sCount[nextLine] + posAfterMarker - (state.bMarks[nextLine] + state.tShift[nextLine]); + while (pos < max) { + ch = state.src.charCodeAt(pos); + if (ch === 9) { + offset += 4 - (offset + state.bsCount[nextLine]) % 4; + } else if (ch === 32) { + offset++; + } else { + break; + } + pos++; + } + contentStart = pos; + if (contentStart >= max) { + // trimming space in "- \n 3" case, indent is 1 here + indentAfterMarker = 1; + } else { + indentAfterMarker = offset - initial; + } + // If we have more than 4 spaces, the indent is 1 + // (the rest is just indented code block) + if (indentAfterMarker > 4) { + indentAfterMarker = 1; + } + // " - test" + // ^^^^^ - calculating total length of this thing + indent = initial + indentAfterMarker; + // Run subparser & write tokens + token = state.push("list_item_open", "li", 1); + token.markup = String.fromCharCode(markerCharCode); + token.map = itemLines = [ nextLine, 0 ]; + if (isOrdered) { + token.info = state.src.slice(start, posAfterMarker - 1); + } + // change current state, then restore it after parser subcall + oldTight = state.tight; + oldTShift = state.tShift[nextLine]; + oldSCount = state.sCount[nextLine]; + // - example list + // ^ listIndent position will be here + // ^ blkIndent position will be here + + oldListIndent = state.listIndent; + state.listIndent = state.blkIndent; + state.blkIndent = indent; + state.tight = true; + state.tShift[nextLine] = contentStart - state.bMarks[nextLine]; + state.sCount[nextLine] = offset; + if (contentStart >= max && state.isEmpty(nextLine + 1)) { + // workaround for this case + // (list item is empty, list terminates before "foo"): + // ~~~~~~~~ + // - + // foo + // ~~~~~~~~ + state.line = Math.min(state.line + 2, endLine); + } else { + state.md.block.tokenize(state, nextLine, endLine, true); + } + // If any of list item is tight, mark list as tight + if (!state.tight || prevEmptyEnd) { + tight = false; + } + // Item become loose if finish with empty line, + // but we should filter last element, because it means list finish + prevEmptyEnd = state.line - nextLine > 1 && state.isEmpty(state.line - 1); + state.blkIndent = state.listIndent; + state.listIndent = oldListIndent; + state.tShift[nextLine] = oldTShift; + state.sCount[nextLine] = oldSCount; + state.tight = oldTight; + token = state.push("list_item_close", "li", -1); + token.markup = String.fromCharCode(markerCharCode); + nextLine = state.line; + itemLines[1] = nextLine; + if (nextLine >= endLine) { + break; + } + + // Try to check if list is terminated or continued. + + if (state.sCount[nextLine] < state.blkIndent) { + break; + } + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[nextLine] - state.blkIndent >= 4) { + break; + } + // fail if terminating block found + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + break; + } + // fail if list has another type + if (isOrdered) { + posAfterMarker = skipOrderedListMarker(state, nextLine); + if (posAfterMarker < 0) { + break; + } + start = state.bMarks[nextLine] + state.tShift[nextLine]; + } else { + posAfterMarker = skipBulletListMarker(state, nextLine); + if (posAfterMarker < 0) { + break; + } + } + if (markerCharCode !== state.src.charCodeAt(posAfterMarker - 1)) { + break; + } + } + // Finalize list + if (isOrdered) { + token = state.push("ordered_list_close", "ol", -1); + } else { + token = state.push("bullet_list_close", "ul", -1); + } + token.markup = String.fromCharCode(markerCharCode); + listLines[1] = nextLine; + state.line = nextLine; + state.parentType = oldParentType; + // mark paragraphs tight if needed + if (tight) { + markTightParagraphs(state, listTokIdx); + } + return true; + }; + var normalizeReference$2 = utils.normalizeReference; + var isSpace$6 = utils.isSpace; + var reference = function reference(state, startLine, _endLine, silent) { + var ch, destEndPos, destEndLineNo, endLine, href, i, l, label, labelEnd, oldParentType, res, start, str, terminate, terminatorRules, title, lines = 0, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine], nextLine = startLine + 1; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + if (state.src.charCodeAt(pos) !== 91 /* [ */) { + return false; + } + // Simple check to quickly interrupt scan on [link](url) at the start of line. + // Can be useful on practice: https://github.com/markdown-it/markdown-it/issues/54 + while (++pos < max) { + if (state.src.charCodeAt(pos) === 93 /* ] */ && state.src.charCodeAt(pos - 1) !== 92 /* \ */) { + if (pos + 1 === max) { + return false; + } + if (state.src.charCodeAt(pos + 1) !== 58 /* : */) { + return false; + } + break; + } + } + endLine = state.lineMax; + // jump line-by-line until empty one or EOF + terminatorRules = state.md.block.ruler.getRules("reference"); + oldParentType = state.parentType; + state.parentType = "reference"; + for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { + // this would be a code block normally, but after paragraph + // it's considered a lazy continuation regardless of what's there + if (state.sCount[nextLine] - state.blkIndent > 3) { + continue; + } + // quirk for blockquotes, this line should already be checked by that rule + if (state.sCount[nextLine] < 0) { + continue; + } + // Some tags can terminate paragraph without empty line. + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + break; + } + } + str = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); + max = str.length; + for (pos = 1; pos < max; pos++) { + ch = str.charCodeAt(pos); + if (ch === 91 /* [ */) { + return false; + } else if (ch === 93 /* ] */) { + labelEnd = pos; + break; + } else if (ch === 10 /* \n */) { + lines++; + } else if (ch === 92 /* \ */) { + pos++; + if (pos < max && str.charCodeAt(pos) === 10) { + lines++; + } + } + } + if (labelEnd < 0 || str.charCodeAt(labelEnd + 1) !== 58 /* : */) { + return false; + } + // [label]: destination 'title' + // ^^^ skip optional whitespace here + for (pos = labelEnd + 2; pos < max; pos++) { + ch = str.charCodeAt(pos); + if (ch === 10) { + lines++; + } else if (isSpace$6(ch)) ; else { + break; + } + } + // [label]: destination 'title' + // ^^^^^^^^^^^ parse this + res = state.md.helpers.parseLinkDestination(str, pos, max); + if (!res.ok) { + return false; + } + href = state.md.normalizeLink(res.str); + if (!state.md.validateLink(href)) { + return false; + } + pos = res.pos; + lines += res.lines; + // save cursor state, we could require to rollback later + destEndPos = pos; + destEndLineNo = lines; + // [label]: destination 'title' + // ^^^ skipping those spaces + start = pos; + for (;pos < max; pos++) { + ch = str.charCodeAt(pos); + if (ch === 10) { + lines++; + } else if (isSpace$6(ch)) ; else { + break; + } + } + // [label]: destination 'title' + // ^^^^^^^ parse this + res = state.md.helpers.parseLinkTitle(str, pos, max); + if (pos < max && start !== pos && res.ok) { + title = res.str; + pos = res.pos; + lines += res.lines; + } else { + title = ""; + pos = destEndPos; + lines = destEndLineNo; + } + // skip trailing spaces until the rest of the line + while (pos < max) { + ch = str.charCodeAt(pos); + if (!isSpace$6(ch)) { + break; + } + pos++; + } + if (pos < max && str.charCodeAt(pos) !== 10) { + if (title) { + // garbage at the end of the line after title, + // but it could still be a valid reference if we roll back + title = ""; + pos = destEndPos; + lines = destEndLineNo; + while (pos < max) { + ch = str.charCodeAt(pos); + if (!isSpace$6(ch)) { + break; + } + pos++; + } + } + } + if (pos < max && str.charCodeAt(pos) !== 10) { + // garbage at the end of the line + return false; + } + label = normalizeReference$2(str.slice(1, labelEnd)); + if (!label) { + // CommonMark 0.20 disallows empty labels + return false; + } + // Reference can not terminate anything. This check is for safety only. + /*istanbul ignore if*/ if (silent) { + return true; + } + if (typeof state.env.references === "undefined") { + state.env.references = {}; + } + if (typeof state.env.references[label] === "undefined") { + state.env.references[label] = { + title: title, + href: href + }; + } + state.parentType = oldParentType; + state.line = startLine + lines + 1; + return true; + }; + // List of valid html blocks names, accorting to commonmark spec + var html_blocks = [ "address", "article", "aside", "base", "basefont", "blockquote", "body", "caption", "center", "col", "colgroup", "dd", "details", "dialog", "dir", "div", "dl", "dt", "fieldset", "figcaption", "figure", "footer", "form", "frame", "frameset", "h1", "h2", "h3", "h4", "h5", "h6", "head", "header", "hr", "html", "iframe", "legend", "li", "link", "main", "menu", "menuitem", "nav", "noframes", "ol", "optgroup", "option", "p", "param", "section", "source", "summary", "table", "tbody", "td", "tfoot", "th", "thead", "title", "tr", "track", "ul" ]; + // Regexps to match html elements + var attr_name = "[a-zA-Z_:][a-zA-Z0-9:._-]*"; + var unquoted = "[^\"'=<>`\\x00-\\x20]+"; + var single_quoted = "'[^']*'"; + var double_quoted = '"[^"]*"'; + var attr_value = "(?:" + unquoted + "|" + single_quoted + "|" + double_quoted + ")"; + var attribute = "(?:\\s+" + attr_name + "(?:\\s*=\\s*" + attr_value + ")?)"; + var open_tag = "<[A-Za-z][A-Za-z0-9\\-]*" + attribute + "*\\s*\\/?>"; + var close_tag = "<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>"; + var comment = "\x3c!----\x3e|\x3c!--(?:-?[^>-])(?:-?[^-])*--\x3e"; + var processing = "<[?][\\s\\S]*?[?]>"; + var declaration = "]*>"; + var cdata = ""; + var HTML_TAG_RE$1 = new RegExp("^(?:" + open_tag + "|" + close_tag + "|" + comment + "|" + processing + "|" + declaration + "|" + cdata + ")"); + var HTML_OPEN_CLOSE_TAG_RE$1 = new RegExp("^(?:" + open_tag + "|" + close_tag + ")"); + var HTML_TAG_RE_1 = HTML_TAG_RE$1; + var HTML_OPEN_CLOSE_TAG_RE_1 = HTML_OPEN_CLOSE_TAG_RE$1; + var html_re = { + HTML_TAG_RE: HTML_TAG_RE_1, + HTML_OPEN_CLOSE_TAG_RE: HTML_OPEN_CLOSE_TAG_RE_1 + }; + var HTML_OPEN_CLOSE_TAG_RE = html_re.HTML_OPEN_CLOSE_TAG_RE; + // An array of opening and corresponding closing sequences for html tags, + // last argument defines whether it can terminate a paragraph or not + + var HTML_SEQUENCES = [ [ /^<(script|pre|style|textarea)(?=(\s|>|$))/i, /<\/(script|pre|style|textarea)>/i, true ], [ /^/, true ], [ /^<\?/, /\?>/, true ], [ /^/, true ], [ /^/, true ], [ new RegExp("^|$))", "i"), /^$/, true ], [ new RegExp(HTML_OPEN_CLOSE_TAG_RE.source + "\\s*$"), /^$/, false ] ]; + var html_block = function html_block(state, startLine, endLine, silent) { + var i, nextLine, token, lineText, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + if (!state.md.options.html) { + return false; + } + if (state.src.charCodeAt(pos) !== 60 /* < */) { + return false; + } + lineText = state.src.slice(pos, max); + for (i = 0; i < HTML_SEQUENCES.length; i++) { + if (HTML_SEQUENCES[i][0].test(lineText)) { + break; + } + } + if (i === HTML_SEQUENCES.length) { + return false; + } + if (silent) { + // true if this sequence can be a terminator, false otherwise + return HTML_SEQUENCES[i][2]; + } + nextLine = startLine + 1; + // If we are here - we detected HTML block. + // Let's roll down till block end. + if (!HTML_SEQUENCES[i][1].test(lineText)) { + for (;nextLine < endLine; nextLine++) { + if (state.sCount[nextLine] < state.blkIndent) { + break; + } + pos = state.bMarks[nextLine] + state.tShift[nextLine]; + max = state.eMarks[nextLine]; + lineText = state.src.slice(pos, max); + if (HTML_SEQUENCES[i][1].test(lineText)) { + if (lineText.length !== 0) { + nextLine++; + } + break; + } + } + } + state.line = nextLine; + token = state.push("html_block", "", 0); + token.map = [ startLine, nextLine ]; + token.content = state.getLines(startLine, nextLine, state.blkIndent, true); + return true; + }; + var isSpace$5 = utils.isSpace; + var heading = function heading(state, startLine, endLine, silent) { + var ch, level, tmp, token, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + ch = state.src.charCodeAt(pos); + if (ch !== 35 /* # */ || pos >= max) { + return false; + } + // count heading level + level = 1; + ch = state.src.charCodeAt(++pos); + while (ch === 35 /* # */ && pos < max && level <= 6) { + level++; + ch = state.src.charCodeAt(++pos); + } + if (level > 6 || pos < max && !isSpace$5(ch)) { + return false; + } + if (silent) { + return true; + } + // Let's cut tails like ' ### ' from the end of string + max = state.skipSpacesBack(max, pos); + tmp = state.skipCharsBack(max, 35, pos); + // # + if (tmp > pos && isSpace$5(state.src.charCodeAt(tmp - 1))) { + max = tmp; + } + state.line = startLine + 1; + token = state.push("heading_open", "h" + String(level), 1); + token.markup = "########".slice(0, level); + token.map = [ startLine, state.line ]; + token = state.push("inline", "", 0); + token.content = state.src.slice(pos, max).trim(); + token.map = [ startLine, state.line ]; + token.children = []; + token = state.push("heading_close", "h" + String(level), -1); + token.markup = "########".slice(0, level); + return true; + }; + // lheading (---, ===) + var lheading = function lheading(state, startLine, endLine /*, silent*/) { + var content, terminate, i, l, token, pos, max, level, marker, nextLine = startLine + 1, oldParentType, terminatorRules = state.md.block.ruler.getRules("paragraph"); + // if it's indented more than 3 spaces, it should be a code block + if (state.sCount[startLine] - state.blkIndent >= 4) { + return false; + } + oldParentType = state.parentType; + state.parentType = "paragraph"; + // use paragraph to match terminatorRules + // jump line-by-line until empty one or EOF + for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { + // this would be a code block normally, but after paragraph + // it's considered a lazy continuation regardless of what's there + if (state.sCount[nextLine] - state.blkIndent > 3) { + continue; + } + + // Check for underline in setext header + + if (state.sCount[nextLine] >= state.blkIndent) { + pos = state.bMarks[nextLine] + state.tShift[nextLine]; + max = state.eMarks[nextLine]; + if (pos < max) { + marker = state.src.charCodeAt(pos); + if (marker === 45 /* - */ || marker === 61 /* = */) { + pos = state.skipChars(pos, marker); + pos = state.skipSpaces(pos); + if (pos >= max) { + level = marker === 61 /* = */ ? 1 : 2; + break; + } + } + } + } + // quirk for blockquotes, this line should already be checked by that rule + if (state.sCount[nextLine] < 0) { + continue; + } + // Some tags can terminate paragraph without empty line. + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + break; + } + } + if (!level) { + // Didn't find valid underline + return false; + } + content = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); + state.line = nextLine + 1; + token = state.push("heading_open", "h" + String(level), 1); + token.markup = String.fromCharCode(marker); + token.map = [ startLine, state.line ]; + token = state.push("inline", "", 0); + token.content = content; + token.map = [ startLine, state.line - 1 ]; + token.children = []; + token = state.push("heading_close", "h" + String(level), -1); + token.markup = String.fromCharCode(marker); + state.parentType = oldParentType; + return true; + }; + // Paragraph + var paragraph = function paragraph(state, startLine, endLine) { + var content, terminate, i, l, token, oldParentType, nextLine = startLine + 1, terminatorRules = state.md.block.ruler.getRules("paragraph"); + oldParentType = state.parentType; + state.parentType = "paragraph"; + // jump line-by-line until empty one or EOF + for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { + // this would be a code block normally, but after paragraph + // it's considered a lazy continuation regardless of what's there + if (state.sCount[nextLine] - state.blkIndent > 3) { + continue; + } + // quirk for blockquotes, this line should already be checked by that rule + if (state.sCount[nextLine] < 0) { + continue; + } + // Some tags can terminate paragraph without empty line. + terminate = false; + for (i = 0, l = terminatorRules.length; i < l; i++) { + if (terminatorRules[i](state, nextLine, endLine, true)) { + terminate = true; + break; + } + } + if (terminate) { + break; + } + } + content = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); + state.line = nextLine; + token = state.push("paragraph_open", "p", 1); + token.map = [ startLine, state.line ]; + token = state.push("inline", "", 0); + token.content = content; + token.map = [ startLine, state.line ]; + token.children = []; + token = state.push("paragraph_close", "p", -1); + state.parentType = oldParentType; + return true; + }; + var isSpace$4 = utils.isSpace; + function StateBlock(src, md, env, tokens) { + var ch, s, start, pos, len, indent, offset, indent_found; + this.src = src; + // link to parser instance + this.md = md; + this.env = env; + + // Internal state vartiables + + this.tokens = tokens; + this.bMarks = []; + // line begin offsets for fast jumps + this.eMarks = []; + // line end offsets for fast jumps + this.tShift = []; + // offsets of the first non-space characters (tabs not expanded) + this.sCount = []; + // indents for each line (tabs expanded) + // An amount of virtual spaces (tabs expanded) between beginning + // of each line (bMarks) and real beginning of that line. + + // It exists only as a hack because blockquotes override bMarks + // losing information in the process. + + // It's used only when expanding tabs, you can think about it as + // an initial tab length, e.g. bsCount=21 applied to string `\t123` + // means first tab should be expanded to 4-21%4 === 3 spaces. + + this.bsCount = []; + // block parser variables + this.blkIndent = 0; + // required block content indent (for example, if we are + // inside a list, it would be positioned after list marker) + this.line = 0; + // line index in src + this.lineMax = 0; + // lines count + this.tight = false; + // loose/tight mode for lists + this.ddIndent = -1; + // indent of the current dd block (-1 if there isn't any) + this.listIndent = -1; + // indent of the current list block (-1 if there isn't any) + // can be 'blockquote', 'list', 'root', 'paragraph' or 'reference' + // used in lists to determine if they interrupt a paragraph + this.parentType = "root"; + this.level = 0; + // renderer + this.result = ""; + // Create caches + // Generate markers. + s = this.src; + indent_found = false; + for (start = pos = indent = offset = 0, len = s.length; pos < len; pos++) { + ch = s.charCodeAt(pos); + if (!indent_found) { + if (isSpace$4(ch)) { + indent++; + if (ch === 9) { + offset += 4 - offset % 4; + } else { + offset++; + } + continue; + } else { + indent_found = true; + } + } + if (ch === 10 || pos === len - 1) { + if (ch !== 10) { + pos++; + } + this.bMarks.push(start); + this.eMarks.push(pos); + this.tShift.push(indent); + this.sCount.push(offset); + this.bsCount.push(0); + indent_found = false; + indent = 0; + offset = 0; + start = pos + 1; + } + } + // Push fake entry to simplify cache bounds checks + this.bMarks.push(s.length); + this.eMarks.push(s.length); + this.tShift.push(0); + this.sCount.push(0); + this.bsCount.push(0); + this.lineMax = this.bMarks.length - 1; + // don't count last fake line + } + // Push new token to "stream". + + StateBlock.prototype.push = function(type, tag, nesting) { + var token$1 = new token(type, tag, nesting); + token$1.block = true; + if (nesting < 0) this.level--; + // closing tag + token$1.level = this.level; + if (nesting > 0) this.level++; + // opening tag + this.tokens.push(token$1); + return token$1; + }; + StateBlock.prototype.isEmpty = function isEmpty(line) { + return this.bMarks[line] + this.tShift[line] >= this.eMarks[line]; + }; + StateBlock.prototype.skipEmptyLines = function skipEmptyLines(from) { + for (var max = this.lineMax; from < max; from++) { + if (this.bMarks[from] + this.tShift[from] < this.eMarks[from]) { + break; + } + } + return from; + }; + // Skip spaces from given position. + StateBlock.prototype.skipSpaces = function skipSpaces(pos) { + var ch; + for (var max = this.src.length; pos < max; pos++) { + ch = this.src.charCodeAt(pos); + if (!isSpace$4(ch)) { + break; + } + } + return pos; + }; + // Skip spaces from given position in reverse. + StateBlock.prototype.skipSpacesBack = function skipSpacesBack(pos, min) { + if (pos <= min) { + return pos; + } + while (pos > min) { + if (!isSpace$4(this.src.charCodeAt(--pos))) { + return pos + 1; + } + } + return pos; + }; + // Skip char codes from given position + StateBlock.prototype.skipChars = function skipChars(pos, code) { + for (var max = this.src.length; pos < max; pos++) { + if (this.src.charCodeAt(pos) !== code) { + break; + } + } + return pos; + }; + // Skip char codes reverse from given position - 1 + StateBlock.prototype.skipCharsBack = function skipCharsBack(pos, code, min) { + if (pos <= min) { + return pos; + } + while (pos > min) { + if (code !== this.src.charCodeAt(--pos)) { + return pos + 1; + } + } + return pos; + }; + // cut lines range from source. + StateBlock.prototype.getLines = function getLines(begin, end, indent, keepLastLF) { + var i, lineIndent, ch, first, last, queue, lineStart, line = begin; + if (begin >= end) { + return ""; + } + queue = new Array(end - begin); + for (i = 0; line < end; line++, i++) { + lineIndent = 0; + lineStart = first = this.bMarks[line]; + if (line + 1 < end || keepLastLF) { + // No need for bounds check because we have fake entry on tail. + last = this.eMarks[line] + 1; + } else { + last = this.eMarks[line]; + } + while (first < last && lineIndent < indent) { + ch = this.src.charCodeAt(first); + if (isSpace$4(ch)) { + if (ch === 9) { + lineIndent += 4 - (lineIndent + this.bsCount[line]) % 4; + } else { + lineIndent++; + } + } else if (first - lineStart < this.tShift[line]) { + // patched tShift masked characters to look like spaces (blockquotes, list markers) + lineIndent++; + } else { + break; + } + first++; + } + if (lineIndent > indent) { + // partially expanding tabs in code blocks, e.g '\t\tfoobar' + // with indent=2 becomes ' \tfoobar' + queue[i] = new Array(lineIndent - indent + 1).join(" ") + this.src.slice(first, last); + } else { + queue[i] = this.src.slice(first, last); + } + } + return queue.join(""); + }; + // re-export Token class to use in block rules + StateBlock.prototype.Token = token; + var state_block = StateBlock; + var _rules$1 = [ + // First 2 params - rule name & source. Secondary array - list of rules, + // which can be terminated by this one. + [ "table", table, [ "paragraph", "reference" ] ], [ "code", code ], [ "fence", fence, [ "paragraph", "reference", "blockquote", "list" ] ], [ "blockquote", blockquote, [ "paragraph", "reference", "blockquote", "list" ] ], [ "hr", hr, [ "paragraph", "reference", "blockquote", "list" ] ], [ "list", list, [ "paragraph", "reference", "blockquote" ] ], [ "reference", reference ], [ "html_block", html_block, [ "paragraph", "reference", "blockquote" ] ], [ "heading", heading, [ "paragraph", "reference", "blockquote" ] ], [ "lheading", lheading ], [ "paragraph", paragraph ] ]; + /** + * new ParserBlock() + **/ function ParserBlock() { + /** + * ParserBlock#ruler -> Ruler + * + * [[Ruler]] instance. Keep configuration of block rules. + **/ + this.ruler = new ruler; + for (var i = 0; i < _rules$1.length; i++) { + this.ruler.push(_rules$1[i][0], _rules$1[i][1], { + alt: (_rules$1[i][2] || []).slice() + }); + } + } + // Generate tokens for input range + + ParserBlock.prototype.tokenize = function(state, startLine, endLine) { + var ok, i, prevLine, rules = this.ruler.getRules(""), len = rules.length, line = startLine, hasEmptyLines = false, maxNesting = state.md.options.maxNesting; + while (line < endLine) { + state.line = line = state.skipEmptyLines(line); + if (line >= endLine) { + break; + } + // Termination condition for nested calls. + // Nested calls currently used for blockquotes & lists + if (state.sCount[line] < state.blkIndent) { + break; + } + // If nesting level exceeded - skip tail to the end. That's not ordinary + // situation and we should not care about content. + if (state.level >= maxNesting) { + state.line = endLine; + break; + } + // Try all possible rules. + // On success, rule should: + + // - update `state.line` + // - update `state.tokens` + // - return true + prevLine = state.line; + for (i = 0; i < len; i++) { + ok = rules[i](state, line, endLine, false); + if (ok) { + if (prevLine >= state.line) { + throw new Error("block rule didn't increment state.line"); + } + break; + } + } + // this can only happen if user disables paragraph rule + if (!ok) throw new Error("none of the block rules matched"); + // set state.tight if we had an empty line before current tag + // i.e. latest empty line should not count + state.tight = !hasEmptyLines; + // paragraph might "eat" one newline after it in nested lists + if (state.isEmpty(state.line - 1)) { + hasEmptyLines = true; + } + line = state.line; + if (line < endLine && state.isEmpty(line)) { + hasEmptyLines = true; + line++; + state.line = line; + } + } + }; + /** + * ParserBlock.parse(str, md, env, outTokens) + * + * Process input string and push block tokens into `outTokens` + **/ ParserBlock.prototype.parse = function(src, md, env, outTokens) { + var state; + if (!src) { + return; + } + state = new this.State(src, md, env, outTokens); + this.tokenize(state, state.line, state.lineMax); + }; + ParserBlock.prototype.State = state_block; + var parser_block = ParserBlock; + // Skip text characters for text token, place those to pending buffer + // Rule to skip pure text + // '{}$%@~+=:' reserved for extentions + // !, ", #, $, %, &, ', (, ), *, +, ,, -, ., /, :, ;, <, =, >, ?, @, [, \, ], ^, _, `, {, |, }, or ~ + // !!!! Don't confuse with "Markdown ASCII Punctuation" chars + // http://spec.commonmark.org/0.15/#ascii-punctuation-character + function isTerminatorChar(ch) { + switch (ch) { + case 10 /* \n */ : + case 33 /* ! */ : + case 35 /* # */ : + case 36 /* $ */ : + case 37 /* % */ : + case 38 /* & */ : + case 42 /* * */ : + case 43 /* + */ : + case 45 /* - */ : + case 58 /* : */ : + case 60 /* < */ : + case 61 /* = */ : + case 62 /* > */ : + case 64 /* @ */ : + case 91 /* [ */ : + case 92 /* \ */ : + case 93 /* ] */ : + case 94 /* ^ */ : + case 95 /* _ */ : + case 96 /* ` */ : + case 123 /* { */ : + case 125 /* } */ : + case 126 /* ~ */ : + return true; + + default: + return false; + } + } + var text = function text(state, silent) { + var pos = state.pos; + while (pos < state.posMax && !isTerminatorChar(state.src.charCodeAt(pos))) { + pos++; + } + if (pos === state.pos) { + return false; + } + if (!silent) { + state.pending += state.src.slice(state.pos, pos); + } + state.pos = pos; + return true; + }; + // Process links like https://example.org/ + // RFC3986: scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) + var SCHEME_RE = /(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i; + var linkify = function linkify(state, silent) { + var pos, max, match, proto, link, url, fullUrl, token; + if (!state.md.options.linkify) return false; + if (state.linkLevel > 0) return false; + pos = state.pos; + max = state.posMax; + if (pos + 3 > max) return false; + if (state.src.charCodeAt(pos) !== 58 /* : */) return false; + if (state.src.charCodeAt(pos + 1) !== 47 /* / */) return false; + if (state.src.charCodeAt(pos + 2) !== 47 /* / */) return false; + match = state.pending.match(SCHEME_RE); + if (!match) return false; + proto = match[1]; + link = state.md.linkify.matchAtStart(state.src.slice(pos - proto.length)); + if (!link) return false; + url = link.url; + // invalid link, but still detected by linkify somehow; + // need to check to prevent infinite loop below + if (url.length <= proto.length) return false; + // disallow '*' at the end of the link (conflicts with emphasis) + url = url.replace(/\*+$/, ""); + fullUrl = state.md.normalizeLink(url); + if (!state.md.validateLink(fullUrl)) return false; + if (!silent) { + state.pending = state.pending.slice(0, -proto.length); + token = state.push("link_open", "a", 1); + token.attrs = [ [ "href", fullUrl ] ]; + token.markup = "linkify"; + token.info = "auto"; + token = state.push("text", "", 0); + token.content = state.md.normalizeLinkText(url); + token = state.push("link_close", "a", -1); + token.markup = "linkify"; + token.info = "auto"; + } + state.pos += url.length - proto.length; + return true; + }; + var isSpace$3 = utils.isSpace; + var newline = function newline(state, silent) { + var pmax, max, ws, pos = state.pos; + if (state.src.charCodeAt(pos) !== 10 /* \n */) { + return false; + } + pmax = state.pending.length - 1; + max = state.posMax; + // ' \n' -> hardbreak + // Lookup in pending chars is bad practice! Don't copy to other rules! + // Pending string is stored in concat mode, indexed lookups will cause + // convertion to flat mode. + if (!silent) { + if (pmax >= 0 && state.pending.charCodeAt(pmax) === 32) { + if (pmax >= 1 && state.pending.charCodeAt(pmax - 1) === 32) { + // Find whitespaces tail of pending chars. + ws = pmax - 1; + while (ws >= 1 && state.pending.charCodeAt(ws - 1) === 32) ws--; + state.pending = state.pending.slice(0, ws); + state.push("hardbreak", "br", 0); + } else { + state.pending = state.pending.slice(0, -1); + state.push("softbreak", "br", 0); + } + } else { + state.push("softbreak", "br", 0); + } + } + pos++; + // skip heading spaces for next line + while (pos < max && isSpace$3(state.src.charCodeAt(pos))) { + pos++; + } + state.pos = pos; + return true; + }; + var isSpace$2 = utils.isSpace; + var ESCAPED = []; + for (var i = 0; i < 256; i++) { + ESCAPED.push(0); + } + "\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach((function(ch) { + ESCAPED[ch.charCodeAt(0)] = 1; + })); + var _escape = function escape(state, silent) { + var ch1, ch2, origStr, escapedStr, token, pos = state.pos, max = state.posMax; + if (state.src.charCodeAt(pos) !== 92 /* \ */) return false; + pos++; + // '\' at the end of the inline block + if (pos >= max) return false; + ch1 = state.src.charCodeAt(pos); + if (ch1 === 10) { + if (!silent) { + state.push("hardbreak", "br", 0); + } + pos++; + // skip leading whitespaces from next line + while (pos < max) { + ch1 = state.src.charCodeAt(pos); + if (!isSpace$2(ch1)) break; + pos++; + } + state.pos = pos; + return true; + } + escapedStr = state.src[pos]; + if (ch1 >= 55296 && ch1 <= 56319 && pos + 1 < max) { + ch2 = state.src.charCodeAt(pos + 1); + if (ch2 >= 56320 && ch2 <= 57343) { + escapedStr += state.src[pos + 1]; + pos++; + } + } + origStr = "\\" + escapedStr; + if (!silent) { + token = state.push("text_special", "", 0); + if (ch1 < 256 && ESCAPED[ch1] !== 0) { + token.content = escapedStr; + } else { + token.content = origStr; + } + token.markup = origStr; + token.info = "escape"; + } + state.pos = pos + 1; + return true; + }; + // Parse backticks + var backticks = function backtick(state, silent) { + var start, max, marker, token, matchStart, matchEnd, openerLength, closerLength, pos = state.pos, ch = state.src.charCodeAt(pos); + if (ch !== 96 /* ` */) { + return false; + } + start = pos; + pos++; + max = state.posMax; + // scan marker length + while (pos < max && state.src.charCodeAt(pos) === 96 /* ` */) { + pos++; + } + marker = state.src.slice(start, pos); + openerLength = marker.length; + if (state.backticksScanned && (state.backticks[openerLength] || 0) <= start) { + if (!silent) state.pending += marker; + state.pos += openerLength; + return true; + } + matchEnd = pos; + // Nothing found in the cache, scan until the end of the line (or until marker is found) + while ((matchStart = state.src.indexOf("`", matchEnd)) !== -1) { + matchEnd = matchStart + 1; + // scan marker length + while (matchEnd < max && state.src.charCodeAt(matchEnd) === 96 /* ` */) { + matchEnd++; + } + closerLength = matchEnd - matchStart; + if (closerLength === openerLength) { + // Found matching closer length. + if (!silent) { + token = state.push("code_inline", "code", 0); + token.markup = marker; + token.content = state.src.slice(pos, matchStart).replace(/\n/g, " ").replace(/^ (.+) $/, "$1"); + } + state.pos = matchEnd; + return true; + } + // Some different length found, put it in cache as upper limit of where closer can be found + state.backticks[closerLength] = matchStart; + } + // Scanned through the end, didn't find anything + state.backticksScanned = true; + if (!silent) state.pending += marker; + state.pos += openerLength; + return true; + }; + // ~~strike through~~ + // Insert each marker as a separate text token, and add it to delimiter list + + var tokenize$1 = function strikethrough(state, silent) { + var i, scanned, token, len, ch, start = state.pos, marker = state.src.charCodeAt(start); + if (silent) { + return false; + } + if (marker !== 126 /* ~ */) { + return false; + } + scanned = state.scanDelims(state.pos, true); + len = scanned.length; + ch = String.fromCharCode(marker); + if (len < 2) { + return false; + } + if (len % 2) { + token = state.push("text", "", 0); + token.content = ch; + len--; + } + for (i = 0; i < len; i += 2) { + token = state.push("text", "", 0); + token.content = ch + ch; + state.delimiters.push({ + marker: marker, + length: 0, + // disable "rule of 3" length checks meant for emphasis + token: state.tokens.length - 1, + end: -1, + open: scanned.can_open, + close: scanned.can_close + }); + } + state.pos += scanned.length; + return true; + }; + function postProcess$1(state, delimiters) { + var i, j, startDelim, endDelim, token, loneMarkers = [], max = delimiters.length; + for (i = 0; i < max; i++) { + startDelim = delimiters[i]; + if (startDelim.marker !== 126 /* ~ */) { + continue; + } + if (startDelim.end === -1) { + continue; + } + endDelim = delimiters[startDelim.end]; + token = state.tokens[startDelim.token]; + token.type = "s_open"; + token.tag = "s"; + token.nesting = 1; + token.markup = "~~"; + token.content = ""; + token = state.tokens[endDelim.token]; + token.type = "s_close"; + token.tag = "s"; + token.nesting = -1; + token.markup = "~~"; + token.content = ""; + if (state.tokens[endDelim.token - 1].type === "text" && state.tokens[endDelim.token - 1].content === "~") { + loneMarkers.push(endDelim.token - 1); + } + } + // If a marker sequence has an odd number of characters, it's splitted + // like this: `~~~~~` -> `~` + `~~` + `~~`, leaving one marker at the + // start of the sequence. + + // So, we have to move all those markers after subsequent s_close tags. + + while (loneMarkers.length) { + i = loneMarkers.pop(); + j = i + 1; + while (j < state.tokens.length && state.tokens[j].type === "s_close") { + j++; + } + j--; + if (i !== j) { + token = state.tokens[j]; + state.tokens[j] = state.tokens[i]; + state.tokens[i] = token; + } + } + } + // Walk through delimiter list and replace text tokens with tags + + var postProcess_1$1 = function strikethrough(state) { + var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; + postProcess$1(state, state.delimiters); + for (curr = 0; curr < max; curr++) { + if (tokens_meta[curr] && tokens_meta[curr].delimiters) { + postProcess$1(state, tokens_meta[curr].delimiters); + } + } + }; + var strikethrough = { + tokenize: tokenize$1, + postProcess: postProcess_1$1 + }; + // Process *this* and _that_ + // Insert each marker as a separate text token, and add it to delimiter list + + var tokenize = function emphasis(state, silent) { + var i, scanned, token, start = state.pos, marker = state.src.charCodeAt(start); + if (silent) { + return false; + } + if (marker !== 95 /* _ */ && marker !== 42 /* * */) { + return false; + } + scanned = state.scanDelims(state.pos, marker === 42); + for (i = 0; i < scanned.length; i++) { + token = state.push("text", "", 0); + token.content = String.fromCharCode(marker); + state.delimiters.push({ + // Char code of the starting marker (number). + marker: marker, + // Total length of these series of delimiters. + length: scanned.length, + // A position of the token this delimiter corresponds to. + token: state.tokens.length - 1, + // If this delimiter is matched as a valid opener, `end` will be + // equal to its position, otherwise it's `-1`. + end: -1, + // Boolean flags that determine if this delimiter could open or close + // an emphasis. + open: scanned.can_open, + close: scanned.can_close + }); + } + state.pos += scanned.length; + return true; + }; + function postProcess(state, delimiters) { + var i, startDelim, endDelim, token, ch, isStrong, max = delimiters.length; + for (i = max - 1; i >= 0; i--) { + startDelim = delimiters[i]; + if (startDelim.marker !== 95 /* _ */ && startDelim.marker !== 42 /* * */) { + continue; + } + // Process only opening markers + if (startDelim.end === -1) { + continue; + } + endDelim = delimiters[startDelim.end]; + // If the previous delimiter has the same marker and is adjacent to this one, + // merge those into one strong delimiter. + + // `whatever` -> `whatever` + + isStrong = i > 0 && delimiters[i - 1].end === startDelim.end + 1 && + // check that first two markers match and adjacent + delimiters[i - 1].marker === startDelim.marker && delimiters[i - 1].token === startDelim.token - 1 && + // check that last two markers are adjacent (we can safely assume they match) + delimiters[startDelim.end + 1].token === endDelim.token + 1; + ch = String.fromCharCode(startDelim.marker); + token = state.tokens[startDelim.token]; + token.type = isStrong ? "strong_open" : "em_open"; + token.tag = isStrong ? "strong" : "em"; + token.nesting = 1; + token.markup = isStrong ? ch + ch : ch; + token.content = ""; + token = state.tokens[endDelim.token]; + token.type = isStrong ? "strong_close" : "em_close"; + token.tag = isStrong ? "strong" : "em"; + token.nesting = -1; + token.markup = isStrong ? ch + ch : ch; + token.content = ""; + if (isStrong) { + state.tokens[delimiters[i - 1].token].content = ""; + state.tokens[delimiters[startDelim.end + 1].token].content = ""; + i--; + } + } + } + // Walk through delimiter list and replace text tokens with tags + + var postProcess_1 = function emphasis(state) { + var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; + postProcess(state, state.delimiters); + for (curr = 0; curr < max; curr++) { + if (tokens_meta[curr] && tokens_meta[curr].delimiters) { + postProcess(state, tokens_meta[curr].delimiters); + } + } + }; + var emphasis = { + tokenize: tokenize, + postProcess: postProcess_1 + }; + var normalizeReference$1 = utils.normalizeReference; + var isSpace$1 = utils.isSpace; + var link = function link(state, silent) { + var attrs, code, label, labelEnd, labelStart, pos, res, ref, token, href = "", title = "", oldPos = state.pos, max = state.posMax, start = state.pos, parseReference = true; + if (state.src.charCodeAt(state.pos) !== 91 /* [ */) { + return false; + } + labelStart = state.pos + 1; + labelEnd = state.md.helpers.parseLinkLabel(state, state.pos, true); + // parser failed to find ']', so it's not a valid link + if (labelEnd < 0) { + return false; + } + pos = labelEnd + 1; + if (pos < max && state.src.charCodeAt(pos) === 40 /* ( */) { + // Inline link + // might have found a valid shortcut link, disable reference parsing + parseReference = false; + // [link]( "title" ) + // ^^ skipping these spaces + pos++; + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace$1(code) && code !== 10) { + break; + } + } + if (pos >= max) { + return false; + } + // [link]( "title" ) + // ^^^^^^ parsing link destination + start = pos; + res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax); + if (res.ok) { + href = state.md.normalizeLink(res.str); + if (state.md.validateLink(href)) { + pos = res.pos; + } else { + href = ""; + } + // [link]( "title" ) + // ^^ skipping these spaces + start = pos; + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace$1(code) && code !== 10) { + break; + } + } + // [link]( "title" ) + // ^^^^^^^ parsing link title + res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax); + if (pos < max && start !== pos && res.ok) { + title = res.str; + pos = res.pos; + // [link]( "title" ) + // ^^ skipping these spaces + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace$1(code) && code !== 10) { + break; + } + } + } + } + if (pos >= max || state.src.charCodeAt(pos) !== 41 /* ) */) { + // parsing a valid shortcut link failed, fallback to reference + parseReference = true; + } + pos++; + } + if (parseReference) { + // Link reference + if (typeof state.env.references === "undefined") { + return false; + } + if (pos < max && state.src.charCodeAt(pos) === 91 /* [ */) { + start = pos + 1; + pos = state.md.helpers.parseLinkLabel(state, pos); + if (pos >= 0) { + label = state.src.slice(start, pos++); + } else { + pos = labelEnd + 1; + } + } else { + pos = labelEnd + 1; + } + // covers label === '' and label === undefined + // (collapsed reference link and shortcut reference link respectively) + if (!label) { + label = state.src.slice(labelStart, labelEnd); + } + ref = state.env.references[normalizeReference$1(label)]; + if (!ref) { + state.pos = oldPos; + return false; + } + href = ref.href; + title = ref.title; + } + + // We found the end of the link, and know for a fact it's a valid link; + // so all that's left to do is to call tokenizer. + + if (!silent) { + state.pos = labelStart; + state.posMax = labelEnd; + token = state.push("link_open", "a", 1); + token.attrs = attrs = [ [ "href", href ] ]; + if (title) { + attrs.push([ "title", title ]); + } + state.linkLevel++; + state.md.inline.tokenize(state); + state.linkLevel--; + token = state.push("link_close", "a", -1); + } + state.pos = pos; + state.posMax = max; + return true; + }; + var normalizeReference = utils.normalizeReference; + var isSpace = utils.isSpace; + var image = function image(state, silent) { + var attrs, code, content, label, labelEnd, labelStart, pos, ref, res, title, token, tokens, start, href = "", oldPos = state.pos, max = state.posMax; + if (state.src.charCodeAt(state.pos) !== 33 /* ! */) { + return false; + } + if (state.src.charCodeAt(state.pos + 1) !== 91 /* [ */) { + return false; + } + labelStart = state.pos + 2; + labelEnd = state.md.helpers.parseLinkLabel(state, state.pos + 1, false); + // parser failed to find ']', so it's not a valid link + if (labelEnd < 0) { + return false; + } + pos = labelEnd + 1; + if (pos < max && state.src.charCodeAt(pos) === 40 /* ( */) { + // Inline link + // [link]( "title" ) + // ^^ skipping these spaces + pos++; + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace(code) && code !== 10) { + break; + } + } + if (pos >= max) { + return false; + } + // [link]( "title" ) + // ^^^^^^ parsing link destination + start = pos; + res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax); + if (res.ok) { + href = state.md.normalizeLink(res.str); + if (state.md.validateLink(href)) { + pos = res.pos; + } else { + href = ""; + } + } + // [link]( "title" ) + // ^^ skipping these spaces + start = pos; + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace(code) && code !== 10) { + break; + } + } + // [link]( "title" ) + // ^^^^^^^ parsing link title + res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax); + if (pos < max && start !== pos && res.ok) { + title = res.str; + pos = res.pos; + // [link]( "title" ) + // ^^ skipping these spaces + for (;pos < max; pos++) { + code = state.src.charCodeAt(pos); + if (!isSpace(code) && code !== 10) { + break; + } + } + } else { + title = ""; + } + if (pos >= max || state.src.charCodeAt(pos) !== 41 /* ) */) { + state.pos = oldPos; + return false; + } + pos++; + } else { + // Link reference + if (typeof state.env.references === "undefined") { + return false; + } + if (pos < max && state.src.charCodeAt(pos) === 91 /* [ */) { + start = pos + 1; + pos = state.md.helpers.parseLinkLabel(state, pos); + if (pos >= 0) { + label = state.src.slice(start, pos++); + } else { + pos = labelEnd + 1; + } + } else { + pos = labelEnd + 1; + } + // covers label === '' and label === undefined + // (collapsed reference link and shortcut reference link respectively) + if (!label) { + label = state.src.slice(labelStart, labelEnd); + } + ref = state.env.references[normalizeReference(label)]; + if (!ref) { + state.pos = oldPos; + return false; + } + href = ref.href; + title = ref.title; + } + + // We found the end of the link, and know for a fact it's a valid link; + // so all that's left to do is to call tokenizer. + + if (!silent) { + content = state.src.slice(labelStart, labelEnd); + state.md.inline.parse(content, state.md, state.env, tokens = []); + token = state.push("image", "img", 0); + token.attrs = attrs = [ [ "src", href ], [ "alt", "" ] ]; + token.children = tokens; + token.content = content; + if (title) { + attrs.push([ "title", title ]); + } + } + state.pos = pos; + state.posMax = max; + return true; + }; + // Process autolinks '' + /*eslint max-len:0*/ var EMAIL_RE = /^([a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/; + var AUTOLINK_RE = /^([a-zA-Z][a-zA-Z0-9+.\-]{1,31}):([^<>\x00-\x20]*)$/; + var autolink = function autolink(state, silent) { + var url, fullUrl, token, ch, start, max, pos = state.pos; + if (state.src.charCodeAt(pos) !== 60 /* < */) { + return false; + } + start = state.pos; + max = state.posMax; + for (;;) { + if (++pos >= max) return false; + ch = state.src.charCodeAt(pos); + if (ch === 60 /* < */) return false; + if (ch === 62 /* > */) break; + } + url = state.src.slice(start + 1, pos); + if (AUTOLINK_RE.test(url)) { + fullUrl = state.md.normalizeLink(url); + if (!state.md.validateLink(fullUrl)) { + return false; + } + if (!silent) { + token = state.push("link_open", "a", 1); + token.attrs = [ [ "href", fullUrl ] ]; + token.markup = "autolink"; + token.info = "auto"; + token = state.push("text", "", 0); + token.content = state.md.normalizeLinkText(url); + token = state.push("link_close", "a", -1); + token.markup = "autolink"; + token.info = "auto"; + } + state.pos += url.length + 2; + return true; + } + if (EMAIL_RE.test(url)) { + fullUrl = state.md.normalizeLink("mailto:" + url); + if (!state.md.validateLink(fullUrl)) { + return false; + } + if (!silent) { + token = state.push("link_open", "a", 1); + token.attrs = [ [ "href", fullUrl ] ]; + token.markup = "autolink"; + token.info = "auto"; + token = state.push("text", "", 0); + token.content = state.md.normalizeLinkText(url); + token = state.push("link_close", "a", -1); + token.markup = "autolink"; + token.info = "auto"; + } + state.pos += url.length + 2; + return true; + } + return false; + }; + var HTML_TAG_RE = html_re.HTML_TAG_RE; + function isLinkOpen(str) { + return /^\s]/i.test(str); + } + function isLinkClose(str) { + return /^<\/a\s*>/i.test(str); + } + function isLetter(ch) { + /*eslint no-bitwise:0*/ + var lc = ch | 32; + // to lower case + return lc >= 97 /* a */ && lc <= 122 /* z */; + } + var html_inline = function html_inline(state, silent) { + var ch, match, max, token, pos = state.pos; + if (!state.md.options.html) { + return false; + } + // Check start + max = state.posMax; + if (state.src.charCodeAt(pos) !== 60 /* < */ || pos + 2 >= max) { + return false; + } + // Quick fail on second char + ch = state.src.charCodeAt(pos + 1); + if (ch !== 33 /* ! */ && ch !== 63 /* ? */ && ch !== 47 /* / */ && !isLetter(ch)) { + return false; + } + match = state.src.slice(pos).match(HTML_TAG_RE); + if (!match) { + return false; + } + if (!silent) { + token = state.push("html_inline", "", 0); + token.content = match[0]; + if (isLinkOpen(token.content)) state.linkLevel++; + if (isLinkClose(token.content)) state.linkLevel--; + } + state.pos += match[0].length; + return true; + }; + var has = utils.has; + var isValidEntityCode = utils.isValidEntityCode; + var fromCodePoint = utils.fromCodePoint; + var DIGITAL_RE = /^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i; + var NAMED_RE = /^&([a-z][a-z0-9]{1,31});/i; + var entity = function entity(state, silent) { + var ch, code, match, token, pos = state.pos, max = state.posMax; + if (state.src.charCodeAt(pos) !== 38 /* & */) return false; + if (pos + 1 >= max) return false; + ch = state.src.charCodeAt(pos + 1); + if (ch === 35 /* # */) { + match = state.src.slice(pos).match(DIGITAL_RE); + if (match) { + if (!silent) { + code = match[1][0].toLowerCase() === "x" ? parseInt(match[1].slice(1), 16) : parseInt(match[1], 10); + token = state.push("text_special", "", 0); + token.content = isValidEntityCode(code) ? fromCodePoint(code) : fromCodePoint(65533); + token.markup = match[0]; + token.info = "entity"; + } + state.pos += match[0].length; + return true; + } + } else { + match = state.src.slice(pos).match(NAMED_RE); + if (match) { + if (has(entities, match[1])) { + if (!silent) { + token = state.push("text_special", "", 0); + token.content = entities[match[1]]; + token.markup = match[0]; + token.info = "entity"; + } + state.pos += match[0].length; + return true; + } + } + } + return false; + }; + // For each opening emphasis-like marker find a matching closing one + function processDelimiters(delimiters) { + var closerIdx, openerIdx, closer, opener, minOpenerIdx, newMinOpenerIdx, isOddMatch, lastJump, openersBottom = {}, max = delimiters.length; + if (!max) return; + // headerIdx is the first delimiter of the current (where closer is) delimiter run + var headerIdx = 0; + var lastTokenIdx = -2; + // needs any value lower than -1 + var jumps = []; + for (closerIdx = 0; closerIdx < max; closerIdx++) { + closer = delimiters[closerIdx]; + jumps.push(0); + // markers belong to same delimiter run if: + // - they have adjacent tokens + // - AND markers are the same + + if (delimiters[headerIdx].marker !== closer.marker || lastTokenIdx !== closer.token - 1) { + headerIdx = closerIdx; + } + lastTokenIdx = closer.token; + // Length is only used for emphasis-specific "rule of 3", + // if it's not defined (in strikethrough or 3rd party plugins), + // we can default it to 0 to disable those checks. + + closer.length = closer.length || 0; + if (!closer.close) continue; + // Previously calculated lower bounds (previous fails) + // for each marker, each delimiter length modulo 3, + // and for whether this closer can be an opener; + // https://github.com/commonmark/cmark/commit/34250e12ccebdc6372b8b49c44fab57c72443460 + if (!openersBottom.hasOwnProperty(closer.marker)) { + openersBottom[closer.marker] = [ -1, -1, -1, -1, -1, -1 ]; + } + minOpenerIdx = openersBottom[closer.marker][(closer.open ? 3 : 0) + closer.length % 3]; + openerIdx = headerIdx - jumps[headerIdx] - 1; + newMinOpenerIdx = openerIdx; + for (;openerIdx > minOpenerIdx; openerIdx -= jumps[openerIdx] + 1) { + opener = delimiters[openerIdx]; + if (opener.marker !== closer.marker) continue; + if (opener.open && opener.end < 0) { + isOddMatch = false; + // from spec: + + // If one of the delimiters can both open and close emphasis, then the + // sum of the lengths of the delimiter runs containing the opening and + // closing delimiters must not be a multiple of 3 unless both lengths + // are multiples of 3. + + if (opener.close || closer.open) { + if ((opener.length + closer.length) % 3 === 0) { + if (opener.length % 3 !== 0 || closer.length % 3 !== 0) { + isOddMatch = true; + } + } + } + if (!isOddMatch) { + // If previous delimiter cannot be an opener, we can safely skip + // the entire sequence in future checks. This is required to make + // sure algorithm has linear complexity (see *_*_*_*_*_... case). + lastJump = openerIdx > 0 && !delimiters[openerIdx - 1].open ? jumps[openerIdx - 1] + 1 : 0; + jumps[closerIdx] = closerIdx - openerIdx + lastJump; + jumps[openerIdx] = lastJump; + closer.open = false; + opener.end = closerIdx; + opener.close = false; + newMinOpenerIdx = -1; + // treat next token as start of run, + // it optimizes skips in **<...>**a**<...>** pathological case + lastTokenIdx = -2; + break; + } + } + } + if (newMinOpenerIdx !== -1) { + // If match for this delimiter run failed, we want to set lower bound for + // future lookups. This is required to make sure algorithm has linear + // complexity. + // See details here: + // https://github.com/commonmark/cmark/issues/178#issuecomment-270417442 + openersBottom[closer.marker][(closer.open ? 3 : 0) + (closer.length || 0) % 3] = newMinOpenerIdx; + } + } + } + var balance_pairs = function link_pairs(state) { + var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; + processDelimiters(state.delimiters); + for (curr = 0; curr < max; curr++) { + if (tokens_meta[curr] && tokens_meta[curr].delimiters) { + processDelimiters(tokens_meta[curr].delimiters); + } + } + }; + // Clean up tokens after emphasis and strikethrough postprocessing: + var fragments_join = function fragments_join(state) { + var curr, last, level = 0, tokens = state.tokens, max = state.tokens.length; + for (curr = last = 0; curr < max; curr++) { + // re-calculate levels after emphasis/strikethrough turns some text nodes + // into opening/closing tags + if (tokens[curr].nesting < 0) level--; + // closing tag + tokens[curr].level = level; + if (tokens[curr].nesting > 0) level++; + // opening tag + if (tokens[curr].type === "text" && curr + 1 < max && tokens[curr + 1].type === "text") { + // collapse two adjacent text nodes + tokens[curr + 1].content = tokens[curr].content + tokens[curr + 1].content; + } else { + if (curr !== last) { + tokens[last] = tokens[curr]; + } + last++; + } + } + if (curr !== last) { + tokens.length = last; + } + }; + var isWhiteSpace = utils.isWhiteSpace; + var isPunctChar = utils.isPunctChar; + var isMdAsciiPunct = utils.isMdAsciiPunct; + function StateInline(src, md, env, outTokens) { + this.src = src; + this.env = env; + this.md = md; + this.tokens = outTokens; + this.tokens_meta = Array(outTokens.length); + this.pos = 0; + this.posMax = this.src.length; + this.level = 0; + this.pending = ""; + this.pendingLevel = 0; + // Stores { start: end } pairs. Useful for backtrack + // optimization of pairs parse (emphasis, strikes). + this.cache = {}; + // List of emphasis-like delimiters for current tag + this.delimiters = []; + // Stack of delimiter lists for upper level tags + this._prev_delimiters = []; + // backtick length => last seen position + this.backticks = {}; + this.backticksScanned = false; + // Counter used to disable inline linkify-it execution + // inside and markdown links + this.linkLevel = 0; + } + // Flush pending text + + StateInline.prototype.pushPending = function() { + var token$1 = new token("text", "", 0); + token$1.content = this.pending; + token$1.level = this.pendingLevel; + this.tokens.push(token$1); + this.pending = ""; + return token$1; + }; + // Push new token to "stream". + // If pending text exists - flush it as text token + + StateInline.prototype.push = function(type, tag, nesting) { + if (this.pending) { + this.pushPending(); + } + var token$1 = new token(type, tag, nesting); + var token_meta = null; + if (nesting < 0) { + // closing tag + this.level--; + this.delimiters = this._prev_delimiters.pop(); + } + token$1.level = this.level; + if (nesting > 0) { + // opening tag + this.level++; + this._prev_delimiters.push(this.delimiters); + this.delimiters = []; + token_meta = { + delimiters: this.delimiters + }; + } + this.pendingLevel = this.level; + this.tokens.push(token$1); + this.tokens_meta.push(token_meta); + return token$1; + }; + // Scan a sequence of emphasis-like markers, and determine whether + // it can start an emphasis sequence or end an emphasis sequence. + + // - start - position to scan from (it should point at a valid marker); + // - canSplitWord - determine if these markers can be found inside a word + + StateInline.prototype.scanDelims = function(start, canSplitWord) { + var pos = start, lastChar, nextChar, count, can_open, can_close, isLastWhiteSpace, isLastPunctChar, isNextWhiteSpace, isNextPunctChar, left_flanking = true, right_flanking = true, max = this.posMax, marker = this.src.charCodeAt(start); + // treat beginning of the line as a whitespace + lastChar = start > 0 ? this.src.charCodeAt(start - 1) : 32; + while (pos < max && this.src.charCodeAt(pos) === marker) { + pos++; + } + count = pos - start; + // treat end of the line as a whitespace + nextChar = pos < max ? this.src.charCodeAt(pos) : 32; + isLastPunctChar = isMdAsciiPunct(lastChar) || isPunctChar(String.fromCharCode(lastChar)); + isNextPunctChar = isMdAsciiPunct(nextChar) || isPunctChar(String.fromCharCode(nextChar)); + isLastWhiteSpace = isWhiteSpace(lastChar); + isNextWhiteSpace = isWhiteSpace(nextChar); + if (isNextWhiteSpace) { + left_flanking = false; + } else if (isNextPunctChar) { + if (!(isLastWhiteSpace || isLastPunctChar)) { + left_flanking = false; + } + } + if (isLastWhiteSpace) { + right_flanking = false; + } else if (isLastPunctChar) { + if (!(isNextWhiteSpace || isNextPunctChar)) { + right_flanking = false; + } + } + if (!canSplitWord) { + can_open = left_flanking && (!right_flanking || isLastPunctChar); + can_close = right_flanking && (!left_flanking || isNextPunctChar); + } else { + can_open = left_flanking; + can_close = right_flanking; + } + return { + can_open: can_open, + can_close: can_close, + length: count + }; + }; + // re-export Token class to use in block rules + StateInline.prototype.Token = token; + var state_inline = StateInline; + //////////////////////////////////////////////////////////////////////////////// + // Parser rules + var _rules = [ [ "text", text ], [ "linkify", linkify ], [ "newline", newline ], [ "escape", _escape ], [ "backticks", backticks ], [ "strikethrough", strikethrough.tokenize ], [ "emphasis", emphasis.tokenize ], [ "link", link ], [ "image", image ], [ "autolink", autolink ], [ "html_inline", html_inline ], [ "entity", entity ] ]; + // `rule2` ruleset was created specifically for emphasis/strikethrough + // post-processing and may be changed in the future. + + // Don't use this for anything except pairs (plugins working with `balance_pairs`). + + var _rules2 = [ [ "balance_pairs", balance_pairs ], [ "strikethrough", strikethrough.postProcess ], [ "emphasis", emphasis.postProcess ], + // rules for pairs separate '**' into its own text tokens, which may be left unused, + // rule below merges unused segments back with the rest of the text + [ "fragments_join", fragments_join ] ]; + /** + * new ParserInline() + **/ function ParserInline() { + var i; + /** + * ParserInline#ruler -> Ruler + * + * [[Ruler]] instance. Keep configuration of inline rules. + **/ this.ruler = new ruler; + for (i = 0; i < _rules.length; i++) { + this.ruler.push(_rules[i][0], _rules[i][1]); + } + /** + * ParserInline#ruler2 -> Ruler + * + * [[Ruler]] instance. Second ruler used for post-processing + * (e.g. in emphasis-like rules). + **/ this.ruler2 = new ruler; + for (i = 0; i < _rules2.length; i++) { + this.ruler2.push(_rules2[i][0], _rules2[i][1]); + } + } + // Skip single token by running all rules in validation mode; + // returns `true` if any rule reported success + + ParserInline.prototype.skipToken = function(state) { + var ok, i, pos = state.pos, rules = this.ruler.getRules(""), len = rules.length, maxNesting = state.md.options.maxNesting, cache = state.cache; + if (typeof cache[pos] !== "undefined") { + state.pos = cache[pos]; + return; + } + if (state.level < maxNesting) { + for (i = 0; i < len; i++) { + // Increment state.level and decrement it later to limit recursion. + // It's harmless to do here, because no tokens are created. But ideally, + // we'd need a separate private state variable for this purpose. + state.level++; + ok = rules[i](state, true); + state.level--; + if (ok) { + if (pos >= state.pos) { + throw new Error("inline rule didn't increment state.pos"); + } + break; + } + } + } else { + // Too much nesting, just skip until the end of the paragraph. + // NOTE: this will cause links to behave incorrectly in the following case, + // when an amount of `[` is exactly equal to `maxNesting + 1`: + // [[[[[[[[[[[[[[[[[[[[[foo]() + // TODO: remove this workaround when CM standard will allow nested links + // (we can replace it by preventing links from being parsed in + // validation mode) + state.pos = state.posMax; + } + if (!ok) { + state.pos++; + } + cache[pos] = state.pos; + }; + // Generate tokens for input range + + ParserInline.prototype.tokenize = function(state) { + var ok, i, prevPos, rules = this.ruler.getRules(""), len = rules.length, end = state.posMax, maxNesting = state.md.options.maxNesting; + while (state.pos < end) { + // Try all possible rules. + // On success, rule should: + // - update `state.pos` + // - update `state.tokens` + // - return true + prevPos = state.pos; + if (state.level < maxNesting) { + for (i = 0; i < len; i++) { + ok = rules[i](state, false); + if (ok) { + if (prevPos >= state.pos) { + throw new Error("inline rule didn't increment state.pos"); + } + break; + } + } + } + if (ok) { + if (state.pos >= end) { + break; + } + continue; + } + state.pending += state.src[state.pos++]; + } + if (state.pending) { + state.pushPending(); + } + }; + /** + * ParserInline.parse(str, md, env, outTokens) + * + * Process input string and push inline tokens into `outTokens` + **/ ParserInline.prototype.parse = function(str, md, env, outTokens) { + var i, rules, len; + var state = new this.State(str, md, env, outTokens); + this.tokenize(state); + rules = this.ruler2.getRules(""); + len = rules.length; + for (i = 0; i < len; i++) { + rules[i](state); + } + }; + ParserInline.prototype.State = state_inline; + var parser_inline = ParserInline; + var re = function(opts) { + var re = {}; + opts = opts || {}; + // Use direct extract instead of `regenerate` to reduse browserified size + re.src_Any = regex$3.source; + re.src_Cc = regex$2.source; + re.src_Z = regex.source; + re.src_P = regex$4.source; + // \p{\Z\P\Cc\CF} (white spaces + control + format + punctuation) + re.src_ZPCc = [ re.src_Z, re.src_P, re.src_Cc ].join("|"); + // \p{\Z\Cc} (white spaces + control) + re.src_ZCc = [ re.src_Z, re.src_Cc ].join("|"); + // Experimental. List of chars, completely prohibited in links + // because can separate it from other part of text + var text_separators = "[><\uff5c]"; + // All possible word characters (everything without punctuation, spaces & controls) + // Defined via punctuation & spaces to save space + // Should be something like \p{\L\N\S\M} (\w but without `_`) + re.src_pseudo_letter = "(?:(?!" + text_separators + "|" + re.src_ZPCc + ")" + re.src_Any + ")"; + // The same as abothe but without [0-9] + // var src_pseudo_letter_non_d = '(?:(?![0-9]|' + src_ZPCc + ')' + src_Any + ')'; + //////////////////////////////////////////////////////////////////////////////// + re.src_ip4 = "(?:(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)"; + // Prohibit any of "@/[]()" in user/pass to avoid wrong domain fetch. + re.src_auth = "(?:(?:(?!" + re.src_ZCc + "|[@/\\[\\]()]).)+@)?"; + re.src_port = "(?::(?:6(?:[0-4]\\d{3}|5(?:[0-4]\\d{2}|5(?:[0-2]\\d|3[0-5])))|[1-5]?\\d{1,4}))?"; + re.src_host_terminator = "(?=$|" + text_separators + "|" + re.src_ZPCc + ")" + "(?!" + (opts["---"] ? "-(?!--)|" : "-|") + "_|:\\d|\\.-|\\.(?!$|" + re.src_ZPCc + "))"; + re.src_path = "(?:" + "[/?#]" + "(?:" + "(?!" + re.src_ZCc + "|" + text_separators + "|[()[\\]{}.,\"'?!\\-;]).|" + "\\[(?:(?!" + re.src_ZCc + "|\\]).)*\\]|" + "\\((?:(?!" + re.src_ZCc + "|[)]).)*\\)|" + "\\{(?:(?!" + re.src_ZCc + "|[}]).)*\\}|" + '\\"(?:(?!' + re.src_ZCc + '|["]).)+\\"|' + "\\'(?:(?!" + re.src_ZCc + "|[']).)+\\'|" + "\\'(?=" + re.src_pseudo_letter + "|[-])|" + // allow `I'm_king` if no pair found + "\\.{2,}[a-zA-Z0-9%/&]|" + // google has many dots in "google search" links (#66, #81). + // github has ... in commit range links, + // Restrict to + // - english + // - percent-encoded + // - parts of file path + // - params separator + // until more examples found. + "\\.(?!" + re.src_ZCc + "|[.]|$)|" + (opts["---"] ? "\\-(?!--(?:[^-]|$))(?:-*)|" : "\\-+|") + ",(?!" + re.src_ZCc + "|$)|" + // allow `,,,` in paths + ";(?!" + re.src_ZCc + "|$)|" + // allow `;` if not followed by space-like char + "\\!+(?!" + re.src_ZCc + "|[!]|$)|" + // allow `!!!` in paths, but not at the end + "\\?(?!" + re.src_ZCc + "|[?]|$)" + ")+" + "|\\/" + ")?"; + // Allow anything in markdown spec, forbid quote (") at the first position + // because emails enclosed in quotes are far more common + re.src_email_name = '[\\-;:&=\\+\\$,\\.a-zA-Z0-9_][\\-;:&=\\+\\$,\\"\\.a-zA-Z0-9_]*'; + re.src_xn = "xn--[a-z0-9\\-]{1,59}"; + // More to read about domain names + // http://serverfault.com/questions/638260/ + re.src_domain_root = + // Allow letters & digits (http://test1) + "(?:" + re.src_xn + "|" + re.src_pseudo_letter + "{1,63}" + ")"; + re.src_domain = "(?:" + re.src_xn + "|" + "(?:" + re.src_pseudo_letter + ")" + "|" + "(?:" + re.src_pseudo_letter + "(?:-|" + re.src_pseudo_letter + "){0,61}" + re.src_pseudo_letter + ")" + ")"; + re.src_host = "(?:" + + // Don't need IP check, because digits are already allowed in normal domain names + // src_ip4 + + // '|' + + "(?:(?:(?:" + re.src_domain + ")\\.)*" + re.src_domain /*_root*/ + ")" + ")"; + re.tpl_host_fuzzy = "(?:" + re.src_ip4 + "|" + "(?:(?:(?:" + re.src_domain + ")\\.)+(?:%TLDS%))" + ")"; + re.tpl_host_no_ip_fuzzy = "(?:(?:(?:" + re.src_domain + ")\\.)+(?:%TLDS%))"; + re.src_host_strict = re.src_host + re.src_host_terminator; + re.tpl_host_fuzzy_strict = re.tpl_host_fuzzy + re.src_host_terminator; + re.src_host_port_strict = re.src_host + re.src_port + re.src_host_terminator; + re.tpl_host_port_fuzzy_strict = re.tpl_host_fuzzy + re.src_port + re.src_host_terminator; + re.tpl_host_port_no_ip_fuzzy_strict = re.tpl_host_no_ip_fuzzy + re.src_port + re.src_host_terminator; + //////////////////////////////////////////////////////////////////////////////// + // Main rules + // Rude test fuzzy links by host, for quick deny + re.tpl_host_fuzzy_test = "localhost|www\\.|\\.\\d{1,3}\\.|(?:\\.(?:%TLDS%)(?:" + re.src_ZPCc + "|>|$))"; + re.tpl_email_fuzzy = "(^|" + text_separators + '|"|\\(|' + re.src_ZCc + ")" + "(" + re.src_email_name + "@" + re.tpl_host_fuzzy_strict + ")"; + re.tpl_link_fuzzy = + // Fuzzy link can't be prepended with .:/\- and non punctuation. + // but can start with > (markdown blockquote) + "(^|(?![.:/\\-_@])(?:[$+<=>^`|\uff5c]|" + re.src_ZPCc + "))" + "((?![$+<=>^`|\uff5c])" + re.tpl_host_port_fuzzy_strict + re.src_path + ")"; + re.tpl_link_no_ip_fuzzy = + // Fuzzy link can't be prepended with .:/\- and non punctuation. + // but can start with > (markdown blockquote) + "(^|(?![.:/\\-_@])(?:[$+<=>^`|\uff5c]|" + re.src_ZPCc + "))" + "((?![$+<=>^`|\uff5c])" + re.tpl_host_port_no_ip_fuzzy_strict + re.src_path + ")"; + return re; + }; + //////////////////////////////////////////////////////////////////////////////// + // Helpers + // Merge objects + + function assign(obj /*from1, from2, from3, ...*/) { + var sources = Array.prototype.slice.call(arguments, 1); + sources.forEach((function(source) { + if (!source) { + return; + } + Object.keys(source).forEach((function(key) { + obj[key] = source[key]; + })); + })); + return obj; + } + function _class(obj) { + return Object.prototype.toString.call(obj); + } + function isString(obj) { + return _class(obj) === "[object String]"; + } + function isObject(obj) { + return _class(obj) === "[object Object]"; + } + function isRegExp(obj) { + return _class(obj) === "[object RegExp]"; + } + function isFunction(obj) { + return _class(obj) === "[object Function]"; + } + function escapeRE(str) { + return str.replace(/[.?*+^$[\]\\(){}|-]/g, "\\$&"); + } + //////////////////////////////////////////////////////////////////////////////// + var defaultOptions = { + fuzzyLink: true, + fuzzyEmail: true, + fuzzyIP: false + }; + function isOptionsObj(obj) { + return Object.keys(obj || {}).reduce((function(acc, k) { + return acc || defaultOptions.hasOwnProperty(k); + }), false); + } + var defaultSchemas = { + "http:": { + validate: function(text, pos, self) { + var tail = text.slice(pos); + if (!self.re.http) { + // compile lazily, because "host"-containing variables can change on tlds update. + self.re.http = new RegExp("^\\/\\/" + self.re.src_auth + self.re.src_host_port_strict + self.re.src_path, "i"); + } + if (self.re.http.test(tail)) { + return tail.match(self.re.http)[0].length; + } + return 0; + } + }, + "https:": "http:", + "ftp:": "http:", + "//": { + validate: function(text, pos, self) { + var tail = text.slice(pos); + if (!self.re.no_http) { + // compile lazily, because "host"-containing variables can change on tlds update. + self.re.no_http = new RegExp("^" + self.re.src_auth + + // Don't allow single-level domains, because of false positives like '//test' + // with code comments + "(?:localhost|(?:(?:" + self.re.src_domain + ")\\.)+" + self.re.src_domain_root + ")" + self.re.src_port + self.re.src_host_terminator + self.re.src_path, "i"); + } + if (self.re.no_http.test(tail)) { + // should not be `://` & `///`, that protects from errors in protocol name + if (pos >= 3 && text[pos - 3] === ":") { + return 0; + } + if (pos >= 3 && text[pos - 3] === "/") { + return 0; + } + return tail.match(self.re.no_http)[0].length; + } + return 0; + } + }, + "mailto:": { + validate: function(text, pos, self) { + var tail = text.slice(pos); + if (!self.re.mailto) { + self.re.mailto = new RegExp("^" + self.re.src_email_name + "@" + self.re.src_host_strict, "i"); + } + if (self.re.mailto.test(tail)) { + return tail.match(self.re.mailto)[0].length; + } + return 0; + } + } + }; + /*eslint-disable max-len*/ + // RE pattern for 2-character tlds (autogenerated by ./support/tlds_2char_gen.js) + var tlds_2ch_src_re = "a[cdefgilmnoqrstuwxz]|b[abdefghijmnorstvwyz]|c[acdfghiklmnoruvwxyz]|d[ejkmoz]|e[cegrstu]|f[ijkmor]|g[abdefghilmnpqrstuwy]|h[kmnrtu]|i[delmnoqrst]|j[emop]|k[eghimnprwyz]|l[abcikrstuvy]|m[acdeghklmnopqrstuvwxyz]|n[acefgilopruz]|om|p[aefghklmnrstwy]|qa|r[eosuw]|s[abcdeghijklmnortuvxyz]|t[cdfghjklmnortvwz]|u[agksyz]|v[aceginu]|w[fs]|y[et]|z[amw]"; + // DON'T try to make PRs with changes. Extend TLDs with LinkifyIt.tlds() instead + var tlds_default = "biz|com|edu|gov|net|org|pro|web|xxx|aero|asia|coop|info|museum|name|shop|\u0440\u0444".split("|"); + /*eslint-enable max-len*/ + //////////////////////////////////////////////////////////////////////////////// + function resetScanCache(self) { + self.__index__ = -1; + self.__text_cache__ = ""; + } + function createValidator(re) { + return function(text, pos) { + var tail = text.slice(pos); + if (re.test(tail)) { + return tail.match(re)[0].length; + } + return 0; + }; + } + function createNormalizer() { + return function(match, self) { + self.normalize(match); + }; + } + // Schemas compiler. Build regexps. + + function compile(self) { + // Load & clone RE patterns. + var re$1 = self.re = re(self.__opts__); + // Define dynamic patterns + var tlds = self.__tlds__.slice(); + self.onCompile(); + if (!self.__tlds_replaced__) { + tlds.push(tlds_2ch_src_re); + } + tlds.push(re$1.src_xn); + re$1.src_tlds = tlds.join("|"); + function untpl(tpl) { + return tpl.replace("%TLDS%", re$1.src_tlds); + } + re$1.email_fuzzy = RegExp(untpl(re$1.tpl_email_fuzzy), "i"); + re$1.link_fuzzy = RegExp(untpl(re$1.tpl_link_fuzzy), "i"); + re$1.link_no_ip_fuzzy = RegExp(untpl(re$1.tpl_link_no_ip_fuzzy), "i"); + re$1.host_fuzzy_test = RegExp(untpl(re$1.tpl_host_fuzzy_test), "i"); + + // Compile each schema + + var aliases = []; + self.__compiled__ = {}; + // Reset compiled data + function schemaError(name, val) { + throw new Error('(LinkifyIt) Invalid schema "' + name + '": ' + val); + } + Object.keys(self.__schemas__).forEach((function(name) { + var val = self.__schemas__[name]; + // skip disabled methods + if (val === null) { + return; + } + var compiled = { + validate: null, + link: null + }; + self.__compiled__[name] = compiled; + if (isObject(val)) { + if (isRegExp(val.validate)) { + compiled.validate = createValidator(val.validate); + } else if (isFunction(val.validate)) { + compiled.validate = val.validate; + } else { + schemaError(name, val); + } + if (isFunction(val.normalize)) { + compiled.normalize = val.normalize; + } else if (!val.normalize) { + compiled.normalize = createNormalizer(); + } else { + schemaError(name, val); + } + return; + } + if (isString(val)) { + aliases.push(name); + return; + } + schemaError(name, val); + })); + + // Compile postponed aliases + + aliases.forEach((function(alias) { + if (!self.__compiled__[self.__schemas__[alias]]) { + // Silently fail on missed schemas to avoid errons on disable. + // schemaError(alias, self.__schemas__[alias]); + return; + } + self.__compiled__[alias].validate = self.__compiled__[self.__schemas__[alias]].validate; + self.__compiled__[alias].normalize = self.__compiled__[self.__schemas__[alias]].normalize; + })); + + // Fake record for guessed links + + self.__compiled__[""] = { + validate: null, + normalize: createNormalizer() + }; + + // Build schema condition + + var slist = Object.keys(self.__compiled__).filter((function(name) { + // Filter disabled & fake schemas + return name.length > 0 && self.__compiled__[name]; + })).map(escapeRE).join("|"); + // (?!_) cause 1.5x slowdown + self.re.schema_test = RegExp("(^|(?!_)(?:[><\uff5c]|" + re$1.src_ZPCc + "))(" + slist + ")", "i"); + self.re.schema_search = RegExp("(^|(?!_)(?:[><\uff5c]|" + re$1.src_ZPCc + "))(" + slist + ")", "ig"); + self.re.schema_at_start = RegExp("^" + self.re.schema_search.source, "i"); + self.re.pretest = RegExp("(" + self.re.schema_test.source + ")|(" + self.re.host_fuzzy_test.source + ")|@", "i"); + + // Cleanup + + resetScanCache(self); + } + /** + * class Match + * + * Match result. Single element of array, returned by [[LinkifyIt#match]] + **/ function Match(self, shift) { + var start = self.__index__, end = self.__last_index__, text = self.__text_cache__.slice(start, end); + /** + * Match#schema -> String + * + * Prefix (protocol) for matched string. + **/ this.schema = self.__schema__.toLowerCase(); + /** + * Match#index -> Number + * + * First position of matched string. + **/ this.index = start + shift; + /** + * Match#lastIndex -> Number + * + * Next position after matched string. + **/ this.lastIndex = end + shift; + /** + * Match#raw -> String + * + * Matched string. + **/ this.raw = text; + /** + * Match#text -> String + * + * Notmalized text of matched string. + **/ this.text = text; + /** + * Match#url -> String + * + * Normalized url of matched string. + **/ this.url = text; + } + function createMatch(self, shift) { + var match = new Match(self, shift); + self.__compiled__[match.schema].normalize(match, self); + return match; + } + /** + * class LinkifyIt + **/ + /** + * new LinkifyIt(schemas, options) + * - schemas (Object): Optional. Additional schemas to validate (prefix/validator) + * - options (Object): { fuzzyLink|fuzzyEmail|fuzzyIP: true|false } + * + * Creates new linkifier instance with optional additional schemas. + * Can be called without `new` keyword for convenience. + * + * By default understands: + * + * - `http(s)://...` , `ftp://...`, `mailto:...` & `//...` links + * - "fuzzy" links and emails (example.com, foo@bar.com). + * + * `schemas` is an object, where each key/value describes protocol/rule: + * + * - __key__ - link prefix (usually, protocol name with `:` at the end, `skype:` + * for example). `linkify-it` makes shure that prefix is not preceeded with + * alphanumeric char and symbols. Only whitespaces and punctuation allowed. + * - __value__ - rule to check tail after link prefix + * - _String_ - just alias to existing rule + * - _Object_ + * - _validate_ - validator function (should return matched length on success), + * or `RegExp`. + * - _normalize_ - optional function to normalize text & url of matched result + * (for example, for @twitter mentions). + * + * `options`: + * + * - __fuzzyLink__ - recognige URL-s without `http(s):` prefix. Default `true`. + * - __fuzzyIP__ - allow IPs in fuzzy links above. Can conflict with some texts + * like version numbers. Default `false`. + * - __fuzzyEmail__ - recognize emails without `mailto:` prefix. + * + **/ function LinkifyIt(schemas, options) { + if (!(this instanceof LinkifyIt)) { + return new LinkifyIt(schemas, options); + } + if (!options) { + if (isOptionsObj(schemas)) { + options = schemas; + schemas = {}; + } + } + this.__opts__ = assign({}, defaultOptions, options); + // Cache last tested result. Used to skip repeating steps on next `match` call. + this.__index__ = -1; + this.__last_index__ = -1; + // Next scan position + this.__schema__ = ""; + this.__text_cache__ = ""; + this.__schemas__ = assign({}, defaultSchemas, schemas); + this.__compiled__ = {}; + this.__tlds__ = tlds_default; + this.__tlds_replaced__ = false; + this.re = {}; + compile(this); + } + /** chainable + * LinkifyIt#add(schema, definition) + * - schema (String): rule name (fixed pattern prefix) + * - definition (String|RegExp|Object): schema definition + * + * Add new rule definition. See constructor description for details. + **/ LinkifyIt.prototype.add = function add(schema, definition) { + this.__schemas__[schema] = definition; + compile(this); + return this; + }; + /** chainable + * LinkifyIt#set(options) + * - options (Object): { fuzzyLink|fuzzyEmail|fuzzyIP: true|false } + * + * Set recognition options for links without schema. + **/ LinkifyIt.prototype.set = function set(options) { + this.__opts__ = assign(this.__opts__, options); + return this; + }; + /** + * LinkifyIt#test(text) -> Boolean + * + * Searches linkifiable pattern and returns `true` on success or `false` on fail. + **/ LinkifyIt.prototype.test = function test(text) { + // Reset scan cache + this.__text_cache__ = text; + this.__index__ = -1; + if (!text.length) { + return false; + } + var m, ml, me, len, shift, next, re, tld_pos, at_pos; + // try to scan for link with schema - that's the most simple rule + if (this.re.schema_test.test(text)) { + re = this.re.schema_search; + re.lastIndex = 0; + while ((m = re.exec(text)) !== null) { + len = this.testSchemaAt(text, m[2], re.lastIndex); + if (len) { + this.__schema__ = m[2]; + this.__index__ = m.index + m[1].length; + this.__last_index__ = m.index + m[0].length + len; + break; + } + } + } + if (this.__opts__.fuzzyLink && this.__compiled__["http:"]) { + // guess schemaless links + tld_pos = text.search(this.re.host_fuzzy_test); + if (tld_pos >= 0) { + // if tld is located after found link - no need to check fuzzy pattern + if (this.__index__ < 0 || tld_pos < this.__index__) { + if ((ml = text.match(this.__opts__.fuzzyIP ? this.re.link_fuzzy : this.re.link_no_ip_fuzzy)) !== null) { + shift = ml.index + ml[1].length; + if (this.__index__ < 0 || shift < this.__index__) { + this.__schema__ = ""; + this.__index__ = shift; + this.__last_index__ = ml.index + ml[0].length; + } + } + } + } + } + if (this.__opts__.fuzzyEmail && this.__compiled__["mailto:"]) { + // guess schemaless emails + at_pos = text.indexOf("@"); + if (at_pos >= 0) { + // We can't skip this check, because this cases are possible: + // 192.168.1.1@gmail.com, my.in@example.com + if ((me = text.match(this.re.email_fuzzy)) !== null) { + shift = me.index + me[1].length; + next = me.index + me[0].length; + if (this.__index__ < 0 || shift < this.__index__ || shift === this.__index__ && next > this.__last_index__) { + this.__schema__ = "mailto:"; + this.__index__ = shift; + this.__last_index__ = next; + } + } + } + } + return this.__index__ >= 0; + }; + /** + * LinkifyIt#pretest(text) -> Boolean + * + * Very quick check, that can give false positives. Returns true if link MAY BE + * can exists. Can be used for speed optimization, when you need to check that + * link NOT exists. + **/ LinkifyIt.prototype.pretest = function pretest(text) { + return this.re.pretest.test(text); + }; + /** + * LinkifyIt#testSchemaAt(text, name, position) -> Number + * - text (String): text to scan + * - name (String): rule (schema) name + * - position (Number): text offset to check from + * + * Similar to [[LinkifyIt#test]] but checks only specific protocol tail exactly + * at given position. Returns length of found pattern (0 on fail). + **/ LinkifyIt.prototype.testSchemaAt = function testSchemaAt(text, schema, pos) { + // If not supported schema check requested - terminate + if (!this.__compiled__[schema.toLowerCase()]) { + return 0; + } + return this.__compiled__[schema.toLowerCase()].validate(text, pos, this); + }; + /** + * LinkifyIt#match(text) -> Array|null + * + * Returns array of found link descriptions or `null` on fail. We strongly + * recommend to use [[LinkifyIt#test]] first, for best speed. + * + * ##### Result match description + * + * - __schema__ - link schema, can be empty for fuzzy links, or `//` for + * protocol-neutral links. + * - __index__ - offset of matched text + * - __lastIndex__ - index of next char after mathch end + * - __raw__ - matched text + * - __text__ - normalized text + * - __url__ - link, generated from matched text + **/ LinkifyIt.prototype.match = function match(text) { + var shift = 0, result = []; + // Try to take previous element from cache, if .test() called before + if (this.__index__ >= 0 && this.__text_cache__ === text) { + result.push(createMatch(this, shift)); + shift = this.__last_index__; + } + // Cut head if cache was used + var tail = shift ? text.slice(shift) : text; + // Scan string until end reached + while (this.test(tail)) { + result.push(createMatch(this, shift)); + tail = tail.slice(this.__last_index__); + shift += this.__last_index__; + } + if (result.length) { + return result; + } + return null; + }; + /** + * LinkifyIt#matchAtStart(text) -> Match|null + * + * Returns fully-formed (not fuzzy) link if it starts at the beginning + * of the string, and null otherwise. + **/ LinkifyIt.prototype.matchAtStart = function matchAtStart(text) { + // Reset scan cache + this.__text_cache__ = text; + this.__index__ = -1; + if (!text.length) return null; + var m = this.re.schema_at_start.exec(text); + if (!m) return null; + var len = this.testSchemaAt(text, m[2], m[0].length); + if (!len) return null; + this.__schema__ = m[2]; + this.__index__ = m.index + m[1].length; + this.__last_index__ = m.index + m[0].length + len; + return createMatch(this, 0); + }; + /** chainable + * LinkifyIt#tlds(list [, keepOld]) -> this + * - list (Array): list of tlds + * - keepOld (Boolean): merge with current list if `true` (`false` by default) + * + * Load (or merge) new tlds list. Those are user for fuzzy links (without prefix) + * to avoid false positives. By default this algorythm used: + * + * - hostname with any 2-letter root zones are ok. + * - biz|com|edu|gov|net|org|pro|web|xxx|aero|asia|coop|info|museum|name|shop|рф + * are ok. + * - encoded (`xn--...`) root zones are ok. + * + * If list is replaced, then exact match for 2-chars root zones will be checked. + **/ LinkifyIt.prototype.tlds = function tlds(list, keepOld) { + list = Array.isArray(list) ? list : [ list ]; + if (!keepOld) { + this.__tlds__ = list.slice(); + this.__tlds_replaced__ = true; + compile(this); + return this; + } + this.__tlds__ = this.__tlds__.concat(list).sort().filter((function(el, idx, arr) { + return el !== arr[idx - 1]; + })).reverse(); + compile(this); + return this; + }; + /** + * LinkifyIt#normalize(match) + * + * Default normalizer (if schema does not define it's own). + **/ LinkifyIt.prototype.normalize = function normalize(match) { + // Do minimal possible changes by default. Need to collect feedback prior + // to move forward https://github.com/markdown-it/linkify-it/issues/1 + if (!match.schema) { + match.url = "http://" + match.url; + } + if (match.schema === "mailto:" && !/^mailto:/i.test(match.url)) { + match.url = "mailto:" + match.url; + } + }; + /** + * LinkifyIt#onCompile() + * + * Override to modify basic RegExp-s. + **/ LinkifyIt.prototype.onCompile = function onCompile() {}; + var linkifyIt = LinkifyIt; + /*! https://mths.be/punycode v1.4.1 by @mathias */ + /** Highest positive signed 32-bit float value */ var maxInt = 2147483647; + // aka. 0x7FFFFFFF or 2^31-1 + /** Bootstring parameters */ var base = 36; + var tMin = 1; + var tMax = 26; + var skew = 38; + var damp = 700; + var initialBias = 72; + var initialN = 128; + // 0x80 + var delimiter = "-"; + // '\x2D' + /** Regular expressions */ var regexPunycode = /^xn--/; + var regexNonASCII = /[^\x20-\x7E]/; + // unprintable ASCII chars + non-ASCII chars + var regexSeparators = /[\x2E\u3002\uFF0E\uFF61]/g; + // RFC 3490 separators + /** Error messages */ var errors = { + overflow: "Overflow: input needs wider integers to process", + "not-basic": "Illegal input >= 0x80 (not a basic code point)", + "invalid-input": "Invalid input" + }; + /** Convenience shortcuts */ var baseMinusTMin = base - tMin; + var floor = Math.floor; + var stringFromCharCode = String.fromCharCode; + /*--------------------------------------------------------------------------*/ + /** + * A generic error utility function. + * @private + * @param {String} type The error type. + * @returns {Error} Throws a `RangeError` with the applicable error message. + */ function error(type) { + throw new RangeError(errors[type]); + } + /** + * A generic `Array#map` utility function. + * @private + * @param {Array} array The array to iterate over. + * @param {Function} callback The function that gets called for every array + * item. + * @returns {Array} A new array of values returned by the callback function. + */ function map(array, fn) { + var length = array.length; + var result = []; + while (length--) { + result[length] = fn(array[length]); + } + return result; + } + /** + * A simple `Array#map`-like wrapper to work with domain name strings or email + * addresses. + * @private + * @param {String} domain The domain name or email address. + * @param {Function} callback The function that gets called for every + * character. + * @returns {Array} A new string of characters returned by the callback + * function. + */ function mapDomain(string, fn) { + var parts = string.split("@"); + var result = ""; + if (parts.length > 1) { + // In email addresses, only the domain name should be punycoded. Leave + // the local part (i.e. everything up to `@`) intact. + result = parts[0] + "@"; + string = parts[1]; + } + // Avoid `split(regex)` for IE8 compatibility. See #17. + string = string.replace(regexSeparators, "."); + var labels = string.split("."); + var encoded = map(labels, fn).join("."); + return result + encoded; + } + /** + * Creates an array containing the numeric code points of each Unicode + * character in the string. While JavaScript uses UCS-2 internally, + * this function will convert a pair of surrogate halves (each of which + * UCS-2 exposes as separate characters) into a single code point, + * matching UTF-16. + * @see `punycode.ucs2.encode` + * @see + * @memberOf punycode.ucs2 + * @name decode + * @param {String} string The Unicode input string (UCS-2). + * @returns {Array} The new array of code points. + */ function ucs2decode(string) { + var output = [], counter = 0, length = string.length, value, extra; + while (counter < length) { + value = string.charCodeAt(counter++); + if (value >= 55296 && value <= 56319 && counter < length) { + // high surrogate, and there is a next character + extra = string.charCodeAt(counter++); + if ((extra & 64512) == 56320) { + // low surrogate + output.push(((value & 1023) << 10) + (extra & 1023) + 65536); + } else { + // unmatched surrogate; only append this code unit, in case the next + // code unit is the high surrogate of a surrogate pair + output.push(value); + counter--; + } + } else { + output.push(value); + } + } + return output; + } + /** + * Creates a string based on an array of numeric code points. + * @see `punycode.ucs2.decode` + * @memberOf punycode.ucs2 + * @name encode + * @param {Array} codePoints The array of numeric code points. + * @returns {String} The new Unicode string (UCS-2). + */ function ucs2encode(array) { + return map(array, (function(value) { + var output = ""; + if (value > 65535) { + value -= 65536; + output += stringFromCharCode(value >>> 10 & 1023 | 55296); + value = 56320 | value & 1023; + } + output += stringFromCharCode(value); + return output; + })).join(""); + } + /** + * Converts a basic code point into a digit/integer. + * @see `digitToBasic()` + * @private + * @param {Number} codePoint The basic numeric code point value. + * @returns {Number} The numeric value of a basic code point (for use in + * representing integers) in the range `0` to `base - 1`, or `base` if + * the code point does not represent a value. + */ function basicToDigit(codePoint) { + if (codePoint - 48 < 10) { + return codePoint - 22; + } + if (codePoint - 65 < 26) { + return codePoint - 65; + } + if (codePoint - 97 < 26) { + return codePoint - 97; + } + return base; + } + /** + * Converts a digit/integer into a basic code point. + * @see `basicToDigit()` + * @private + * @param {Number} digit The numeric value of a basic code point. + * @returns {Number} The basic code point whose value (when used for + * representing integers) is `digit`, which needs to be in the range + * `0` to `base - 1`. If `flag` is non-zero, the uppercase form is + * used; else, the lowercase form is used. The behavior is undefined + * if `flag` is non-zero and `digit` has no uppercase form. + */ function digitToBasic(digit, flag) { + // 0..25 map to ASCII a..z or A..Z + // 26..35 map to ASCII 0..9 + return digit + 22 + 75 * (digit < 26) - ((flag != 0) << 5); + } + /** + * Bias adaptation function as per section 3.4 of RFC 3492. + * https://tools.ietf.org/html/rfc3492#section-3.4 + * @private + */ function adapt(delta, numPoints, firstTime) { + var k = 0; + delta = firstTime ? floor(delta / damp) : delta >> 1; + delta += floor(delta / numPoints); + for (;delta > baseMinusTMin * tMax >> 1; k += base) { + delta = floor(delta / baseMinusTMin); + } + return floor(k + (baseMinusTMin + 1) * delta / (delta + skew)); + } + /** + * Converts a Punycode string of ASCII-only symbols to a string of Unicode + * symbols. + * @memberOf punycode + * @param {String} input The Punycode string of ASCII-only symbols. + * @returns {String} The resulting string of Unicode symbols. + */ function decode(input) { + // Don't use UCS-2 + var output = [], inputLength = input.length, out, i = 0, n = initialN, bias = initialBias, basic, j, index, oldi, w, k, digit, t, + /** Cached calculation results */ + baseMinusT; + // Handle the basic code points: let `basic` be the number of input code + // points before the last delimiter, or `0` if there is none, then copy + // the first basic code points to the output. + basic = input.lastIndexOf(delimiter); + if (basic < 0) { + basic = 0; + } + for (j = 0; j < basic; ++j) { + // if it's not a basic code point + if (input.charCodeAt(j) >= 128) { + error("not-basic"); + } + output.push(input.charCodeAt(j)); + } + // Main decoding loop: start just after the last delimiter if any basic code + // points were copied; start at the beginning otherwise. + for (index = basic > 0 ? basic + 1 : 0; index < inputLength; ) { + // `index` is the index of the next character to be consumed. + // Decode a generalized variable-length integer into `delta`, + // which gets added to `i`. The overflow checking is easier + // if we increase `i` as we go, then subtract off its starting + // value at the end to obtain `delta`. + for (oldi = i, w = 1, k = base; ;k += base) { + if (index >= inputLength) { + error("invalid-input"); + } + digit = basicToDigit(input.charCodeAt(index++)); + if (digit >= base || digit > floor((maxInt - i) / w)) { + error("overflow"); + } + i += digit * w; + t = k <= bias ? tMin : k >= bias + tMax ? tMax : k - bias; + if (digit < t) { + break; + } + baseMinusT = base - t; + if (w > floor(maxInt / baseMinusT)) { + error("overflow"); + } + w *= baseMinusT; + } + out = output.length + 1; + bias = adapt(i - oldi, out, oldi == 0); + // `i` was supposed to wrap around from `out` to `0`, + // incrementing `n` each time, so we'll fix that now: + if (floor(i / out) > maxInt - n) { + error("overflow"); + } + n += floor(i / out); + i %= out; + // Insert `n` at position `i` of the output + output.splice(i++, 0, n); + } + return ucs2encode(output); + } + /** + * Converts a string of Unicode symbols (e.g. a domain name label) to a + * Punycode string of ASCII-only symbols. + * @memberOf punycode + * @param {String} input The string of Unicode symbols. + * @returns {String} The resulting Punycode string of ASCII-only symbols. + */ function encode(input) { + var n, delta, handledCPCount, basicLength, bias, j, m, q, k, t, currentValue, output = [], + /** `inputLength` will hold the number of code points in `input`. */ + inputLength, + /** Cached calculation results */ + handledCPCountPlusOne, baseMinusT, qMinusT; + // Convert the input in UCS-2 to Unicode + input = ucs2decode(input); + // Cache the length + inputLength = input.length; + // Initialize the state + n = initialN; + delta = 0; + bias = initialBias; + // Handle the basic code points + for (j = 0; j < inputLength; ++j) { + currentValue = input[j]; + if (currentValue < 128) { + output.push(stringFromCharCode(currentValue)); + } + } + handledCPCount = basicLength = output.length; + // `handledCPCount` is the number of code points that have been handled; + // `basicLength` is the number of basic code points. + // Finish the basic string - if it is not empty - with a delimiter + if (basicLength) { + output.push(delimiter); + } + // Main encoding loop: + while (handledCPCount < inputLength) { + // All non-basic code points < n have been handled already. Find the next + // larger one: + for (m = maxInt, j = 0; j < inputLength; ++j) { + currentValue = input[j]; + if (currentValue >= n && currentValue < m) { + m = currentValue; + } + } + // Increase `delta` enough to advance the decoder's state to , + // but guard against overflow + handledCPCountPlusOne = handledCPCount + 1; + if (m - n > floor((maxInt - delta) / handledCPCountPlusOne)) { + error("overflow"); + } + delta += (m - n) * handledCPCountPlusOne; + n = m; + for (j = 0; j < inputLength; ++j) { + currentValue = input[j]; + if (currentValue < n && ++delta > maxInt) { + error("overflow"); + } + if (currentValue == n) { + // Represent delta as a generalized variable-length integer + for (q = delta, k = base; ;k += base) { + t = k <= bias ? tMin : k >= bias + tMax ? tMax : k - bias; + if (q < t) { + break; + } + qMinusT = q - t; + baseMinusT = base - t; + output.push(stringFromCharCode(digitToBasic(t + qMinusT % baseMinusT, 0))); + q = floor(qMinusT / baseMinusT); + } + output.push(stringFromCharCode(digitToBasic(q, 0))); + bias = adapt(delta, handledCPCountPlusOne, handledCPCount == basicLength); + delta = 0; + ++handledCPCount; + } + } + ++delta; + ++n; + } + return output.join(""); + } + /** + * Converts a Punycode string representing a domain name or an email address + * to Unicode. Only the Punycoded parts of the input will be converted, i.e. + * it doesn't matter if you call it on a string that has already been + * converted to Unicode. + * @memberOf punycode + * @param {String} input The Punycoded domain name or email address to + * convert to Unicode. + * @returns {String} The Unicode representation of the given Punycode + * string. + */ function toUnicode(input) { + return mapDomain(input, (function(string) { + return regexPunycode.test(string) ? decode(string.slice(4).toLowerCase()) : string; + })); + } + /** + * Converts a Unicode string representing a domain name or an email address to + * Punycode. Only the non-ASCII parts of the domain name will be converted, + * i.e. it doesn't matter if you call it with a domain that's already in + * ASCII. + * @memberOf punycode + * @param {String} input The domain name or email address to convert, as a + * Unicode string. + * @returns {String} The Punycode representation of the given domain name or + * email address. + */ function toASCII(input) { + return mapDomain(input, (function(string) { + return regexNonASCII.test(string) ? "xn--" + encode(string) : string; + })); + } + var version = "1.4.1"; + /** + * An object of methods to convert from JavaScript's internal character + * representation (UCS-2) to Unicode code points, and back. + * @see + * @memberOf punycode + * @type Object + */ var ucs2 = { + decode: ucs2decode, + encode: ucs2encode + }; + var punycode$1 = { + version: version, + ucs2: ucs2, + toASCII: toASCII, + toUnicode: toUnicode, + encode: encode, + decode: decode + }; + var punycode$2 = Object.freeze({ + __proto__: null, + decode: decode, + encode: encode, + toUnicode: toUnicode, + toASCII: toASCII, + version: version, + ucs2: ucs2, + default: punycode$1 + }); + // markdown-it default options + var _default = { + options: { + html: false, + // Enable HTML tags in source + xhtmlOut: false, + // Use '/' to close single tags (
) + breaks: false, + // Convert '\n' in paragraphs into
+ langPrefix: "language-", + // CSS language prefix for fenced blocks + linkify: false, + // autoconvert URL-like texts to links + // Enable some language-neutral replacements + quotes beautification + typographer: false, + // Double + single quotes replacement pairs, when typographer enabled, + // and smartquotes on. Could be either a String or an Array. + // For example, you can use '«»„“' for Russian, '„“‚‘' for German, + // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + quotes: "\u201c\u201d\u2018\u2019", + /* “”‘’ */ + // Highlighter function. Should return escaped HTML, + // or '' if the source string is not changed and should be escaped externaly. + // If result starts with ) + breaks: false, + // Convert '\n' in paragraphs into
+ langPrefix: "language-", + // CSS language prefix for fenced blocks + linkify: false, + // autoconvert URL-like texts to links + // Enable some language-neutral replacements + quotes beautification + typographer: false, + // Double + single quotes replacement pairs, when typographer enabled, + // and smartquotes on. Could be either a String or an Array. + // For example, you can use '«»„“' for Russian, '„“‚‘' for German, + // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + quotes: "\u201c\u201d\u2018\u2019", + /* “”‘’ */ + // Highlighter function. Should return escaped HTML, + // or '' if the source string is not changed and should be escaped externaly. + // If result starts with ) + breaks: false, + // Convert '\n' in paragraphs into
+ langPrefix: "language-", + // CSS language prefix for fenced blocks + linkify: false, + // autoconvert URL-like texts to links + // Enable some language-neutral replacements + quotes beautification + typographer: false, + // Double + single quotes replacement pairs, when typographer enabled, + // and smartquotes on. Could be either a String or an Array. + // For example, you can use '«»„“' for Russian, '„“‚‘' for German, + // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + quotes: "\u201c\u201d\u2018\u2019", + /* “”‘’ */ + // Highlighter function. Should return escaped HTML, + // or '' if the source string is not changed and should be escaped externaly. + // If result starts with = 0) { + try { + parsed.hostname = punycode.toASCII(parsed.hostname); + } catch (er) {} + } + } + return mdurl.encode(mdurl.format(parsed)); + } + function normalizeLinkText(url) { + var parsed = mdurl.parse(url, true); + if (parsed.hostname) { + // Encode hostnames in urls like: + // `http://host/`, `https://host/`, `mailto:user@host`, `//host/` + // We don't encode unknown schemas, because it's likely that we encode + // something we shouldn't (e.g. `skype:name` treated as `skype:host`) + if (!parsed.protocol || RECODE_HOSTNAME_FOR.indexOf(parsed.protocol) >= 0) { + try { + parsed.hostname = punycode.toUnicode(parsed.hostname); + } catch (er) {} + } + } + // add '%' to exclude list because of https://github.com/markdown-it/markdown-it/issues/720 + return mdurl.decode(mdurl.format(parsed), mdurl.decode.defaultChars + "%"); + } + /** + * class MarkdownIt + * + * Main parser/renderer class. + * + * ##### Usage + * + * ```javascript + * // node.js, "classic" way: + * var MarkdownIt = require('markdown-it'), + * md = new MarkdownIt(); + * var result = md.render('# markdown-it rulezz!'); + * + * // node.js, the same, but with sugar: + * var md = require('markdown-it')(); + * var result = md.render('# markdown-it rulezz!'); + * + * // browser without AMD, added to "window" on script load + * // Note, there are no dash. + * var md = window.markdownit(); + * var result = md.render('# markdown-it rulezz!'); + * ``` + * + * Single line rendering, without paragraph wrap: + * + * ```javascript + * var md = require('markdown-it')(); + * var result = md.renderInline('__markdown-it__ rulezz!'); + * ``` + **/ + /** + * new MarkdownIt([presetName, options]) + * - presetName (String): optional, `commonmark` / `zero` + * - options (Object) + * + * Creates parser instanse with given config. Can be called without `new`. + * + * ##### presetName + * + * MarkdownIt provides named presets as a convenience to quickly + * enable/disable active syntax rules and options for common use cases. + * + * - ["commonmark"](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/commonmark.js) - + * configures parser to strict [CommonMark](http://commonmark.org/) mode. + * - [default](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/default.js) - + * similar to GFM, used when no preset name given. Enables all available rules, + * but still without html, typographer & autolinker. + * - ["zero"](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/zero.js) - + * all rules disabled. Useful to quickly setup your config via `.enable()`. + * For example, when you need only `bold` and `italic` markup and nothing else. + * + * ##### options: + * + * - __html__ - `false`. Set `true` to enable HTML tags in source. Be careful! + * That's not safe! You may need external sanitizer to protect output from XSS. + * It's better to extend features via plugins, instead of enabling HTML. + * - __xhtmlOut__ - `false`. Set `true` to add '/' when closing single tags + * (`
`). This is needed only for full CommonMark compatibility. In real + * world you will need HTML output. + * - __breaks__ - `false`. Set `true` to convert `\n` in paragraphs into `
`. + * - __langPrefix__ - `language-`. CSS language class prefix for fenced blocks. + * Can be useful for external highlighters. + * - __linkify__ - `false`. Set `true` to autoconvert URL-like text to links. + * - __typographer__ - `false`. Set `true` to enable [some language-neutral + * replacement](https://github.com/markdown-it/markdown-it/blob/master/lib/rules_core/replacements.js) + + * quotes beautification (smartquotes). + * - __quotes__ - `“”‘’`, String or Array. Double + single quotes replacement + * pairs, when typographer enabled and smartquotes on. For example, you can + * use `'«»„“'` for Russian, `'„“‚‘'` for German, and + * `['«\xA0', '\xA0»', '‹\xA0', '\xA0›']` for French (including nbsp). + * - __highlight__ - `null`. Highlighter function for fenced code blocks. + * Highlighter `function (str, lang)` should return escaped HTML. It can also + * return empty string if the source was not changed and should be escaped + * externaly. If result starts with `): + * + * ```javascript + * var hljs = require('highlight.js') // https://highlightjs.org/ + * + * // Actual default values + * var md = require('markdown-it')({ + * highlight: function (str, lang) { + * if (lang && hljs.getLanguage(lang)) { + * try { + * return '
' +
+	 *                hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
+	 *                '
'; + * } catch (__) {} + * } + * + * return '
' + md.utils.escapeHtml(str) + '
'; + * } + * }); + * ``` + * + **/ function MarkdownIt(presetName, options) { + if (!(this instanceof MarkdownIt)) { + return new MarkdownIt(presetName, options); + } + if (!options) { + if (!utils.isString(presetName)) { + options = presetName || {}; + presetName = "default"; + } + } + /** + * MarkdownIt#inline -> ParserInline + * + * Instance of [[ParserInline]]. You may need it to add new rules when + * writing plugins. For simple rules control use [[MarkdownIt.disable]] and + * [[MarkdownIt.enable]]. + **/ this.inline = new parser_inline; + /** + * MarkdownIt#block -> ParserBlock + * + * Instance of [[ParserBlock]]. You may need it to add new rules when + * writing plugins. For simple rules control use [[MarkdownIt.disable]] and + * [[MarkdownIt.enable]]. + **/ this.block = new parser_block; + /** + * MarkdownIt#core -> Core + * + * Instance of [[Core]] chain executor. You may need it to add new rules when + * writing plugins. For simple rules control use [[MarkdownIt.disable]] and + * [[MarkdownIt.enable]]. + **/ this.core = new parser_core; + /** + * MarkdownIt#renderer -> Renderer + * + * Instance of [[Renderer]]. Use it to modify output look. Or to add rendering + * rules for new token types, generated by plugins. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')(); + * + * function myToken(tokens, idx, options, env, self) { + * //... + * return result; + * }; + * + * md.renderer.rules['my_token'] = myToken + * ``` + * + * See [[Renderer]] docs and [source code](https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js). + **/ this.renderer = new renderer; + /** + * MarkdownIt#linkify -> LinkifyIt + * + * [linkify-it](https://github.com/markdown-it/linkify-it) instance. + * Used by [linkify](https://github.com/markdown-it/markdown-it/blob/master/lib/rules_core/linkify.js) + * rule. + **/ this.linkify = new linkifyIt; + /** + * MarkdownIt#validateLink(url) -> Boolean + * + * Link validation function. CommonMark allows too much in links. By default + * we disable `javascript:`, `vbscript:`, `file:` schemas, and almost all `data:...` schemas + * except some embedded image types. + * + * You can change this behaviour: + * + * ```javascript + * var md = require('markdown-it')(); + * // enable everything + * md.validateLink = function () { return true; } + * ``` + **/ this.validateLink = validateLink; + /** + * MarkdownIt#normalizeLink(url) -> String + * + * Function used to encode link url to a machine-readable format, + * which includes url-encoding, punycode, etc. + **/ this.normalizeLink = normalizeLink; + /** + * MarkdownIt#normalizeLinkText(url) -> String + * + * Function used to decode link url to a human-readable format` + **/ this.normalizeLinkText = normalizeLinkText; + // Expose utils & helpers for easy acces from plugins + /** + * MarkdownIt#utils -> utils + * + * Assorted utility functions, useful to write plugins. See details + * [here](https://github.com/markdown-it/markdown-it/blob/master/lib/common/utils.js). + **/ this.utils = utils; + /** + * MarkdownIt#helpers -> helpers + * + * Link components parser functions, useful to write plugins. See details + * [here](https://github.com/markdown-it/markdown-it/blob/master/lib/helpers). + **/ this.helpers = utils.assign({}, helpers); + this.options = {}; + this.configure(presetName); + if (options) { + this.set(options); + } + } + /** chainable + * MarkdownIt.set(options) + * + * Set parser options (in the same format as in constructor). Probably, you + * will never need it, but you can change options after constructor call. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')() + * .set({ html: true, breaks: true }) + * .set({ typographer, true }); + * ``` + * + * __Note:__ To achieve the best possible performance, don't modify a + * `markdown-it` instance options on the fly. If you need multiple configurations + * it's best to create multiple instances and initialize each with separate + * config. + **/ MarkdownIt.prototype.set = function(options) { + utils.assign(this.options, options); + return this; + }; + /** chainable, internal + * MarkdownIt.configure(presets) + * + * Batch load of all options and compenent settings. This is internal method, + * and you probably will not need it. But if you will - see available presets + * and data structure [here](https://github.com/markdown-it/markdown-it/tree/master/lib/presets) + * + * We strongly recommend to use presets instead of direct config loads. That + * will give better compatibility with next versions. + **/ MarkdownIt.prototype.configure = function(presets) { + var self = this, presetName; + if (utils.isString(presets)) { + presetName = presets; + presets = config[presetName]; + if (!presets) { + throw new Error('Wrong `markdown-it` preset "' + presetName + '", check name'); + } + } + if (!presets) { + throw new Error("Wrong `markdown-it` preset, can't be empty"); + } + if (presets.options) { + self.set(presets.options); + } + if (presets.components) { + Object.keys(presets.components).forEach((function(name) { + if (presets.components[name].rules) { + self[name].ruler.enableOnly(presets.components[name].rules); + } + if (presets.components[name].rules2) { + self[name].ruler2.enableOnly(presets.components[name].rules2); + } + })); + } + return this; + }; + /** chainable + * MarkdownIt.enable(list, ignoreInvalid) + * - list (String|Array): rule name or list of rule names to enable + * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. + * + * Enable list or rules. It will automatically find appropriate components, + * containing rules with given names. If rule not found, and `ignoreInvalid` + * not set - throws exception. + * + * ##### Example + * + * ```javascript + * var md = require('markdown-it')() + * .enable(['sub', 'sup']) + * .disable('smartquotes'); + * ``` + **/ MarkdownIt.prototype.enable = function(list, ignoreInvalid) { + var result = []; + if (!Array.isArray(list)) { + list = [ list ]; + } + [ "core", "block", "inline" ].forEach((function(chain) { + result = result.concat(this[chain].ruler.enable(list, true)); + }), this); + result = result.concat(this.inline.ruler2.enable(list, true)); + var missed = list.filter((function(name) { + return result.indexOf(name) < 0; + })); + if (missed.length && !ignoreInvalid) { + throw new Error("MarkdownIt. Failed to enable unknown rule(s): " + missed); + } + return this; + }; + /** chainable + * MarkdownIt.disable(list, ignoreInvalid) + * - list (String|Array): rule name or list of rule names to disable. + * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. + * + * The same as [[MarkdownIt.enable]], but turn specified rules off. + **/ MarkdownIt.prototype.disable = function(list, ignoreInvalid) { + var result = []; + if (!Array.isArray(list)) { + list = [ list ]; + } + [ "core", "block", "inline" ].forEach((function(chain) { + result = result.concat(this[chain].ruler.disable(list, true)); + }), this); + result = result.concat(this.inline.ruler2.disable(list, true)); + var missed = list.filter((function(name) { + return result.indexOf(name) < 0; + })); + if (missed.length && !ignoreInvalid) { + throw new Error("MarkdownIt. Failed to disable unknown rule(s): " + missed); + } + return this; + }; + /** chainable + * MarkdownIt.use(plugin, params) + * + * Load specified plugin with given params into current parser instance. + * It's just a sugar to call `plugin(md, params)` with curring. + * + * ##### Example + * + * ```javascript + * var iterator = require('markdown-it-for-inline'); + * var md = require('markdown-it')() + * .use(iterator, 'foo_replace', 'text', function (tokens, idx) { + * tokens[idx].content = tokens[idx].content.replace(/foo/g, 'bar'); + * }); + * ``` + **/ MarkdownIt.prototype.use = function(plugin /*, params, ... */) { + var args = [ this ].concat(Array.prototype.slice.call(arguments, 1)); + plugin.apply(plugin, args); + return this; + }; + /** internal + * MarkdownIt.parse(src, env) -> Array + * - src (String): source string + * - env (Object): environment sandbox + * + * Parse input string and return list of block tokens (special token type + * "inline" will contain list of inline tokens). You should not call this + * method directly, until you write custom renderer (for example, to produce + * AST). + * + * `env` is used to pass data between "distributed" rules and return additional + * metadata like reference info, needed for the renderer. It also can be used to + * inject data in specific cases. Usually, you will be ok to pass `{}`, + * and then pass updated object to renderer. + **/ MarkdownIt.prototype.parse = function(src, env) { + if (typeof src !== "string") { + throw new Error("Input data should be a String"); + } + var state = new this.core.State(src, this, env); + this.core.process(state); + return state.tokens; + }; + /** + * MarkdownIt.render(src [, env]) -> String + * - src (String): source string + * - env (Object): environment sandbox + * + * Render markdown string into html. It does all magic for you :). + * + * `env` can be used to inject additional metadata (`{}` by default). + * But you will not need it with high probability. See also comment + * in [[MarkdownIt.parse]]. + **/ MarkdownIt.prototype.render = function(src, env) { + env = env || {}; + return this.renderer.render(this.parse(src, env), this.options, env); + }; + /** internal + * MarkdownIt.parseInline(src, env) -> Array + * - src (String): source string + * - env (Object): environment sandbox + * + * The same as [[MarkdownIt.parse]] but skip all block rules. It returns the + * block tokens list with the single `inline` element, containing parsed inline + * tokens in `children` property. Also updates `env` object. + **/ MarkdownIt.prototype.parseInline = function(src, env) { + var state = new this.core.State(src, this, env); + state.inlineMode = true; + this.core.process(state); + return state.tokens; + }; + /** + * MarkdownIt.renderInline(src [, env]) -> String + * - src (String): source string + * - env (Object): environment sandbox + * + * Similar to [[MarkdownIt.render]] but for single paragraph content. Result + * will NOT be wrapped into `

` tags. + **/ MarkdownIt.prototype.renderInline = function(src, env) { + env = env || {}; + return this.renderer.render(this.parseInline(src, env), this.options, env); + }; + var lib = MarkdownIt; + var markdownIt = lib; + return markdownIt; +})); + diff --git a/examples/server/public/deps_tailwindcss.js b/examples/server/public/deps_tailwindcss.js new file mode 100644 index 0000000000..6736cb8ca7 --- /dev/null +++ b/examples/server/public/deps_tailwindcss.js @@ -0,0 +1,82 @@ +(()=>{var Iv=Object.create;var Ui=Object.defineProperty;var Dv=Object.getOwnPropertyDescriptor;var qv=Object.getOwnPropertyNames;var $v=Object.getPrototypeOf,Lv=Object.prototype.hasOwnProperty;var cf=r=>Ui(r,"__esModule",{value:!0});var pf=r=>{if(typeof require!="undefined")return require(r);throw new Error('Dynamic require of "'+r+'" is not supported')};var R=(r,e)=>()=>(r&&(e=r(r=0)),e);var x=(r,e)=>()=>(e||r((e={exports:{}}).exports,e),e.exports),Ge=(r,e)=>{cf(r);for(var t in 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Number(val) : NaN; + return isNaN(n) ? val : n; +}; +let _globalThis; +const getGlobalThis = () => { + return _globalThis || (_globalThis = typeof globalThis !== "undefined" ? globalThis : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : typeof global !== "undefined" ? global : {}); +}; +function genCacheKey(source, options) { + return source + JSON.stringify( + options, + (_, val) => typeof val === "function" ? val.toString() : val + ); +} + +const PatchFlagNames = { + [1]: `TEXT`, + [2]: `CLASS`, + [4]: `STYLE`, + [8]: `PROPS`, + [16]: `FULL_PROPS`, + [32]: `NEED_HYDRATION`, + [64]: `STABLE_FRAGMENT`, + [128]: `KEYED_FRAGMENT`, + [256]: `UNKEYED_FRAGMENT`, + [512]: `NEED_PATCH`, + [1024]: `DYNAMIC_SLOTS`, + [2048]: `DEV_ROOT_FRAGMENT`, + [-1]: `HOISTED`, + [-2]: `BAIL` +}; + +const slotFlagsText = { + [1]: "STABLE", + [2]: "DYNAMIC", + [3]: "FORWARDED" +}; + +const GLOBALS_ALLOWED = "Infinity,undefined,NaN,isFinite,isNaN,parseFloat,parseInt,decodeURI,decodeURIComponent,encodeURI,encodeURIComponent,Math,Number,Date,Array,Object,Boolean,String,RegExp,Map,Set,JSON,Intl,BigInt,console,Error,Symbol"; +const isGloballyAllowed = /* @__PURE__ */ makeMap(GLOBALS_ALLOWED); + +const range = 2; +function generateCodeFrame(source, start = 0, end = source.length) { + start = Math.max(0, Math.min(start, source.length)); + end = Math.max(0, Math.min(end, source.length)); + if (start > end) return ""; + let lines = source.split(/(\r?\n)/); + const newlineSequences = lines.filter((_, idx) => idx % 2 === 1); + lines = lines.filter((_, idx) => idx % 2 === 0); + let count = 0; + const res = []; + for (let i = 0; i < lines.length; i++) { + count += lines[i].length + (newlineSequences[i] && newlineSequences[i].length || 0); + if (count >= start) { + for (let j = i - range; j <= i + range || end > count; j++) { + if (j < 0 || j >= lines.length) continue; + const line = j + 1; + res.push( + `${line}${" ".repeat(Math.max(3 - String(line).length, 0))}| ${lines[j]}` + ); + const lineLength = lines[j].length; + const newLineSeqLength = newlineSequences[j] && newlineSequences[j].length || 0; + if (j === i) { + const pad = start - (count - (lineLength + newLineSeqLength)); + const length = Math.max( + 1, + end > count ? lineLength - pad : end - start + ); + res.push(` | ` + " ".repeat(pad) + "^".repeat(length)); + } else if (j > i) { + if (end > count) { + const length = Math.max(Math.min(end - count, lineLength), 1); + res.push(` | ` + "^".repeat(length)); + } + count += lineLength + newLineSeqLength; + } + } + break; + } + } + return res.join("\n"); +} + +function normalizeStyle(value) { + if (isArray(value)) { + const res = {}; + for (let i = 0; i < value.length; i++) { + const item = value[i]; + const normalized = isString(item) ? parseStringStyle(item) : normalizeStyle(item); + if (normalized) { + for (const key in normalized) { + res[key] = normalized[key]; + } + } + } + return res; + } else if (isString(value) || isObject(value)) { + return value; + } +} +const listDelimiterRE = /;(?![^(]*\))/g; +const propertyDelimiterRE = /:([^]+)/; +const styleCommentRE = /\/\*[^]*?\*\//g; +function parseStringStyle(cssText) { + const ret = {}; + cssText.replace(styleCommentRE, "").split(listDelimiterRE).forEach((item) => { + if (item) { + const tmp = item.split(propertyDelimiterRE); + tmp.length > 1 && (ret[tmp[0].trim()] = tmp[1].trim()); + } + }); + return ret; +} +function stringifyStyle(styles) { + let ret = ""; + if (!styles || isString(styles)) { + return ret; + } + for (const key in styles) { + const value = styles[key]; + if (isString(value) || typeof value === "number") { + const normalizedKey = key.startsWith(`--`) ? key : hyphenate(key); + ret += `${normalizedKey}:${value};`; + } + } + return ret; +} +function normalizeClass(value) { + let res = ""; + if (isString(value)) { + res = value; + } else if (isArray(value)) { + for (let i = 0; i < value.length; i++) { + const normalized = normalizeClass(value[i]); + if (normalized) { + res += normalized + " "; + } + } + } else if (isObject(value)) { + for (const name in value) { + if (value[name]) { + res += name + " "; + } + } + } + return res.trim(); +} +function normalizeProps(props) { + if (!props) return null; + let { class: klass, style } = props; + if (klass && !isString(klass)) { + props.class = normalizeClass(klass); + } + if (style) { + props.style = normalizeStyle(style); + } + return props; +} + +const HTML_TAGS = "html,body,base,head,link,meta,style,title,address,article,aside,footer,header,hgroup,h1,h2,h3,h4,h5,h6,nav,section,div,dd,dl,dt,figcaption,figure,picture,hr,img,li,main,ol,p,pre,ul,a,b,abbr,bdi,bdo,br,cite,code,data,dfn,em,i,kbd,mark,q,rp,rt,ruby,s,samp,small,span,strong,sub,sup,time,u,var,wbr,area,audio,map,track,video,embed,object,param,source,canvas,script,noscript,del,ins,caption,col,colgroup,table,thead,tbody,td,th,tr,button,datalist,fieldset,form,input,label,legend,meter,optgroup,option,output,progress,select,textarea,details,dialog,menu,summary,template,blockquote,iframe,tfoot"; +const SVG_TAGS = "svg,animate,animateMotion,animateTransform,circle,clipPath,color-profile,defs,desc,discard,ellipse,feBlend,feColorMatrix,feComponentTransfer,feComposite,feConvolveMatrix,feDiffuseLighting,feDisplacementMap,feDistantLight,feDropShadow,feFlood,feFuncA,feFuncB,feFuncG,feFuncR,feGaussianBlur,feImage,feMerge,feMergeNode,feMorphology,feOffset,fePointLight,feSpecularLighting,feSpotLight,feTile,feTurbulence,filter,foreignObject,g,hatch,hatchpath,image,line,linearGradient,marker,mask,mesh,meshgradient,meshpatch,meshrow,metadata,mpath,path,pattern,polygon,polyline,radialGradient,rect,set,solidcolor,stop,switch,symbol,text,textPath,title,tspan,unknown,use,view"; +const MATH_TAGS = "annotation,annotation-xml,maction,maligngroup,malignmark,math,menclose,merror,mfenced,mfrac,mfraction,mglyph,mi,mlabeledtr,mlongdiv,mmultiscripts,mn,mo,mover,mpadded,mphantom,mprescripts,mroot,mrow,ms,mscarries,mscarry,msgroup,msline,mspace,msqrt,msrow,mstack,mstyle,msub,msubsup,msup,mtable,mtd,mtext,mtr,munder,munderover,none,semantics"; +const VOID_TAGS = "area,base,br,col,embed,hr,img,input,link,meta,param,source,track,wbr"; +const isHTMLTag = /* @__PURE__ */ makeMap(HTML_TAGS); +const isSVGTag = /* @__PURE__ */ makeMap(SVG_TAGS); +const isMathMLTag = /* @__PURE__ */ makeMap(MATH_TAGS); +const isVoidTag = /* @__PURE__ */ makeMap(VOID_TAGS); + +const specialBooleanAttrs = `itemscope,allowfullscreen,formnovalidate,ismap,nomodule,novalidate,readonly`; +const isSpecialBooleanAttr = /* @__PURE__ */ makeMap(specialBooleanAttrs); +const isBooleanAttr = /* @__PURE__ */ makeMap( + specialBooleanAttrs + `,async,autofocus,autoplay,controls,default,defer,disabled,hidden,inert,loop,open,required,reversed,scoped,seamless,checked,muted,multiple,selected` +); +function includeBooleanAttr(value) { + return !!value || value === ""; +} +const isKnownHtmlAttr = /* @__PURE__ */ makeMap( + `accept,accept-charset,accesskey,action,align,allow,alt,async,autocapitalize,autocomplete,autofocus,autoplay,background,bgcolor,border,buffered,capture,challenge,charset,checked,cite,class,code,codebase,color,cols,colspan,content,contenteditable,contextmenu,controls,coords,crossorigin,csp,data,datetime,decoding,default,defer,dir,dirname,disabled,download,draggable,dropzone,enctype,enterkeyhint,for,form,formaction,formenctype,formmethod,formnovalidate,formtarget,headers,height,hidden,high,href,hreflang,http-equiv,icon,id,importance,inert,integrity,ismap,itemprop,keytype,kind,label,lang,language,loading,list,loop,low,manifest,max,maxlength,minlength,media,min,multiple,muted,name,novalidate,open,optimum,pattern,ping,placeholder,poster,preload,radiogroup,readonly,referrerpolicy,rel,required,reversed,rows,rowspan,sandbox,scope,scoped,selected,shape,size,sizes,slot,span,spellcheck,src,srcdoc,srclang,srcset,start,step,style,summary,tabindex,target,title,translate,type,usemap,value,width,wrap` +); +const isKnownSvgAttr = /* @__PURE__ */ makeMap( + `xmlns,accent-height,accumulate,additive,alignment-baseline,alphabetic,amplitude,arabic-form,ascent,attributeName,attributeType,azimuth,baseFrequency,baseline-shift,baseProfile,bbox,begin,bias,by,calcMode,cap-height,class,clip,clipPathUnits,clip-path,clip-rule,color,color-interpolation,color-interpolation-filters,color-profile,color-rendering,contentScriptType,contentStyleType,crossorigin,cursor,cx,cy,d,decelerate,descent,diffuseConstant,direction,display,divisor,dominant-baseline,dur,dx,dy,edgeMode,elevation,enable-background,end,exponent,fill,fill-opacity,fill-rule,filter,filterRes,filterUnits,flood-color,flood-opacity,font-family,font-size,font-size-adjust,font-stretch,font-style,font-variant,font-weight,format,from,fr,fx,fy,g1,g2,glyph-name,glyph-orientation-horizontal,glyph-orientation-vertical,glyphRef,gradientTransform,gradientUnits,hanging,height,href,hreflang,horiz-adv-x,horiz-origin-x,id,ideographic,image-rendering,in,in2,intercept,k,k1,k2,k3,k4,kernelMatrix,kernelUnitLength,kerning,keyPoints,keySplines,keyTimes,lang,lengthAdjust,letter-spacing,lighting-color,limitingConeAngle,local,marker-end,marker-mid,marker-start,markerHeight,markerUnits,markerWidth,mask,maskContentUnits,maskUnits,mathematical,max,media,method,min,mode,name,numOctaves,offset,opacity,operator,order,orient,orientation,origin,overflow,overline-position,overline-thickness,panose-1,paint-order,path,pathLength,patternContentUnits,patternTransform,patternUnits,ping,pointer-events,points,pointsAtX,pointsAtY,pointsAtZ,preserveAlpha,preserveAspectRatio,primitiveUnits,r,radius,referrerPolicy,refX,refY,rel,rendering-intent,repeatCount,repeatDur,requiredExtensions,requiredFeatures,restart,result,rotate,rx,ry,scale,seed,shape-rendering,slope,spacing,specularConstant,specularExponent,speed,spreadMethod,startOffset,stdDeviation,stemh,stemv,stitchTiles,stop-color,stop-opacity,strikethrough-position,strikethrough-thickness,string,stroke,stroke-dasharray,stroke-dashoffset,stroke-linecap,stroke-linejoin,stroke-miterlimit,stroke-opacity,stroke-width,style,surfaceScale,systemLanguage,tabindex,tableValues,target,targetX,targetY,text-anchor,text-decoration,text-rendering,textLength,to,transform,transform-origin,type,u1,u2,underline-position,underline-thickness,unicode,unicode-bidi,unicode-range,units-per-em,v-alphabetic,v-hanging,v-ideographic,v-mathematical,values,vector-effect,version,vert-adv-y,vert-origin-x,vert-origin-y,viewBox,viewTarget,visibility,width,widths,word-spacing,writing-mode,x,x-height,x1,x2,xChannelSelector,xlink:actuate,xlink:arcrole,xlink:href,xlink:role,xlink:show,xlink:title,xlink:type,xmlns:xlink,xml:base,xml:lang,xml:space,y,y1,y2,yChannelSelector,z,zoomAndPan` +); +function isRenderableAttrValue(value) { + if (value == null) { + return false; + } + const type = typeof value; + return type === "string" || type === "number" || type === "boolean"; +} + +const cssVarNameEscapeSymbolsRE = /[ !"#$%&'()*+,./:;<=>?@[\\\]^`{|}~]/g; +function getEscapedCssVarName(key, doubleEscape) { + return key.replace( + cssVarNameEscapeSymbolsRE, + (s) => `\\${s}` + ); +} + +function looseCompareArrays(a, b) { + if (a.length !== b.length) return false; + let equal = true; + for (let i = 0; equal && i < a.length; i++) { + equal = looseEqual(a[i], b[i]); + } + return equal; +} +function looseEqual(a, b) { + if (a === b) return true; + let aValidType = isDate(a); + let bValidType = isDate(b); + if (aValidType || bValidType) { + return aValidType && bValidType ? a.getTime() === b.getTime() : false; + } + aValidType = isSymbol(a); + bValidType = isSymbol(b); + if (aValidType || bValidType) { + return a === b; + } + aValidType = isArray(a); + bValidType = isArray(b); + if (aValidType || bValidType) { + return aValidType && bValidType ? looseCompareArrays(a, b) : false; + } + aValidType = isObject(a); + bValidType = isObject(b); + if (aValidType || bValidType) { + if (!aValidType || !bValidType) { + return false; + } + const aKeysCount = Object.keys(a).length; + const bKeysCount = Object.keys(b).length; + if (aKeysCount !== bKeysCount) { + return false; + } + for (const key in a) { + const aHasKey = a.hasOwnProperty(key); + const bHasKey = b.hasOwnProperty(key); + if (aHasKey && !bHasKey || !aHasKey && bHasKey || !looseEqual(a[key], b[key])) { + return false; + } + } + } + return String(a) === String(b); +} +function looseIndexOf(arr, val) { + return arr.findIndex((item) => looseEqual(item, val)); +} + +const isRef$1 = (val) => { + return !!(val && val["__v_isRef"] === true); +}; +const toDisplayString = (val) => { + return isString(val) ? val : val == null ? "" : isArray(val) || isObject(val) && (val.toString === objectToString || !isFunction(val.toString)) ? isRef$1(val) ? toDisplayString(val.value) : JSON.stringify(val, replacer, 2) : String(val); +}; +const replacer = (_key, val) => { + if (isRef$1(val)) { + return replacer(_key, val.value); + } else if (isMap(val)) { + return { + [`Map(${val.size})`]: [...val.entries()].reduce( + (entries, [key, val2], i) => { + entries[stringifySymbol(key, i) + " =>"] = val2; + return entries; + }, + {} + ) + }; + } else if (isSet(val)) { + return { + [`Set(${val.size})`]: [...val.values()].map((v) => stringifySymbol(v)) + }; + } else if (isSymbol(val)) { + return stringifySymbol(val); + } else if (isObject(val) && !isArray(val) && !isPlainObject(val)) { + return String(val); + } + return val; +}; +const stringifySymbol = (v, i = "") => { + var _a; + return ( + // Symbol.description in es2019+ so we need to cast here to pass + // the lib: es2016 check + isSymbol(v) ? `Symbol(${(_a = v.description) != null ? _a : i})` : v + ); +}; + +function warn$2(msg, ...args) { + console.warn(`[Vue warn] ${msg}`, ...args); +} + +let activeEffectScope; +class EffectScope { + constructor(detached = false) { + this.detached = detached; + /** + * @internal + */ + this._active = true; + /** + * @internal + */ + this.effects = []; + /** + * @internal + */ + this.cleanups = []; + this._isPaused = false; + this.parent = activeEffectScope; + if (!detached && activeEffectScope) { + this.index = (activeEffectScope.scopes || (activeEffectScope.scopes = [])).push( + this + ) - 1; + } + } + get active() { + return this._active; + } + pause() { + if (this._active) { + this._isPaused = true; + let i, l; + if (this.scopes) { + for (i = 0, l = this.scopes.length; i < l; i++) { + this.scopes[i].pause(); + } + } + for (i = 0, l = this.effects.length; i < l; i++) { + this.effects[i].pause(); + } + } + } + /** + * Resumes the effect scope, including all child scopes and effects. + */ + resume() { + if (this._active) { + if (this._isPaused) { + this._isPaused = false; + let i, l; + if (this.scopes) { + for (i = 0, l = this.scopes.length; i < l; i++) { + this.scopes[i].resume(); + } + } + for (i = 0, l = this.effects.length; i < l; i++) { + this.effects[i].resume(); + } + } + } + } + run(fn) { + if (this._active) { + const currentEffectScope = activeEffectScope; + try { + activeEffectScope = this; + return fn(); + } finally { + activeEffectScope = currentEffectScope; + } + } else { + warn$2(`cannot run an inactive effect scope.`); + } + } + /** + * This should only be called on non-detached scopes + * @internal + */ + on() { + activeEffectScope = this; + } + /** + * This should only be called on non-detached scopes + * @internal + */ + off() { + activeEffectScope = this.parent; + } + stop(fromParent) { + if (this._active) { + let i, l; + for (i = 0, l = this.effects.length; i < l; i++) { + this.effects[i].stop(); + } + for (i = 0, l = this.cleanups.length; i < l; i++) { + this.cleanups[i](); + } + if (this.scopes) { + for (i = 0, l = this.scopes.length; i < l; i++) { + this.scopes[i].stop(true); + } + } + if (!this.detached && this.parent && !fromParent) { + const last = this.parent.scopes.pop(); + if (last && last !== this) { + this.parent.scopes[this.index] = last; + last.index = this.index; + } + } + this.parent = void 0; + this._active = false; + } + } +} +function effectScope(detached) { + return new EffectScope(detached); +} +function getCurrentScope() { + return activeEffectScope; +} +function onScopeDispose(fn, failSilently = false) { + if (activeEffectScope) { + activeEffectScope.cleanups.push(fn); + } else if (!failSilently) { + warn$2( + `onScopeDispose() is called when there is no active effect scope to be associated with.` + ); + } +} + +let activeSub; +const pausedQueueEffects = /* @__PURE__ */ new WeakSet(); +class ReactiveEffect { + constructor(fn) { + this.fn = fn; + /** + * @internal + */ + this.deps = void 0; + /** + * @internal + */ + this.depsTail = void 0; + /** + * @internal + */ + this.flags = 1 | 4; + /** + * @internal + */ + this.next = void 0; + /** + * @internal + */ + this.cleanup = void 0; + this.scheduler = void 0; + if (activeEffectScope && activeEffectScope.active) { + activeEffectScope.effects.push(this); + } + } + pause() { + this.flags |= 64; + } + resume() { + if (this.flags & 64) { + this.flags &= ~64; + if (pausedQueueEffects.has(this)) { + pausedQueueEffects.delete(this); + this.trigger(); + } + } + } + /** + * @internal + */ + notify() { + if (this.flags & 2 && !(this.flags & 32)) { + return; + } + if (!(this.flags & 8)) { + batch(this); + } + } + run() { + if (!(this.flags & 1)) { + return this.fn(); + } + this.flags |= 2; + cleanupEffect(this); + prepareDeps(this); + const prevEffect = activeSub; + const prevShouldTrack = shouldTrack; + activeSub = this; + shouldTrack = true; + try { + return this.fn(); + } finally { + if (activeSub !== this) { + warn$2( + "Active effect was not restored correctly - this is likely a Vue internal bug." + ); + } + cleanupDeps(this); + activeSub = prevEffect; + shouldTrack = prevShouldTrack; + this.flags &= ~2; + } + } + stop() { + if (this.flags & 1) { + for (let link = this.deps; link; link = link.nextDep) { + removeSub(link); + } + this.deps = this.depsTail = void 0; + cleanupEffect(this); + this.onStop && this.onStop(); + this.flags &= ~1; + } + } + trigger() { + if (this.flags & 64) { + pausedQueueEffects.add(this); + } else if (this.scheduler) { + this.scheduler(); + } else { + this.runIfDirty(); + } + } + /** + * @internal + */ + runIfDirty() { + if (isDirty(this)) { + this.run(); + } + } + get dirty() { + return isDirty(this); + } +} +let batchDepth = 0; +let batchedSub; +let batchedComputed; +function batch(sub, isComputed = false) { + sub.flags |= 8; + if (isComputed) { + sub.next = batchedComputed; + batchedComputed = sub; + return; + } + sub.next = batchedSub; + batchedSub = sub; +} +function startBatch() { + batchDepth++; +} +function endBatch() { + if (--batchDepth > 0) { + return; + } + if (batchedComputed) { + let e = batchedComputed; + batchedComputed = void 0; + while (e) { + const next = e.next; + e.next = void 0; + e.flags &= ~8; + e = next; + } + } + let error; + while (batchedSub) { + let e = batchedSub; + batchedSub = void 0; + while (e) { + const next = e.next; + e.next = void 0; + e.flags &= ~8; + if (e.flags & 1) { + try { + ; + e.trigger(); + } catch (err) { + if (!error) error = err; + } + } + e = next; + } + } + if (error) throw error; +} +function prepareDeps(sub) { + for (let link = sub.deps; link; link = link.nextDep) { + link.version = -1; + link.prevActiveLink = link.dep.activeLink; + link.dep.activeLink = link; + } +} +function cleanupDeps(sub) { + let head; + let tail = sub.depsTail; + let link = tail; + while (link) { + const prev = link.prevDep; + if (link.version === -1) { + if (link === tail) tail = prev; + removeSub(link); + removeDep(link); + } else { + head = link; + } + link.dep.activeLink = link.prevActiveLink; + link.prevActiveLink = void 0; + link = prev; + } + sub.deps = head; + sub.depsTail = tail; +} +function isDirty(sub) { + for (let link = sub.deps; link; link = link.nextDep) { + if (link.dep.version !== link.version || link.dep.computed && (refreshComputed(link.dep.computed) || link.dep.version !== link.version)) { + return true; + } + } + if (sub._dirty) { + return true; + } + return false; +} +function refreshComputed(computed) { + if (computed.flags & 4 && !(computed.flags & 16)) { + return; + } + computed.flags &= ~16; + if (computed.globalVersion === globalVersion) { + return; + } + computed.globalVersion = globalVersion; + const dep = computed.dep; + computed.flags |= 2; + if (dep.version > 0 && !computed.isSSR && computed.deps && !isDirty(computed)) { + computed.flags &= ~2; + return; + } + const prevSub = activeSub; + const prevShouldTrack = shouldTrack; + activeSub = computed; + shouldTrack = true; + try { + prepareDeps(computed); + const value = computed.fn(computed._value); + if (dep.version === 0 || hasChanged(value, computed._value)) { + computed._value = value; + dep.version++; + } + } catch (err) { + dep.version++; + throw err; + } finally { + activeSub = prevSub; + shouldTrack = prevShouldTrack; + cleanupDeps(computed); + computed.flags &= ~2; + } +} +function removeSub(link, soft = false) { + const { dep, prevSub, nextSub } = link; + if (prevSub) { + prevSub.nextSub = nextSub; + link.prevSub = void 0; + } + if (nextSub) { + nextSub.prevSub = prevSub; + link.nextSub = void 0; + } + if (dep.subsHead === link) { + dep.subsHead = nextSub; + } + if (dep.subs === link) { + dep.subs = prevSub; + if (!prevSub && dep.computed) { + dep.computed.flags &= ~4; + for (let l = dep.computed.deps; l; l = l.nextDep) { + removeSub(l, true); + } + } + } + if (!soft && !--dep.sc && dep.map) { + dep.map.delete(dep.key); + } +} +function removeDep(link) { + const { prevDep, nextDep } = link; + if (prevDep) { + prevDep.nextDep = nextDep; + link.prevDep = void 0; + } + if (nextDep) { + nextDep.prevDep = prevDep; + link.nextDep = void 0; + } +} +function effect(fn, options) { + if (fn.effect instanceof ReactiveEffect) { + fn = fn.effect.fn; + } + const e = new ReactiveEffect(fn); + if (options) { + extend(e, options); + } + try { + e.run(); + } catch (err) { + e.stop(); + throw err; + } + const runner = e.run.bind(e); + runner.effect = e; + return runner; +} +function stop(runner) { + runner.effect.stop(); +} +let shouldTrack = true; +const trackStack = []; +function pauseTracking() { + trackStack.push(shouldTrack); + shouldTrack = false; +} +function resetTracking() { + const last = trackStack.pop(); + shouldTrack = last === void 0 ? true : last; +} +function cleanupEffect(e) { + const { cleanup } = e; + e.cleanup = void 0; + if (cleanup) { + const prevSub = activeSub; + activeSub = void 0; + try { + cleanup(); + } finally { + activeSub = prevSub; + } + } +} + +let globalVersion = 0; +class Link { + constructor(sub, dep) { + this.sub = sub; + this.dep = dep; + this.version = dep.version; + this.nextDep = this.prevDep = this.nextSub = this.prevSub = this.prevActiveLink = void 0; + } +} +class Dep { + constructor(computed) { + this.computed = computed; + this.version = 0; + /** + * Link between this dep and the current active effect + */ + this.activeLink = void 0; + /** + * Doubly linked list representing the subscribing effects (tail) + */ + this.subs = void 0; + /** + * For object property deps cleanup + */ + this.map = void 0; + this.key = void 0; + /** + * Subscriber counter + */ + this.sc = 0; + { + this.subsHead = void 0; + } + } + track(debugInfo) { + if (!activeSub || !shouldTrack || activeSub === this.computed) { + return; + } + let link = this.activeLink; + if (link === void 0 || link.sub !== activeSub) { + link = this.activeLink = new Link(activeSub, this); + if (!activeSub.deps) { + activeSub.deps = activeSub.depsTail = link; + } else { + link.prevDep = activeSub.depsTail; + activeSub.depsTail.nextDep = link; + activeSub.depsTail = link; + } + addSub(link); + } else if (link.version === -1) { + link.version = this.version; + if (link.nextDep) { + const next = link.nextDep; + next.prevDep = link.prevDep; + if (link.prevDep) { + link.prevDep.nextDep = next; + } + link.prevDep = activeSub.depsTail; + link.nextDep = void 0; + activeSub.depsTail.nextDep = link; + activeSub.depsTail = link; + if (activeSub.deps === link) { + activeSub.deps = next; + } + } + } + if (activeSub.onTrack) { + activeSub.onTrack( + extend( + { + effect: activeSub + }, + debugInfo + ) + ); + } + return link; + } + trigger(debugInfo) { + this.version++; + globalVersion++; + this.notify(debugInfo); + } + notify(debugInfo) { + startBatch(); + try { + if (true) { + for (let head = this.subsHead; head; head = head.nextSub) { + if (head.sub.onTrigger && !(head.sub.flags & 8)) { + head.sub.onTrigger( + extend( + { + effect: head.sub + }, + debugInfo + ) + ); + } + } + } + for (let link = this.subs; link; link = link.prevSub) { + if (link.sub.notify()) { + ; + link.sub.dep.notify(); + } + } + } finally { + endBatch(); + } + } +} +function addSub(link) { + link.dep.sc++; + if (link.sub.flags & 4) { + const computed = link.dep.computed; + if (computed && !link.dep.subs) { + computed.flags |= 4 | 16; + for (let l = computed.deps; l; l = l.nextDep) { + addSub(l); + } + } + const currentTail = link.dep.subs; + if (currentTail !== link) { + link.prevSub = currentTail; + if (currentTail) currentTail.nextSub = link; + } + if (link.dep.subsHead === void 0) { + link.dep.subsHead = link; + } + link.dep.subs = link; + } +} +const targetMap = /* @__PURE__ */ new WeakMap(); +const ITERATE_KEY = Symbol( + "Object iterate" +); +const MAP_KEY_ITERATE_KEY = Symbol( + "Map keys iterate" +); +const ARRAY_ITERATE_KEY = Symbol( + "Array iterate" +); +function track(target, type, key) { + if (shouldTrack && activeSub) { + let depsMap = targetMap.get(target); + if (!depsMap) { + targetMap.set(target, depsMap = /* @__PURE__ */ new Map()); + } + let dep = depsMap.get(key); + if (!dep) { + depsMap.set(key, dep = new Dep()); + dep.map = depsMap; + dep.key = key; + } + { + dep.track({ + target, + type, + key + }); + } + } +} +function trigger(target, type, key, newValue, oldValue, oldTarget) { + const depsMap = targetMap.get(target); + if (!depsMap) { + globalVersion++; + return; + } + const run = (dep) => { + if (dep) { + { + dep.trigger({ + target, + type, + key, + newValue, + oldValue, + oldTarget + }); + } + } + }; + startBatch(); + if (type === "clear") { + depsMap.forEach(run); + } else { + const targetIsArray = isArray(target); + const isArrayIndex = targetIsArray && isIntegerKey(key); + if (targetIsArray && key === "length") { + const newLength = Number(newValue); + depsMap.forEach((dep, key2) => { + if (key2 === "length" || key2 === ARRAY_ITERATE_KEY || !isSymbol(key2) && key2 >= newLength) { + run(dep); + } + }); + } else { + if (key !== void 0 || depsMap.has(void 0)) { + run(depsMap.get(key)); + } + if (isArrayIndex) { + run(depsMap.get(ARRAY_ITERATE_KEY)); + } + switch (type) { + case "add": + if (!targetIsArray) { + run(depsMap.get(ITERATE_KEY)); + if (isMap(target)) { + run(depsMap.get(MAP_KEY_ITERATE_KEY)); + } + } else if (isArrayIndex) { + run(depsMap.get("length")); + } + break; + case "delete": + if (!targetIsArray) { + run(depsMap.get(ITERATE_KEY)); + if (isMap(target)) { + run(depsMap.get(MAP_KEY_ITERATE_KEY)); + } + } + break; + case "set": + if (isMap(target)) { + run(depsMap.get(ITERATE_KEY)); + } + break; + } + } + } + endBatch(); +} +function getDepFromReactive(object, key) { + const depMap = targetMap.get(object); + return depMap && depMap.get(key); +} + +function reactiveReadArray(array) { + const raw = toRaw(array); + if (raw === array) return raw; + track(raw, "iterate", ARRAY_ITERATE_KEY); + return isShallow(array) ? raw : raw.map(toReactive); +} +function shallowReadArray(arr) { + track(arr = toRaw(arr), "iterate", ARRAY_ITERATE_KEY); + return arr; +} +const arrayInstrumentations = { + __proto__: null, + [Symbol.iterator]() { + return iterator(this, Symbol.iterator, toReactive); + }, + concat(...args) { + return reactiveReadArray(this).concat( + ...args.map((x) => isArray(x) ? reactiveReadArray(x) : x) + ); + }, + entries() { + return iterator(this, "entries", (value) => { + value[1] = toReactive(value[1]); + return value; + }); + }, + every(fn, thisArg) { + return apply(this, "every", fn, thisArg, void 0, arguments); + }, + filter(fn, thisArg) { + return apply(this, "filter", fn, thisArg, (v) => v.map(toReactive), arguments); + }, + find(fn, thisArg) { + return apply(this, "find", fn, thisArg, toReactive, arguments); + }, + findIndex(fn, thisArg) { + return apply(this, "findIndex", fn, thisArg, void 0, arguments); + }, + findLast(fn, thisArg) { + return apply(this, "findLast", fn, thisArg, toReactive, arguments); + }, + findLastIndex(fn, thisArg) { + return apply(this, "findLastIndex", fn, thisArg, void 0, arguments); + }, + // flat, flatMap could benefit from ARRAY_ITERATE but are not straight-forward to implement + forEach(fn, thisArg) { + return apply(this, "forEach", fn, thisArg, void 0, arguments); + }, + includes(...args) { + return searchProxy(this, "includes", args); + }, + indexOf(...args) { + return searchProxy(this, "indexOf", args); + }, + join(separator) { + return reactiveReadArray(this).join(separator); + }, + // keys() iterator only reads `length`, no optimisation required + lastIndexOf(...args) { + return searchProxy(this, "lastIndexOf", args); + }, + map(fn, thisArg) { + return apply(this, "map", fn, thisArg, void 0, arguments); + }, + pop() { + return noTracking(this, "pop"); + }, + push(...args) { + return noTracking(this, "push", args); + }, + reduce(fn, ...args) { + return reduce(this, "reduce", fn, args); + }, + reduceRight(fn, ...args) { + return reduce(this, "reduceRight", fn, args); + }, + shift() { + return noTracking(this, "shift"); + }, + // slice could use ARRAY_ITERATE but also seems to beg for range tracking + some(fn, thisArg) { + return apply(this, "some", fn, thisArg, void 0, arguments); + }, + splice(...args) { + return noTracking(this, "splice", args); + }, + toReversed() { + return reactiveReadArray(this).toReversed(); + }, + toSorted(comparer) { + return reactiveReadArray(this).toSorted(comparer); + }, + toSpliced(...args) { + return reactiveReadArray(this).toSpliced(...args); + }, + unshift(...args) { + return noTracking(this, "unshift", args); + }, + values() { + return iterator(this, "values", toReactive); + } +}; +function iterator(self, method, wrapValue) { + const arr = shallowReadArray(self); + const iter = arr[method](); + if (arr !== self && !isShallow(self)) { + iter._next = iter.next; + iter.next = () => { + const result = iter._next(); + if (result.value) { + result.value = wrapValue(result.value); + } + return result; + }; + } + return iter; +} +const arrayProto = Array.prototype; +function apply(self, method, fn, thisArg, wrappedRetFn, args) { + const arr = shallowReadArray(self); + const needsWrap = arr !== self && !isShallow(self); + const methodFn = arr[method]; + if (methodFn !== arrayProto[method]) { + const result2 = methodFn.apply(self, args); + return needsWrap ? toReactive(result2) : result2; + } + let wrappedFn = fn; + if (arr !== self) { + if (needsWrap) { + wrappedFn = function(item, index) { + return fn.call(this, toReactive(item), index, self); + }; + } else if (fn.length > 2) { + wrappedFn = function(item, index) { + return fn.call(this, item, index, self); + }; + } + } + const result = methodFn.call(arr, wrappedFn, thisArg); + return needsWrap && wrappedRetFn ? wrappedRetFn(result) : result; +} +function reduce(self, method, fn, args) { + const arr = shallowReadArray(self); + let wrappedFn = fn; + if (arr !== self) { + if (!isShallow(self)) { + wrappedFn = function(acc, item, index) { + return fn.call(this, acc, toReactive(item), index, self); + }; + } else if (fn.length > 3) { + wrappedFn = function(acc, item, index) { + return fn.call(this, acc, item, index, self); + }; + } + } + return arr[method](wrappedFn, ...args); +} +function searchProxy(self, method, args) { + const arr = toRaw(self); + track(arr, "iterate", ARRAY_ITERATE_KEY); + const res = arr[method](...args); + if ((res === -1 || res === false) && isProxy(args[0])) { + args[0] = toRaw(args[0]); + return arr[method](...args); + } + return res; +} +function noTracking(self, method, args = []) { + pauseTracking(); + startBatch(); + const res = toRaw(self)[method].apply(self, args); + endBatch(); + resetTracking(); + return res; +} + +const isNonTrackableKeys = /* @__PURE__ */ makeMap(`__proto__,__v_isRef,__isVue`); +const builtInSymbols = new Set( + /* @__PURE__ */ Object.getOwnPropertyNames(Symbol).filter((key) => key !== "arguments" && key !== "caller").map((key) => Symbol[key]).filter(isSymbol) +); +function hasOwnProperty(key) { + if (!isSymbol(key)) key = String(key); + const obj = toRaw(this); + track(obj, "has", key); + return obj.hasOwnProperty(key); +} +class BaseReactiveHandler { + constructor(_isReadonly = false, _isShallow = false) { + this._isReadonly = _isReadonly; + this._isShallow = _isShallow; + } + get(target, key, receiver) { + const isReadonly2 = this._isReadonly, isShallow2 = this._isShallow; + if (key === "__v_isReactive") { + return !isReadonly2; + } else if (key === "__v_isReadonly") { + return isReadonly2; + } else if (key === "__v_isShallow") { + return isShallow2; + } else if (key === "__v_raw") { + if (receiver === (isReadonly2 ? isShallow2 ? shallowReadonlyMap : readonlyMap : isShallow2 ? shallowReactiveMap : reactiveMap).get(target) || // receiver is not the reactive proxy, but has the same prototype + // this means the receiver is a user proxy of the reactive proxy + Object.getPrototypeOf(target) === Object.getPrototypeOf(receiver)) { + return target; + } + return; + } + const targetIsArray = isArray(target); + if (!isReadonly2) { + let fn; + if (targetIsArray && (fn = arrayInstrumentations[key])) { + return fn; + } + if (key === "hasOwnProperty") { + return hasOwnProperty; + } + } + const res = Reflect.get( + target, + key, + // if this is a proxy wrapping a ref, return methods using the raw ref + // as receiver so that we don't have to call `toRaw` on the ref in all + // its class methods + isRef(target) ? target : receiver + ); + if (isSymbol(key) ? builtInSymbols.has(key) : isNonTrackableKeys(key)) { + return res; + } + if (!isReadonly2) { + track(target, "get", key); + } + if (isShallow2) { + return res; + } + if (isRef(res)) { + return targetIsArray && isIntegerKey(key) ? res : res.value; + } + if (isObject(res)) { + return isReadonly2 ? readonly(res) : reactive(res); + } + return res; + } +} +class MutableReactiveHandler extends BaseReactiveHandler { + constructor(isShallow2 = false) { + super(false, isShallow2); + } + set(target, key, value, receiver) { + let oldValue = target[key]; + if (!this._isShallow) { + const isOldValueReadonly = isReadonly(oldValue); + if (!isShallow(value) && !isReadonly(value)) { + oldValue = toRaw(oldValue); + value = toRaw(value); + } + if (!isArray(target) && isRef(oldValue) && !isRef(value)) { + if (isOldValueReadonly) { + return false; + } else { + oldValue.value = value; + return true; + } + } + } + const hadKey = isArray(target) && isIntegerKey(key) ? Number(key) < target.length : hasOwn(target, key); + const result = Reflect.set( + target, + key, + value, + isRef(target) ? target : receiver + ); + if (target === toRaw(receiver)) { + if (!hadKey) { + trigger(target, "add", key, value); + } else if (hasChanged(value, oldValue)) { + trigger(target, "set", key, value, oldValue); + } + } + return result; + } + deleteProperty(target, key) { + const hadKey = hasOwn(target, key); + const oldValue = target[key]; + const result = Reflect.deleteProperty(target, key); + if (result && hadKey) { + trigger(target, "delete", key, void 0, oldValue); + } + return result; + } + has(target, key) { + const result = Reflect.has(target, key); + if (!isSymbol(key) || !builtInSymbols.has(key)) { + track(target, "has", key); + } + return result; + } + ownKeys(target) { + track( + target, + "iterate", + isArray(target) ? "length" : ITERATE_KEY + ); + return Reflect.ownKeys(target); + } +} +class ReadonlyReactiveHandler extends BaseReactiveHandler { + constructor(isShallow2 = false) { + super(true, isShallow2); + } + set(target, key) { + { + warn$2( + `Set operation on key "${String(key)}" failed: target is readonly.`, + target + ); + } + return true; + } + deleteProperty(target, key) { + { + warn$2( + `Delete operation on key "${String(key)}" failed: target is readonly.`, + target + ); + } + return true; + } +} +const mutableHandlers = /* @__PURE__ */ new MutableReactiveHandler(); +const readonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(); +const shallowReactiveHandlers = /* @__PURE__ */ new MutableReactiveHandler(true); +const shallowReadonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(true); + +const toShallow = (value) => value; +const getProto = (v) => Reflect.getPrototypeOf(v); +function createIterableMethod(method, isReadonly2, isShallow2) { + return function(...args) { + const target = this["__v_raw"]; + const rawTarget = toRaw(target); + const targetIsMap = isMap(rawTarget); + const isPair = method === "entries" || method === Symbol.iterator && targetIsMap; + const isKeyOnly = method === "keys" && targetIsMap; + const innerIterator = target[method](...args); + const wrap = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive; + !isReadonly2 && track( + rawTarget, + "iterate", + isKeyOnly ? MAP_KEY_ITERATE_KEY : ITERATE_KEY + ); + return { + // iterator protocol + next() { + const { value, done } = innerIterator.next(); + return done ? { value, done } : { + value: isPair ? [wrap(value[0]), wrap(value[1])] : wrap(value), + done + }; + }, + // iterable protocol + [Symbol.iterator]() { + return this; + } + }; + }; +} +function createReadonlyMethod(type) { + return function(...args) { + { + const key = args[0] ? `on key "${args[0]}" ` : ``; + warn$2( + `${capitalize(type)} operation ${key}failed: target is readonly.`, + toRaw(this) + ); + } + return type === "delete" ? false : type === "clear" ? void 0 : this; + }; +} +function createInstrumentations(readonly, shallow) { + const instrumentations = { + get(key) { + const target = this["__v_raw"]; + const rawTarget = toRaw(target); + const rawKey = toRaw(key); + if (!readonly) { + if (hasChanged(key, rawKey)) { + track(rawTarget, "get", key); + } + track(rawTarget, "get", rawKey); + } + const { has } = getProto(rawTarget); + const wrap = shallow ? toShallow : readonly ? toReadonly : toReactive; + if (has.call(rawTarget, key)) { + return wrap(target.get(key)); + } else if (has.call(rawTarget, rawKey)) { + return wrap(target.get(rawKey)); + } else if (target !== rawTarget) { + target.get(key); + } + }, + get size() { + const target = this["__v_raw"]; + !readonly && track(toRaw(target), "iterate", ITERATE_KEY); + return Reflect.get(target, "size", target); + }, + has(key) { + const target = this["__v_raw"]; + const rawTarget = toRaw(target); + const rawKey = toRaw(key); + if (!readonly) { + if (hasChanged(key, rawKey)) { + track(rawTarget, "has", key); + } + track(rawTarget, "has", rawKey); + } + return key === rawKey ? target.has(key) : target.has(key) || target.has(rawKey); + }, + forEach(callback, thisArg) { + const observed = this; + const target = observed["__v_raw"]; + const rawTarget = toRaw(target); + const wrap = shallow ? toShallow : readonly ? toReadonly : toReactive; + !readonly && track(rawTarget, "iterate", ITERATE_KEY); + return target.forEach((value, key) => { + return callback.call(thisArg, wrap(value), wrap(key), observed); + }); + } + }; + extend( + instrumentations, + readonly ? { + add: createReadonlyMethod("add"), + set: createReadonlyMethod("set"), + delete: createReadonlyMethod("delete"), + clear: createReadonlyMethod("clear") + } : { + add(value) { + if (!shallow && !isShallow(value) && !isReadonly(value)) { + value = toRaw(value); + } + const target = toRaw(this); + const proto = getProto(target); + const hadKey = proto.has.call(target, value); + if (!hadKey) { + target.add(value); + trigger(target, "add", value, value); + } + return this; + }, + set(key, value) { + if (!shallow && !isShallow(value) && !isReadonly(value)) { + value = toRaw(value); + } + const target = toRaw(this); + const { has, get } = getProto(target); + let hadKey = has.call(target, key); + if (!hadKey) { + key = toRaw(key); + hadKey = has.call(target, key); + } else { + checkIdentityKeys(target, has, key); + } + const oldValue = get.call(target, key); + target.set(key, value); + if (!hadKey) { + trigger(target, "add", key, value); + } else if (hasChanged(value, oldValue)) { + trigger(target, "set", key, value, oldValue); + } + return this; + }, + delete(key) { + const target = toRaw(this); + const { has, get } = getProto(target); + let hadKey = has.call(target, key); + if (!hadKey) { + key = toRaw(key); + hadKey = has.call(target, key); + } else { + checkIdentityKeys(target, has, key); + } + const oldValue = get ? get.call(target, key) : void 0; + const result = target.delete(key); + if (hadKey) { + trigger(target, "delete", key, void 0, oldValue); + } + return result; + }, + clear() { + const target = toRaw(this); + const hadItems = target.size !== 0; + const oldTarget = isMap(target) ? new Map(target) : new Set(target) ; + const result = target.clear(); + if (hadItems) { + trigger( + target, + "clear", + void 0, + void 0, + oldTarget + ); + } + return result; + } + } + ); + const iteratorMethods = [ + "keys", + "values", + "entries", + Symbol.iterator + ]; + iteratorMethods.forEach((method) => { + instrumentations[method] = createIterableMethod(method, readonly, shallow); + }); + return instrumentations; +} +function createInstrumentationGetter(isReadonly2, shallow) { + const instrumentations = createInstrumentations(isReadonly2, shallow); + return (target, key, receiver) => { + if (key === "__v_isReactive") { + return !isReadonly2; + } else if (key === "__v_isReadonly") { + return isReadonly2; + } else if (key === "__v_raw") { + return target; + } + return Reflect.get( + hasOwn(instrumentations, key) && key in target ? instrumentations : target, + key, + receiver + ); + }; +} +const mutableCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(false, false) +}; +const shallowCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(false, true) +}; +const readonlyCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(true, false) +}; +const shallowReadonlyCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(true, true) +}; +function checkIdentityKeys(target, has, key) { + const rawKey = toRaw(key); + if (rawKey !== key && has.call(target, rawKey)) { + const type = toRawType(target); + warn$2( + `Reactive ${type} contains both the raw and reactive versions of the same object${type === `Map` ? ` as keys` : ``}, which can lead to inconsistencies. Avoid differentiating between the raw and reactive versions of an object and only use the reactive version if possible.` + ); + } +} + +const reactiveMap = /* @__PURE__ */ new WeakMap(); +const shallowReactiveMap = /* @__PURE__ */ new WeakMap(); +const readonlyMap = /* @__PURE__ */ new WeakMap(); +const shallowReadonlyMap = /* @__PURE__ */ new WeakMap(); +function targetTypeMap(rawType) { + switch (rawType) { + case "Object": + case "Array": + return 1 /* COMMON */; + case "Map": + case "Set": + case "WeakMap": + case "WeakSet": + return 2 /* COLLECTION */; + default: + return 0 /* INVALID */; + } +} +function getTargetType(value) { + return value["__v_skip"] || !Object.isExtensible(value) ? 0 /* INVALID */ : targetTypeMap(toRawType(value)); +} +function reactive(target) { + if (isReadonly(target)) { + return target; + } + return createReactiveObject( + target, + false, + mutableHandlers, + mutableCollectionHandlers, + reactiveMap + ); +} +function shallowReactive(target) { + return createReactiveObject( + target, + false, + shallowReactiveHandlers, + shallowCollectionHandlers, + shallowReactiveMap + ); +} +function readonly(target) { + return createReactiveObject( + target, + true, + readonlyHandlers, + readonlyCollectionHandlers, + readonlyMap + ); +} +function shallowReadonly(target) { + return createReactiveObject( + target, + true, + shallowReadonlyHandlers, + shallowReadonlyCollectionHandlers, + shallowReadonlyMap + ); +} +function createReactiveObject(target, isReadonly2, baseHandlers, collectionHandlers, proxyMap) { + if (!isObject(target)) { + { + warn$2( + `value cannot be made ${isReadonly2 ? "readonly" : "reactive"}: ${String( + target + )}` + ); + } + return target; + } + if (target["__v_raw"] && !(isReadonly2 && target["__v_isReactive"])) { + return target; + } + const existingProxy = proxyMap.get(target); + if (existingProxy) { + return existingProxy; + } + const targetType = getTargetType(target); + if (targetType === 0 /* INVALID */) { + return target; + } + const proxy = new Proxy( + target, + targetType === 2 /* COLLECTION */ ? collectionHandlers : baseHandlers + ); + proxyMap.set(target, proxy); + return proxy; +} +function isReactive(value) { + if (isReadonly(value)) { + return isReactive(value["__v_raw"]); + } + return !!(value && value["__v_isReactive"]); +} +function isReadonly(value) { + return !!(value && value["__v_isReadonly"]); +} +function isShallow(value) { + return !!(value && value["__v_isShallow"]); +} +function isProxy(value) { + return value ? !!value["__v_raw"] : false; +} +function toRaw(observed) { + const raw = observed && observed["__v_raw"]; + return raw ? toRaw(raw) : observed; +} +function markRaw(value) { + if (!hasOwn(value, "__v_skip") && Object.isExtensible(value)) { + def(value, "__v_skip", true); + } + return value; +} +const toReactive = (value) => isObject(value) ? reactive(value) : value; +const toReadonly = (value) => isObject(value) ? readonly(value) : value; + +function isRef(r) { + return r ? r["__v_isRef"] === true : false; +} +function ref(value) { + return createRef(value, false); +} +function shallowRef(value) { + return createRef(value, true); +} +function createRef(rawValue, shallow) { + if (isRef(rawValue)) { + return rawValue; + } + return new RefImpl(rawValue, shallow); +} +class RefImpl { + constructor(value, isShallow2) { + this.dep = new Dep(); + this["__v_isRef"] = true; + this["__v_isShallow"] = false; + this._rawValue = isShallow2 ? value : toRaw(value); + this._value = isShallow2 ? value : toReactive(value); + this["__v_isShallow"] = isShallow2; + } + get value() { + { + this.dep.track({ + target: this, + type: "get", + key: "value" + }); + } + return this._value; + } + set value(newValue) { + const oldValue = this._rawValue; + const useDirectValue = this["__v_isShallow"] || isShallow(newValue) || isReadonly(newValue); + newValue = useDirectValue ? newValue : toRaw(newValue); + if (hasChanged(newValue, oldValue)) { + this._rawValue = newValue; + this._value = useDirectValue ? newValue : toReactive(newValue); + { + this.dep.trigger({ + target: this, + type: "set", + key: "value", + newValue, + oldValue + }); + } + } + } +} +function triggerRef(ref2) { + if (ref2.dep) { + { + ref2.dep.trigger({ + target: ref2, + type: "set", + key: "value", + newValue: ref2._value + }); + } + } +} +function unref(ref2) { + return isRef(ref2) ? ref2.value : ref2; +} +function toValue(source) { + return isFunction(source) ? source() : unref(source); +} +const shallowUnwrapHandlers = { + get: (target, key, receiver) => key === "__v_raw" ? target : unref(Reflect.get(target, key, receiver)), + set: (target, key, value, receiver) => { + const oldValue = target[key]; + if (isRef(oldValue) && !isRef(value)) { + oldValue.value = value; + return true; + } else { + return Reflect.set(target, key, value, receiver); + } + } +}; +function proxyRefs(objectWithRefs) { + return isReactive(objectWithRefs) ? objectWithRefs : new Proxy(objectWithRefs, shallowUnwrapHandlers); +} +class CustomRefImpl { + constructor(factory) { + this["__v_isRef"] = true; + this._value = void 0; + const dep = this.dep = new Dep(); + const { get, set } = factory(dep.track.bind(dep), dep.trigger.bind(dep)); + this._get = get; + this._set = set; + } + get value() { + return this._value = this._get(); + } + set value(newVal) { + this._set(newVal); + } +} +function customRef(factory) { + return new CustomRefImpl(factory); +} +function toRefs(object) { + if (!isProxy(object)) { + warn$2(`toRefs() expects a reactive object but received a plain one.`); + } + const ret = isArray(object) ? new Array(object.length) : {}; + for (const key in object) { + ret[key] = propertyToRef(object, key); + } + return ret; +} +class ObjectRefImpl { + constructor(_object, _key, _defaultValue) { + this._object = _object; + this._key = _key; + this._defaultValue = _defaultValue; + this["__v_isRef"] = true; + this._value = void 0; + } + get value() { + const val = this._object[this._key]; + return this._value = val === void 0 ? this._defaultValue : val; + } + set value(newVal) { + this._object[this._key] = newVal; + } + get dep() { + return getDepFromReactive(toRaw(this._object), this._key); + } +} +class GetterRefImpl { + constructor(_getter) { + this._getter = _getter; + this["__v_isRef"] = true; + this["__v_isReadonly"] = true; + this._value = void 0; + } + get value() { + return this._value = this._getter(); + } +} +function toRef(source, key, defaultValue) { + if (isRef(source)) { + return source; + } else if (isFunction(source)) { + return new GetterRefImpl(source); + } else if (isObject(source) && arguments.length > 1) { + return propertyToRef(source, key, defaultValue); + } else { + return ref(source); + } +} +function propertyToRef(source, key, defaultValue) { + const val = source[key]; + return isRef(val) ? val : new ObjectRefImpl(source, key, defaultValue); +} + +class ComputedRefImpl { + constructor(fn, setter, isSSR) { + this.fn = fn; + this.setter = setter; + /** + * @internal + */ + this._value = void 0; + /** + * @internal + */ + this.dep = new Dep(this); + /** + * @internal + */ + this.__v_isRef = true; + // TODO isolatedDeclarations "__v_isReadonly" + // A computed is also a subscriber that tracks other deps + /** + * @internal + */ + this.deps = void 0; + /** + * @internal + */ + this.depsTail = void 0; + /** + * @internal + */ + this.flags = 16; + /** + * @internal + */ + this.globalVersion = globalVersion - 1; + /** + * @internal + */ + this.next = void 0; + // for backwards compat + this.effect = this; + this["__v_isReadonly"] = !setter; + this.isSSR = isSSR; + } + /** + * @internal + */ + notify() { + this.flags |= 16; + if (!(this.flags & 8) && // avoid infinite self recursion + activeSub !== this) { + batch(this, true); + return true; + } + } + get value() { + const link = this.dep.track({ + target: this, + type: "get", + key: "value" + }) ; + refreshComputed(this); + if (link) { + link.version = this.dep.version; + } + return this._value; + } + set value(newValue) { + if (this.setter) { + this.setter(newValue); + } else { + warn$2("Write operation failed: computed value is readonly"); + } + } +} +function computed$1(getterOrOptions, debugOptions, isSSR = false) { + let getter; + let setter; + if (isFunction(getterOrOptions)) { + getter = getterOrOptions; + } else { + getter = getterOrOptions.get; + setter = getterOrOptions.set; + } + const cRef = new ComputedRefImpl(getter, setter, isSSR); + if (debugOptions && !isSSR) { + cRef.onTrack = debugOptions.onTrack; + cRef.onTrigger = debugOptions.onTrigger; + } + return cRef; +} + +const TrackOpTypes = { + "GET": "get", + "HAS": "has", + "ITERATE": "iterate" +}; +const TriggerOpTypes = { + "SET": "set", + "ADD": "add", + "DELETE": "delete", + "CLEAR": "clear" +}; + +const INITIAL_WATCHER_VALUE = {}; +const cleanupMap = /* @__PURE__ */ new WeakMap(); +let activeWatcher = void 0; +function getCurrentWatcher() { + return activeWatcher; +} +function onWatcherCleanup(cleanupFn, failSilently = false, owner = activeWatcher) { + if (owner) { + let cleanups = cleanupMap.get(owner); + if (!cleanups) cleanupMap.set(owner, cleanups = []); + cleanups.push(cleanupFn); + } else if (!failSilently) { + warn$2( + `onWatcherCleanup() was called when there was no active watcher to associate with.` + ); + } +} +function watch$1(source, cb, options = EMPTY_OBJ) { + const { immediate, deep, once, scheduler, augmentJob, call } = options; + const warnInvalidSource = (s) => { + (options.onWarn || warn$2)( + `Invalid watch source: `, + s, + `A watch source can only be a getter/effect function, a ref, a reactive object, or an array of these types.` + ); + }; + const reactiveGetter = (source2) => { + if (deep) return source2; + if (isShallow(source2) || deep === false || deep === 0) + return traverse(source2, 1); + return traverse(source2); + }; + let effect; + let getter; + let cleanup; + let boundCleanup; + let forceTrigger = false; + let isMultiSource = false; + if (isRef(source)) { + getter = () => source.value; + forceTrigger = isShallow(source); + } else if (isReactive(source)) { + getter = () => reactiveGetter(source); + forceTrigger = true; + } else if (isArray(source)) { + isMultiSource = true; + forceTrigger = source.some((s) => isReactive(s) || isShallow(s)); + getter = () => source.map((s) => { + if (isRef(s)) { + return s.value; + } else if (isReactive(s)) { + return reactiveGetter(s); + } else if (isFunction(s)) { + return call ? call(s, 2) : s(); + } else { + warnInvalidSource(s); + } + }); + } else if (isFunction(source)) { + if (cb) { + getter = call ? () => call(source, 2) : source; + } else { + getter = () => { + if (cleanup) { + pauseTracking(); + try { + cleanup(); + } finally { + resetTracking(); + } + } + const currentEffect = activeWatcher; + activeWatcher = effect; + try { + return call ? call(source, 3, [boundCleanup]) : source(boundCleanup); + } finally { + activeWatcher = currentEffect; + } + }; + } + } else { + getter = NOOP; + warnInvalidSource(source); + } + if (cb && deep) { + const baseGetter = getter; + const depth = deep === true ? Infinity : deep; + getter = () => traverse(baseGetter(), depth); + } + const scope = getCurrentScope(); + const watchHandle = () => { + effect.stop(); + if (scope) { + remove(scope.effects, effect); + } + }; + if (once && cb) { + const _cb = cb; + cb = (...args) => { + _cb(...args); + watchHandle(); + }; + } + let oldValue = isMultiSource ? new Array(source.length).fill(INITIAL_WATCHER_VALUE) : INITIAL_WATCHER_VALUE; + const job = (immediateFirstRun) => { + if (!(effect.flags & 1) || !effect.dirty && !immediateFirstRun) { + return; + } + if (cb) { + const newValue = effect.run(); + if (deep || forceTrigger || (isMultiSource ? newValue.some((v, i) => hasChanged(v, oldValue[i])) : hasChanged(newValue, oldValue))) { + if (cleanup) { + cleanup(); + } + const currentWatcher = activeWatcher; + activeWatcher = effect; + try { + const args = [ + newValue, + // pass undefined as the old value when it's changed for the first time + oldValue === INITIAL_WATCHER_VALUE ? void 0 : isMultiSource && oldValue[0] === INITIAL_WATCHER_VALUE ? [] : oldValue, + boundCleanup + ]; + call ? call(cb, 3, args) : ( + // @ts-expect-error + cb(...args) + ); + oldValue = newValue; + } finally { + activeWatcher = currentWatcher; + } + } + } else { + effect.run(); + } + }; + if (augmentJob) { + augmentJob(job); + } + effect = new ReactiveEffect(getter); + effect.scheduler = scheduler ? () => scheduler(job, false) : job; + boundCleanup = (fn) => onWatcherCleanup(fn, false, effect); + cleanup = effect.onStop = () => { + const cleanups = cleanupMap.get(effect); + if (cleanups) { + if (call) { + call(cleanups, 4); + } else { + for (const cleanup2 of cleanups) cleanup2(); + } + cleanupMap.delete(effect); + } + }; + { + effect.onTrack = options.onTrack; + effect.onTrigger = options.onTrigger; + } + if (cb) { + if (immediate) { + job(true); + } else { + oldValue = effect.run(); + } + } else if (scheduler) { + scheduler(job.bind(null, true), true); + } else { + effect.run(); + } + watchHandle.pause = effect.pause.bind(effect); + watchHandle.resume = effect.resume.bind(effect); + watchHandle.stop = watchHandle; + return watchHandle; +} +function traverse(value, depth = Infinity, seen) { + if (depth <= 0 || !isObject(value) || value["__v_skip"]) { + return value; + } + seen = seen || /* @__PURE__ */ new Set(); + if (seen.has(value)) { + return value; + } + seen.add(value); + depth--; + if (isRef(value)) { + traverse(value.value, depth, seen); + } else if (isArray(value)) { + for (let i = 0; i < value.length; i++) { + traverse(value[i], depth, seen); + } + } else if (isSet(value) || isMap(value)) { + value.forEach((v) => { + traverse(v, depth, seen); + }); + } else if (isPlainObject(value)) { + for (const key in value) { + traverse(value[key], depth, seen); + } + for (const key of Object.getOwnPropertySymbols(value)) { + if (Object.prototype.propertyIsEnumerable.call(value, key)) { + traverse(value[key], depth, seen); + } + } + } + return value; +} + +const stack$1 = []; +function pushWarningContext(vnode) { + stack$1.push(vnode); +} +function popWarningContext() { + stack$1.pop(); +} +let isWarning = false; +function warn$1(msg, ...args) { + if (isWarning) return; + isWarning = true; + pauseTracking(); + const instance = stack$1.length ? stack$1[stack$1.length - 1].component : null; + const appWarnHandler = instance && instance.appContext.config.warnHandler; + const trace = getComponentTrace(); + if (appWarnHandler) { + callWithErrorHandling( + appWarnHandler, + instance, + 11, + [ + // eslint-disable-next-line no-restricted-syntax + msg + args.map((a) => { + var _a, _b; + return (_b = (_a = a.toString) == null ? void 0 : _a.call(a)) != null ? _b : JSON.stringify(a); + }).join(""), + instance && instance.proxy, + trace.map( + ({ vnode }) => `at <${formatComponentName(instance, vnode.type)}>` + ).join("\n"), + trace + ] + ); + } else { + const warnArgs = [`[Vue warn]: ${msg}`, ...args]; + if (trace.length && // avoid spamming console during tests + true) { + warnArgs.push(` +`, ...formatTrace(trace)); + } + console.warn(...warnArgs); + } + resetTracking(); + isWarning = false; +} +function getComponentTrace() { + let currentVNode = stack$1[stack$1.length - 1]; + if (!currentVNode) { + return []; + } + const normalizedStack = []; + while (currentVNode) { + const last = normalizedStack[0]; + if (last && last.vnode === currentVNode) { + last.recurseCount++; + } else { + normalizedStack.push({ + vnode: currentVNode, + recurseCount: 0 + }); + } + const parentInstance = currentVNode.component && currentVNode.component.parent; + currentVNode = parentInstance && parentInstance.vnode; + } + return normalizedStack; +} +function formatTrace(trace) { + const logs = []; + trace.forEach((entry, i) => { + logs.push(...i === 0 ? [] : [` +`], ...formatTraceEntry(entry)); + }); + return logs; +} +function formatTraceEntry({ vnode, recurseCount }) { + const postfix = recurseCount > 0 ? `... (${recurseCount} recursive calls)` : ``; + const isRoot = vnode.component ? vnode.component.parent == null : false; + const open = ` at <${formatComponentName( + vnode.component, + vnode.type, + isRoot + )}`; + const close = `>` + postfix; + return vnode.props ? [open, ...formatProps(vnode.props), close] : [open + close]; +} +function formatProps(props) { + const res = []; + const keys = Object.keys(props); + keys.slice(0, 3).forEach((key) => { + res.push(...formatProp(key, props[key])); + }); + if (keys.length > 3) { + res.push(` ...`); + } + return res; +} +function formatProp(key, value, raw) { + if (isString(value)) { + value = JSON.stringify(value); + return raw ? value : [`${key}=${value}`]; + } else if (typeof value === "number" || typeof value === "boolean" || value == null) { + return raw ? value : [`${key}=${value}`]; + } else if (isRef(value)) { + value = formatProp(key, toRaw(value.value), true); + return raw ? value : [`${key}=Ref<`, value, `>`]; + } else if (isFunction(value)) { + return [`${key}=fn${value.name ? `<${value.name}>` : ``}`]; + } else { + value = toRaw(value); + return raw ? value : [`${key}=`, value]; + } +} +function assertNumber(val, type) { + if (val === void 0) { + return; + } else if (typeof val !== "number") { + warn$1(`${type} is not a valid number - got ${JSON.stringify(val)}.`); + } else if (isNaN(val)) { + warn$1(`${type} is NaN - the duration expression might be incorrect.`); + } +} + +const ErrorCodes = { + "SETUP_FUNCTION": 0, + "0": "SETUP_FUNCTION", + "RENDER_FUNCTION": 1, + "1": "RENDER_FUNCTION", + "NATIVE_EVENT_HANDLER": 5, + "5": "NATIVE_EVENT_HANDLER", + "COMPONENT_EVENT_HANDLER": 6, + "6": "COMPONENT_EVENT_HANDLER", + "VNODE_HOOK": 7, + "7": "VNODE_HOOK", + "DIRECTIVE_HOOK": 8, + "8": "DIRECTIVE_HOOK", + "TRANSITION_HOOK": 9, + "9": "TRANSITION_HOOK", + "APP_ERROR_HANDLER": 10, + "10": "APP_ERROR_HANDLER", + "APP_WARN_HANDLER": 11, + "11": "APP_WARN_HANDLER", + "FUNCTION_REF": 12, + "12": "FUNCTION_REF", + "ASYNC_COMPONENT_LOADER": 13, + "13": "ASYNC_COMPONENT_LOADER", + "SCHEDULER": 14, + "14": "SCHEDULER", + "COMPONENT_UPDATE": 15, + "15": "COMPONENT_UPDATE", + "APP_UNMOUNT_CLEANUP": 16, + "16": "APP_UNMOUNT_CLEANUP" +}; +const ErrorTypeStrings$1 = { + ["sp"]: "serverPrefetch hook", + ["bc"]: "beforeCreate hook", + ["c"]: "created hook", + ["bm"]: "beforeMount hook", + ["m"]: "mounted hook", + ["bu"]: "beforeUpdate hook", + ["u"]: "updated", + ["bum"]: "beforeUnmount hook", + ["um"]: "unmounted hook", + ["a"]: "activated hook", + ["da"]: "deactivated hook", + ["ec"]: "errorCaptured hook", + ["rtc"]: "renderTracked hook", + ["rtg"]: "renderTriggered hook", + [0]: "setup function", + [1]: "render function", + [2]: "watcher getter", + [3]: "watcher callback", + [4]: "watcher cleanup function", + [5]: "native event handler", + [6]: "component event handler", + [7]: "vnode hook", + [8]: "directive hook", + [9]: "transition hook", + [10]: "app errorHandler", + [11]: "app warnHandler", + [12]: "ref function", + [13]: "async component loader", + [14]: "scheduler flush", + [15]: "component update", + [16]: "app unmount cleanup function" +}; +function callWithErrorHandling(fn, instance, type, args) { + try { + return args ? fn(...args) : fn(); + } catch (err) { + handleError(err, instance, type); + } +} +function callWithAsyncErrorHandling(fn, instance, type, args) { + if (isFunction(fn)) { + const res = callWithErrorHandling(fn, instance, type, args); + if (res && isPromise(res)) { + res.catch((err) => { + handleError(err, instance, type); + }); + } + return res; + } + if (isArray(fn)) { + const values = []; + for (let i = 0; i < fn.length; i++) { + values.push(callWithAsyncErrorHandling(fn[i], instance, type, args)); + } + return values; + } else { + warn$1( + `Invalid value type passed to callWithAsyncErrorHandling(): ${typeof fn}` + ); + } +} +function handleError(err, instance, type, throwInDev = true) { + const contextVNode = instance ? instance.vnode : null; + const { errorHandler, throwUnhandledErrorInProduction } = instance && instance.appContext.config || EMPTY_OBJ; + if (instance) { + let cur = instance.parent; + const exposedInstance = instance.proxy; + const errorInfo = ErrorTypeStrings$1[type] ; + while (cur) { + const errorCapturedHooks = cur.ec; + if (errorCapturedHooks) { + for (let i = 0; i < errorCapturedHooks.length; i++) { + if (errorCapturedHooks[i](err, exposedInstance, errorInfo) === false) { + return; + } + } + } + cur = cur.parent; + } + if (errorHandler) { + pauseTracking(); + callWithErrorHandling(errorHandler, null, 10, [ + err, + exposedInstance, + errorInfo + ]); + resetTracking(); + return; + } + } + logError(err, type, contextVNode, throwInDev, throwUnhandledErrorInProduction); +} +function logError(err, type, contextVNode, throwInDev = true, throwInProd = false) { + { + const info = ErrorTypeStrings$1[type]; + if (contextVNode) { + pushWarningContext(contextVNode); + } + warn$1(`Unhandled error${info ? ` during execution of ${info}` : ``}`); + if (contextVNode) { + popWarningContext(); + } + if (throwInDev) { + throw err; + } else { + console.error(err); + } + } +} + +const queue = []; +let flushIndex = -1; +const pendingPostFlushCbs = []; +let activePostFlushCbs = null; +let postFlushIndex = 0; +const resolvedPromise = /* @__PURE__ */ Promise.resolve(); +let currentFlushPromise = null; +const RECURSION_LIMIT = 100; +function nextTick(fn) { + const p = currentFlushPromise || resolvedPromise; + return fn ? p.then(this ? fn.bind(this) : fn) : p; +} +function findInsertionIndex(id) { + let start = flushIndex + 1; + let end = queue.length; + while (start < end) { + const middle = start + end >>> 1; + const middleJob = queue[middle]; + const middleJobId = getId(middleJob); + if (middleJobId < id || middleJobId === id && middleJob.flags & 2) { + start = middle + 1; + } else { + end = middle; + } + } + return start; +} +function queueJob(job) { + if (!(job.flags & 1)) { + const jobId = getId(job); + const lastJob = queue[queue.length - 1]; + if (!lastJob || // fast path when the job id is larger than the tail + !(job.flags & 2) && jobId >= getId(lastJob)) { + queue.push(job); + } else { + queue.splice(findInsertionIndex(jobId), 0, job); + } + job.flags |= 1; + queueFlush(); + } +} +function queueFlush() { + if (!currentFlushPromise) { + currentFlushPromise = resolvedPromise.then(flushJobs); + } +} +function queuePostFlushCb(cb) { + if (!isArray(cb)) { + if (activePostFlushCbs && cb.id === -1) { + activePostFlushCbs.splice(postFlushIndex + 1, 0, cb); + } else if (!(cb.flags & 1)) { + pendingPostFlushCbs.push(cb); + cb.flags |= 1; + } + } else { + pendingPostFlushCbs.push(...cb); + } + queueFlush(); +} +function flushPreFlushCbs(instance, seen, i = flushIndex + 1) { + { + seen = seen || /* @__PURE__ */ new Map(); + } + for (; i < queue.length; i++) { + const cb = queue[i]; + if (cb && cb.flags & 2) { + if (instance && cb.id !== instance.uid) { + continue; + } + if (checkRecursiveUpdates(seen, cb)) { + continue; + } + queue.splice(i, 1); + i--; + if (cb.flags & 4) { + cb.flags &= ~1; + } + cb(); + if (!(cb.flags & 4)) { + cb.flags &= ~1; + } + } + } +} +function flushPostFlushCbs(seen) { + if (pendingPostFlushCbs.length) { + const deduped = [...new Set(pendingPostFlushCbs)].sort( + (a, b) => getId(a) - getId(b) + ); + pendingPostFlushCbs.length = 0; + if (activePostFlushCbs) { + activePostFlushCbs.push(...deduped); + return; + } + activePostFlushCbs = deduped; + { + seen = seen || /* @__PURE__ */ new Map(); + } + for (postFlushIndex = 0; postFlushIndex < activePostFlushCbs.length; postFlushIndex++) { + const cb = activePostFlushCbs[postFlushIndex]; + if (checkRecursiveUpdates(seen, cb)) { + continue; + } + if (cb.flags & 4) { + cb.flags &= ~1; + } + if (!(cb.flags & 8)) cb(); + cb.flags &= ~1; + } + activePostFlushCbs = null; + postFlushIndex = 0; + } +} +const getId = (job) => job.id == null ? job.flags & 2 ? -1 : Infinity : job.id; +function flushJobs(seen) { + { + seen = seen || /* @__PURE__ */ new Map(); + } + const check = (job) => checkRecursiveUpdates(seen, job) ; + try { + for (flushIndex = 0; flushIndex < queue.length; flushIndex++) { + const job = queue[flushIndex]; + if (job && !(job.flags & 8)) { + if (check(job)) { + continue; + } + if (job.flags & 4) { + job.flags &= ~1; + } + callWithErrorHandling( + job, + job.i, + job.i ? 15 : 14 + ); + if (!(job.flags & 4)) { + job.flags &= ~1; + } + } + } + } finally { + for (; flushIndex < queue.length; flushIndex++) { + const job = queue[flushIndex]; + if (job) { + job.flags &= ~1; + } + } + flushIndex = -1; + queue.length = 0; + flushPostFlushCbs(seen); + currentFlushPromise = null; + if (queue.length || pendingPostFlushCbs.length) { + flushJobs(seen); + } + } +} +function checkRecursiveUpdates(seen, fn) { + const count = seen.get(fn) || 0; + if (count > RECURSION_LIMIT) { + const instance = fn.i; + const componentName = instance && getComponentName(instance.type); + handleError( + `Maximum recursive updates exceeded${componentName ? ` in component <${componentName}>` : ``}. This means you have a reactive effect that is mutating its own dependencies and thus recursively triggering itself. Possible sources include component template, render function, updated hook or watcher source function.`, + null, + 10 + ); + return true; + } + seen.set(fn, count + 1); + return false; +} + +let isHmrUpdating = false; +const hmrDirtyComponents = /* @__PURE__ */ new Map(); +{ + getGlobalThis().__VUE_HMR_RUNTIME__ = { + createRecord: tryWrap(createRecord), + rerender: tryWrap(rerender), + reload: tryWrap(reload) + }; +} +const map = /* @__PURE__ */ new Map(); +function registerHMR(instance) { + const id = instance.type.__hmrId; + let record = map.get(id); + if (!record) { + createRecord(id, instance.type); + record = map.get(id); + } + record.instances.add(instance); +} +function unregisterHMR(instance) { + map.get(instance.type.__hmrId).instances.delete(instance); +} +function createRecord(id, initialDef) { + if (map.has(id)) { + return false; + } + map.set(id, { + initialDef: normalizeClassComponent(initialDef), + instances: /* @__PURE__ */ new Set() + }); + return true; +} +function normalizeClassComponent(component) { + return isClassComponent(component) ? component.__vccOpts : component; +} +function rerender(id, newRender) { + const record = map.get(id); + if (!record) { + return; + } + record.initialDef.render = newRender; + [...record.instances].forEach((instance) => { + if (newRender) { + instance.render = newRender; + normalizeClassComponent(instance.type).render = newRender; + } + instance.renderCache = []; + isHmrUpdating = true; + instance.update(); + isHmrUpdating = false; + }); +} +function reload(id, newComp) { + const record = map.get(id); + if (!record) return; + newComp = normalizeClassComponent(newComp); + updateComponentDef(record.initialDef, newComp); + const instances = [...record.instances]; + for (let i = 0; i < instances.length; i++) { + const instance = instances[i]; + const oldComp = normalizeClassComponent(instance.type); + let dirtyInstances = hmrDirtyComponents.get(oldComp); + if (!dirtyInstances) { + if (oldComp !== record.initialDef) { + updateComponentDef(oldComp, newComp); + } + hmrDirtyComponents.set(oldComp, dirtyInstances = /* @__PURE__ */ new Set()); + } + dirtyInstances.add(instance); + instance.appContext.propsCache.delete(instance.type); + instance.appContext.emitsCache.delete(instance.type); + instance.appContext.optionsCache.delete(instance.type); + if (instance.ceReload) { + dirtyInstances.add(instance); + instance.ceReload(newComp.styles); + dirtyInstances.delete(instance); + } else if (instance.parent) { + queueJob(() => { + isHmrUpdating = true; + instance.parent.update(); + isHmrUpdating = false; + dirtyInstances.delete(instance); + }); + } else if (instance.appContext.reload) { + instance.appContext.reload(); + } else if (typeof window !== "undefined") { + window.location.reload(); + } else { + console.warn( + "[HMR] Root or manually mounted instance modified. Full reload required." + ); + } + if (instance.root.ce && instance !== instance.root) { + instance.root.ce._removeChildStyle(oldComp); + } + } + queuePostFlushCb(() => { + hmrDirtyComponents.clear(); + }); +} +function updateComponentDef(oldComp, newComp) { + extend(oldComp, newComp); + for (const key in oldComp) { + if (key !== "__file" && !(key in newComp)) { + delete oldComp[key]; + } + } +} +function tryWrap(fn) { + return (id, arg) => { + try { + return fn(id, arg); + } catch (e) { + console.error(e); + console.warn( + `[HMR] Something went wrong during Vue component hot-reload. Full reload required.` + ); + } + }; +} + +let devtools$1; +let buffer = []; +let devtoolsNotInstalled = false; +function emit$1(event, ...args) { + if (devtools$1) { + devtools$1.emit(event, ...args); + } else if (!devtoolsNotInstalled) { + buffer.push({ event, args }); + } +} +function setDevtoolsHook$1(hook, target) { + var _a, _b; + devtools$1 = hook; + if (devtools$1) { + devtools$1.enabled = true; + buffer.forEach(({ event, args }) => devtools$1.emit(event, ...args)); + buffer = []; + } else if ( + // handle late devtools injection - only do this if we are in an actual + // browser environment to avoid the timer handle stalling test runner exit + // (#4815) + typeof window !== "undefined" && // some envs mock window but not fully + window.HTMLElement && // also exclude jsdom + // eslint-disable-next-line no-restricted-syntax + !((_b = (_a = window.navigator) == null ? void 0 : _a.userAgent) == null ? void 0 : _b.includes("jsdom")) + ) { + const replay = target.__VUE_DEVTOOLS_HOOK_REPLAY__ = target.__VUE_DEVTOOLS_HOOK_REPLAY__ || []; + replay.push((newHook) => { + setDevtoolsHook$1(newHook, target); + }); + setTimeout(() => { + if (!devtools$1) { + target.__VUE_DEVTOOLS_HOOK_REPLAY__ = null; + devtoolsNotInstalled = true; + buffer = []; + } + }, 3e3); + } else { + devtoolsNotInstalled = true; + buffer = []; + } +} +function devtoolsInitApp(app, version) { + emit$1("app:init" /* APP_INIT */, app, version, { + Fragment, + Text, + Comment, + Static + }); +} +function devtoolsUnmountApp(app) { + emit$1("app:unmount" /* APP_UNMOUNT */, app); +} +const devtoolsComponentAdded = /* @__PURE__ */ createDevtoolsComponentHook("component:added" /* COMPONENT_ADDED */); +const devtoolsComponentUpdated = /* @__PURE__ */ createDevtoolsComponentHook("component:updated" /* COMPONENT_UPDATED */); +const _devtoolsComponentRemoved = /* @__PURE__ */ createDevtoolsComponentHook( + "component:removed" /* COMPONENT_REMOVED */ +); +const devtoolsComponentRemoved = (component) => { + if (devtools$1 && typeof devtools$1.cleanupBuffer === "function" && // remove the component if it wasn't buffered + !devtools$1.cleanupBuffer(component)) { + _devtoolsComponentRemoved(component); + } +}; +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function createDevtoolsComponentHook(hook) { + return (component) => { + emit$1( + hook, + component.appContext.app, + component.uid, + component.parent ? component.parent.uid : void 0, + component + ); + }; +} +const devtoolsPerfStart = /* @__PURE__ */ createDevtoolsPerformanceHook("perf:start" /* PERFORMANCE_START */); +const devtoolsPerfEnd = /* @__PURE__ */ createDevtoolsPerformanceHook("perf:end" /* PERFORMANCE_END */); +function createDevtoolsPerformanceHook(hook) { + return (component, type, time) => { + emit$1(hook, component.appContext.app, component.uid, component, type, time); + }; +} +function devtoolsComponentEmit(component, event, params) { + emit$1( + "component:emit" /* COMPONENT_EMIT */, + component.appContext.app, + component, + event, + params + ); +} + +let currentRenderingInstance = null; +let currentScopeId = null; +function setCurrentRenderingInstance(instance) { + const prev = currentRenderingInstance; + currentRenderingInstance = instance; + currentScopeId = instance && instance.type.__scopeId || null; + return prev; +} +function pushScopeId(id) { + currentScopeId = id; +} +function popScopeId() { + currentScopeId = null; +} +const withScopeId = (_id) => withCtx; +function withCtx(fn, ctx = currentRenderingInstance, isNonScopedSlot) { + if (!ctx) return fn; + if (fn._n) { + return fn; + } + const renderFnWithContext = (...args) => { + if (renderFnWithContext._d) { + setBlockTracking(-1); + } + const prevInstance = setCurrentRenderingInstance(ctx); + let res; + try { + res = fn(...args); + } finally { + setCurrentRenderingInstance(prevInstance); + if (renderFnWithContext._d) { + setBlockTracking(1); + } + } + { + devtoolsComponentUpdated(ctx); + } + return res; + }; + renderFnWithContext._n = true; + renderFnWithContext._c = true; + renderFnWithContext._d = true; + return renderFnWithContext; +} + +function validateDirectiveName(name) { + if (isBuiltInDirective(name)) { + warn$1("Do not use built-in directive ids as custom directive id: " + name); + } +} +function withDirectives(vnode, directives) { + if (currentRenderingInstance === null) { + warn$1(`withDirectives can only be used inside render functions.`); + return vnode; + } + const instance = getComponentPublicInstance(currentRenderingInstance); + const bindings = vnode.dirs || (vnode.dirs = []); + for (let i = 0; i < directives.length; i++) { + let [dir, value, arg, modifiers = EMPTY_OBJ] = directives[i]; + if (dir) { + if (isFunction(dir)) { + dir = { + mounted: dir, + updated: dir + }; + } + if (dir.deep) { + traverse(value); + } + bindings.push({ + dir, + instance, + value, + oldValue: void 0, + arg, + modifiers + }); + } + } + return vnode; +} +function invokeDirectiveHook(vnode, prevVNode, instance, name) { + const bindings = vnode.dirs; + const oldBindings = prevVNode && prevVNode.dirs; + for (let i = 0; i < bindings.length; i++) { + const binding = bindings[i]; + if (oldBindings) { + binding.oldValue = oldBindings[i].value; + } + let hook = binding.dir[name]; + if (hook) { + pauseTracking(); + callWithAsyncErrorHandling(hook, instance, 8, [ + vnode.el, + binding, + vnode, + prevVNode + ]); + resetTracking(); + } + } +} + +const TeleportEndKey = Symbol("_vte"); +const isTeleport = (type) => type.__isTeleport; +const isTeleportDisabled = (props) => props && (props.disabled || props.disabled === ""); +const isTeleportDeferred = (props) => props && (props.defer || props.defer === ""); +const isTargetSVG = (target) => typeof SVGElement !== "undefined" && target instanceof SVGElement; +const isTargetMathML = (target) => typeof MathMLElement === "function" && target instanceof MathMLElement; +const resolveTarget = (props, select) => { + const targetSelector = props && props.to; + if (isString(targetSelector)) { + if (!select) { + warn$1( + `Current renderer does not support string target for Teleports. (missing querySelector renderer option)` + ); + return null; + } else { + const target = select(targetSelector); + if (!target && !isTeleportDisabled(props)) { + warn$1( + `Failed to locate Teleport target with selector "${targetSelector}". Note the target element must exist before the component is mounted - i.e. the target cannot be rendered by the component itself, and ideally should be outside of the entire Vue component tree.` + ); + } + return target; + } + } else { + if (!targetSelector && !isTeleportDisabled(props)) { + warn$1(`Invalid Teleport target: ${targetSelector}`); + } + return targetSelector; + } +}; +const TeleportImpl = { + name: "Teleport", + __isTeleport: true, + process(n1, n2, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, internals) { + const { + mc: mountChildren, + pc: patchChildren, + pbc: patchBlockChildren, + o: { insert, querySelector, createText, createComment } + } = internals; + const disabled = isTeleportDisabled(n2.props); + let { shapeFlag, children, dynamicChildren } = n2; + if (isHmrUpdating) { + optimized = false; + dynamicChildren = null; + } + if (n1 == null) { + const placeholder = n2.el = createComment("teleport start") ; + const mainAnchor = n2.anchor = createComment("teleport end") ; + insert(placeholder, container, anchor); + insert(mainAnchor, container, anchor); + const mount = (container2, anchor2) => { + if (shapeFlag & 16) { + if (parentComponent && parentComponent.isCE) { + parentComponent.ce._teleportTarget = container2; + } + mountChildren( + children, + container2, + anchor2, + parentComponent, + parentSuspense, + namespace, + slotScopeIds, + optimized + ); + } + }; + const mountToTarget = () => { + const target = n2.target = resolveTarget(n2.props, querySelector); + const targetAnchor = prepareAnchor(target, n2, createText, insert); + if (target) { + if (namespace !== "svg" && isTargetSVG(target)) { + namespace = "svg"; + } else if (namespace !== "mathml" && isTargetMathML(target)) { + namespace = "mathml"; + } + if (!disabled) { + mount(target, targetAnchor); + updateCssVars(n2, false); + } + } else if (!disabled) { + warn$1( + "Invalid Teleport target on mount:", + target, + `(${typeof target})` + ); + } + }; + if (disabled) { + mount(container, mainAnchor); + updateCssVars(n2, true); + } + if (isTeleportDeferred(n2.props)) { + queuePostRenderEffect(mountToTarget, parentSuspense); + } else { + mountToTarget(); + } + } else { + n2.el = n1.el; + n2.targetStart = n1.targetStart; + const mainAnchor = n2.anchor = n1.anchor; + const target = n2.target = n1.target; + const targetAnchor = n2.targetAnchor = n1.targetAnchor; + const wasDisabled = isTeleportDisabled(n1.props); + const currentContainer = wasDisabled ? container : target; + const currentAnchor = wasDisabled ? mainAnchor : targetAnchor; + if (namespace === "svg" || isTargetSVG(target)) { + namespace = "svg"; + } else if (namespace === "mathml" || isTargetMathML(target)) { + namespace = "mathml"; + } + if (dynamicChildren) { + patchBlockChildren( + n1.dynamicChildren, + dynamicChildren, + currentContainer, + parentComponent, + parentSuspense, + namespace, + slotScopeIds + ); + traverseStaticChildren(n1, n2, true); + } else if (!optimized) { + patchChildren( + n1, + n2, + currentContainer, + currentAnchor, + parentComponent, + parentSuspense, + namespace, + slotScopeIds, + false + ); + } + if (disabled) { + if (!wasDisabled) { + moveTeleport( + n2, + container, + mainAnchor, + internals, + 1 + ); + } else { + if (n2.props && n1.props && n2.props.to !== n1.props.to) { + n2.props.to = n1.props.to; + } + } + } else { + if ((n2.props && n2.props.to) !== (n1.props && n1.props.to)) { + const nextTarget = n2.target = resolveTarget( + n2.props, + querySelector + ); + if (nextTarget) { + moveTeleport( + n2, + nextTarget, + null, + internals, + 0 + ); + } else { + warn$1( + "Invalid Teleport target on update:", + target, + `(${typeof target})` + ); + } + } else if (wasDisabled) { + moveTeleport( + n2, + target, + targetAnchor, + internals, + 1 + ); + } + } + updateCssVars(n2, disabled); + } + }, + remove(vnode, parentComponent, parentSuspense, { um: unmount, o: { remove: hostRemove } }, doRemove) { + const { + shapeFlag, + children, + anchor, + targetStart, + targetAnchor, + target, + props + } = vnode; + if (target) { + hostRemove(targetStart); + hostRemove(targetAnchor); + } + doRemove && hostRemove(anchor); + if (shapeFlag & 16) { + const shouldRemove = doRemove || !isTeleportDisabled(props); + for (let i = 0; i < children.length; i++) { + const child = children[i]; + unmount( + child, + parentComponent, + parentSuspense, + shouldRemove, + !!child.dynamicChildren + ); + } + } + }, + move: moveTeleport, + hydrate: hydrateTeleport +}; +function moveTeleport(vnode, container, parentAnchor, { o: { insert }, m: move }, moveType = 2) { + if (moveType === 0) { + insert(vnode.targetAnchor, container, parentAnchor); + } + const { el, anchor, shapeFlag, children, props } = vnode; + const isReorder = moveType === 2; + if (isReorder) { + insert(el, container, parentAnchor); + } + if (!isReorder || isTeleportDisabled(props)) { + if (shapeFlag & 16) { + for (let i = 0; i < children.length; i++) { + move( + children[i], + container, + parentAnchor, + 2 + ); + } + } + } + if (isReorder) { + insert(anchor, container, parentAnchor); + } +} +function hydrateTeleport(node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized, { + o: { nextSibling, parentNode, querySelector, insert, createText } +}, hydrateChildren) { + const target = vnode.target = resolveTarget( + vnode.props, + querySelector + ); + if (target) { + const disabled = isTeleportDisabled(vnode.props); + const targetNode = target._lpa || target.firstChild; + if (vnode.shapeFlag & 16) { + if (disabled) { + vnode.anchor = hydrateChildren( + nextSibling(node), + vnode, + parentNode(node), + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + vnode.targetStart = targetNode; + vnode.targetAnchor = targetNode && nextSibling(targetNode); + } else { + vnode.anchor = nextSibling(node); + let targetAnchor = targetNode; + while (targetAnchor) { + if (targetAnchor && targetAnchor.nodeType === 8) { + if (targetAnchor.data === "teleport start anchor") { + vnode.targetStart = targetAnchor; + } else if (targetAnchor.data === "teleport anchor") { + vnode.targetAnchor = targetAnchor; + target._lpa = vnode.targetAnchor && nextSibling(vnode.targetAnchor); + break; + } + } + targetAnchor = nextSibling(targetAnchor); + } + if (!vnode.targetAnchor) { + prepareAnchor(target, vnode, createText, insert); + } + hydrateChildren( + targetNode && nextSibling(targetNode), + vnode, + target, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + } + } + updateCssVars(vnode, disabled); + } + return vnode.anchor && nextSibling(vnode.anchor); +} +const Teleport = TeleportImpl; +function updateCssVars(vnode, isDisabled) { + const ctx = vnode.ctx; + if (ctx && ctx.ut) { + let node, anchor; + if (isDisabled) { + node = vnode.el; + anchor = vnode.anchor; + } else { + node = vnode.targetStart; + anchor = vnode.targetAnchor; + } + while (node && node !== anchor) { + if (node.nodeType === 1) node.setAttribute("data-v-owner", ctx.uid); + node = node.nextSibling; + } + ctx.ut(); + } +} +function prepareAnchor(target, vnode, createText, insert) { + const targetStart = vnode.targetStart = createText(""); + const targetAnchor = vnode.targetAnchor = createText(""); + targetStart[TeleportEndKey] = targetAnchor; + if (target) { + insert(targetStart, target); + insert(targetAnchor, target); + } + return targetAnchor; +} + +const leaveCbKey = Symbol("_leaveCb"); +const enterCbKey$1 = Symbol("_enterCb"); +function useTransitionState() { + const state = { + isMounted: false, + isLeaving: false, + isUnmounting: false, + leavingVNodes: /* @__PURE__ */ new Map() + }; + onMounted(() => { + state.isMounted = true; + }); + onBeforeUnmount(() => { + state.isUnmounting = true; + }); + return state; +} +const TransitionHookValidator = [Function, Array]; +const BaseTransitionPropsValidators = { + mode: String, + appear: Boolean, + persisted: Boolean, + // enter + onBeforeEnter: TransitionHookValidator, + onEnter: TransitionHookValidator, + onAfterEnter: TransitionHookValidator, + onEnterCancelled: TransitionHookValidator, + // leave + onBeforeLeave: TransitionHookValidator, + onLeave: TransitionHookValidator, + onAfterLeave: TransitionHookValidator, + onLeaveCancelled: TransitionHookValidator, + // appear + onBeforeAppear: TransitionHookValidator, + onAppear: TransitionHookValidator, + onAfterAppear: TransitionHookValidator, + onAppearCancelled: TransitionHookValidator +}; +const recursiveGetSubtree = (instance) => { + const subTree = instance.subTree; + return subTree.component ? recursiveGetSubtree(subTree.component) : subTree; +}; +const BaseTransitionImpl = { + name: `BaseTransition`, + props: BaseTransitionPropsValidators, + setup(props, { slots }) { + const instance = getCurrentInstance(); + const state = useTransitionState(); + return () => { + const children = slots.default && getTransitionRawChildren(slots.default(), true); + if (!children || !children.length) { + return; + } + const child = findNonCommentChild(children); + const rawProps = toRaw(props); + const { mode } = rawProps; + if (mode && mode !== "in-out" && mode !== "out-in" && mode !== "default") { + warn$1(`invalid mode: ${mode}`); + } + if (state.isLeaving) { + return emptyPlaceholder(child); + } + const innerChild = getInnerChild$1(child); + if (!innerChild) { + return emptyPlaceholder(child); + } + let enterHooks = resolveTransitionHooks( + innerChild, + rawProps, + state, + instance, + // #11061, ensure enterHooks is fresh after clone + (hooks) => enterHooks = hooks + ); + if (innerChild.type !== Comment) { + setTransitionHooks(innerChild, enterHooks); + } + const oldChild = instance.subTree; + const oldInnerChild = oldChild && getInnerChild$1(oldChild); + if (oldInnerChild && oldInnerChild.type !== Comment && !isSameVNodeType(innerChild, oldInnerChild) && recursiveGetSubtree(instance).type !== Comment) { + const leavingHooks = resolveTransitionHooks( + oldInnerChild, + rawProps, + state, + instance + ); + setTransitionHooks(oldInnerChild, leavingHooks); + if (mode === "out-in" && innerChild.type !== Comment) { + state.isLeaving = true; + leavingHooks.afterLeave = () => { + state.isLeaving = false; + if (!(instance.job.flags & 8)) { + instance.update(); + } + delete leavingHooks.afterLeave; + }; + return emptyPlaceholder(child); + } else if (mode === "in-out" && innerChild.type !== Comment) { + leavingHooks.delayLeave = (el, earlyRemove, delayedLeave) => { + const leavingVNodesCache = getLeavingNodesForType( + state, + oldInnerChild + ); + leavingVNodesCache[String(oldInnerChild.key)] = oldInnerChild; + el[leaveCbKey] = () => { + earlyRemove(); + el[leaveCbKey] = void 0; + delete enterHooks.delayedLeave; + }; + enterHooks.delayedLeave = delayedLeave; + }; + } + } + return child; + }; + } +}; +function findNonCommentChild(children) { + let child = children[0]; + if (children.length > 1) { + let hasFound = false; + for (const c of children) { + if (c.type !== Comment) { + if (hasFound) { + warn$1( + " can only be used on a single element or component. Use for lists." + ); + break; + } + child = c; + hasFound = true; + } + } + } + return child; +} +const BaseTransition = BaseTransitionImpl; +function getLeavingNodesForType(state, vnode) { + const { leavingVNodes } = state; + let leavingVNodesCache = leavingVNodes.get(vnode.type); + if (!leavingVNodesCache) { + leavingVNodesCache = /* @__PURE__ */ Object.create(null); + leavingVNodes.set(vnode.type, leavingVNodesCache); + } + return leavingVNodesCache; +} +function resolveTransitionHooks(vnode, props, state, instance, postClone) { + const { + appear, + mode, + persisted = false, + onBeforeEnter, + onEnter, + onAfterEnter, + onEnterCancelled, + onBeforeLeave, + onLeave, + onAfterLeave, + onLeaveCancelled, + onBeforeAppear, + onAppear, + onAfterAppear, + onAppearCancelled + } = props; + const key = String(vnode.key); + const leavingVNodesCache = getLeavingNodesForType(state, vnode); + const callHook = (hook, args) => { + hook && callWithAsyncErrorHandling( + hook, + instance, + 9, + args + ); + }; + const callAsyncHook = (hook, args) => { + const done = args[1]; + callHook(hook, args); + if (isArray(hook)) { + if (hook.every((hook2) => hook2.length <= 1)) done(); + } else if (hook.length <= 1) { + done(); + } + }; + const hooks = { + mode, + persisted, + beforeEnter(el) { + let hook = onBeforeEnter; + if (!state.isMounted) { + if (appear) { + hook = onBeforeAppear || onBeforeEnter; + } else { + return; + } + } + if (el[leaveCbKey]) { + el[leaveCbKey]( + true + /* cancelled */ + ); + } + const leavingVNode = leavingVNodesCache[key]; + if (leavingVNode && isSameVNodeType(vnode, leavingVNode) && leavingVNode.el[leaveCbKey]) { + leavingVNode.el[leaveCbKey](); + } + callHook(hook, [el]); + }, + enter(el) { + let hook = onEnter; + let afterHook = onAfterEnter; + let cancelHook = onEnterCancelled; + if (!state.isMounted) { + if (appear) { + hook = onAppear || onEnter; + afterHook = onAfterAppear || onAfterEnter; + cancelHook = onAppearCancelled || onEnterCancelled; + } else { + return; + } + } + let called = false; + const done = el[enterCbKey$1] = (cancelled) => { + if (called) return; + called = true; + if (cancelled) { + callHook(cancelHook, [el]); + } else { + callHook(afterHook, [el]); + } + if (hooks.delayedLeave) { + hooks.delayedLeave(); + } + el[enterCbKey$1] = void 0; + }; + if (hook) { + callAsyncHook(hook, [el, done]); + } else { + done(); + } + }, + leave(el, remove) { + const key2 = String(vnode.key); + if (el[enterCbKey$1]) { + el[enterCbKey$1]( + true + /* cancelled */ + ); + } + if (state.isUnmounting) { + return remove(); + } + callHook(onBeforeLeave, [el]); + let called = false; + const done = el[leaveCbKey] = (cancelled) => { + if (called) return; + called = true; + remove(); + if (cancelled) { + callHook(onLeaveCancelled, [el]); + } else { + callHook(onAfterLeave, [el]); + } + el[leaveCbKey] = void 0; + if (leavingVNodesCache[key2] === vnode) { + delete leavingVNodesCache[key2]; + } + }; + leavingVNodesCache[key2] = vnode; + if (onLeave) { + callAsyncHook(onLeave, [el, done]); + } else { + done(); + } + }, + clone(vnode2) { + const hooks2 = resolveTransitionHooks( + vnode2, + props, + state, + instance, + postClone + ); + if (postClone) postClone(hooks2); + return hooks2; + } + }; + return hooks; +} +function emptyPlaceholder(vnode) { + if (isKeepAlive(vnode)) { + vnode = cloneVNode(vnode); + vnode.children = null; + return vnode; + } +} +function getInnerChild$1(vnode) { + if (!isKeepAlive(vnode)) { + if (isTeleport(vnode.type) && vnode.children) { + return findNonCommentChild(vnode.children); + } + return vnode; + } + if (vnode.component) { + return vnode.component.subTree; + } + const { shapeFlag, children } = vnode; + if (children) { + if (shapeFlag & 16) { + return children[0]; + } + if (shapeFlag & 32 && isFunction(children.default)) { + return children.default(); + } + } +} +function setTransitionHooks(vnode, hooks) { + if (vnode.shapeFlag & 6 && vnode.component) { + vnode.transition = hooks; + setTransitionHooks(vnode.component.subTree, hooks); + } else if (vnode.shapeFlag & 128) { + vnode.ssContent.transition = hooks.clone(vnode.ssContent); + vnode.ssFallback.transition = hooks.clone(vnode.ssFallback); + } else { + vnode.transition = hooks; + } +} +function getTransitionRawChildren(children, keepComment = false, parentKey) { + let ret = []; + let keyedFragmentCount = 0; + for (let i = 0; i < children.length; i++) { + let child = children[i]; + const key = parentKey == null ? child.key : String(parentKey) + String(child.key != null ? child.key : i); + if (child.type === Fragment) { + if (child.patchFlag & 128) keyedFragmentCount++; + ret = ret.concat( + getTransitionRawChildren(child.children, keepComment, key) + ); + } else if (keepComment || child.type !== Comment) { + ret.push(key != null ? cloneVNode(child, { key }) : child); + } + } + if (keyedFragmentCount > 1) { + for (let i = 0; i < ret.length; i++) { + ret[i].patchFlag = -2; + } + } + return ret; +} + +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function defineComponent(options, extraOptions) { + return isFunction(options) ? ( + // #8236: extend call and options.name access are considered side-effects + // by Rollup, so we have to wrap it in a pure-annotated IIFE. + /* @__PURE__ */ (() => extend({ name: options.name }, extraOptions, { setup: options }))() + ) : options; +} + +function useId() { + const i = getCurrentInstance(); + if (i) { + return (i.appContext.config.idPrefix || "v") + "-" + i.ids[0] + i.ids[1]++; + } else { + warn$1( + `useId() is called when there is no active component instance to be associated with.` + ); + } + return ""; +} +function markAsyncBoundary(instance) { + instance.ids = [instance.ids[0] + instance.ids[2]++ + "-", 0, 0]; +} + +const knownTemplateRefs = /* @__PURE__ */ new WeakSet(); +function useTemplateRef(key) { + const i = getCurrentInstance(); + const r = shallowRef(null); + if (i) { + const refs = i.refs === EMPTY_OBJ ? i.refs = {} : i.refs; + let desc; + if ((desc = Object.getOwnPropertyDescriptor(refs, key)) && !desc.configurable) { + warn$1(`useTemplateRef('${key}') already exists.`); + } else { + Object.defineProperty(refs, key, { + enumerable: true, + get: () => r.value, + set: (val) => r.value = val + }); + } + } else { + warn$1( + `useTemplateRef() is called when there is no active component instance to be associated with.` + ); + } + const ret = readonly(r) ; + { + knownTemplateRefs.add(ret); + } + return ret; +} + +function setRef(rawRef, oldRawRef, parentSuspense, vnode, isUnmount = false) { + if (isArray(rawRef)) { + rawRef.forEach( + (r, i) => setRef( + r, + oldRawRef && (isArray(oldRawRef) ? oldRawRef[i] : oldRawRef), + parentSuspense, + vnode, + isUnmount + ) + ); + return; + } + if (isAsyncWrapper(vnode) && !isUnmount) { + return; + } + const refValue = vnode.shapeFlag & 4 ? getComponentPublicInstance(vnode.component) : vnode.el; + const value = isUnmount ? null : refValue; + const { i: owner, r: ref } = rawRef; + if (!owner) { + warn$1( + `Missing ref owner context. ref cannot be used on hoisted vnodes. A vnode with ref must be created inside the render function.` + ); + return; + } + const oldRef = oldRawRef && oldRawRef.r; + const refs = owner.refs === EMPTY_OBJ ? owner.refs = {} : owner.refs; + const setupState = owner.setupState; + const rawSetupState = toRaw(setupState); + const canSetSetupRef = setupState === EMPTY_OBJ ? () => false : (key) => { + { + if (hasOwn(rawSetupState, key) && !isRef(rawSetupState[key])) { + warn$1( + `Template ref "${key}" used on a non-ref value. It will not work in the production build.` + ); + } + if (knownTemplateRefs.has(rawSetupState[key])) { + return false; + } + } + return hasOwn(rawSetupState, key); + }; + if (oldRef != null && oldRef !== ref) { + if (isString(oldRef)) { + refs[oldRef] = null; + if (canSetSetupRef(oldRef)) { + setupState[oldRef] = null; + } + } else if (isRef(oldRef)) { + oldRef.value = null; + } + } + if (isFunction(ref)) { + callWithErrorHandling(ref, owner, 12, [value, refs]); + } else { + const _isString = isString(ref); + const _isRef = isRef(ref); + if (_isString || _isRef) { + const doSet = () => { + if (rawRef.f) { + const existing = _isString ? canSetSetupRef(ref) ? setupState[ref] : refs[ref] : ref.value; + if (isUnmount) { + isArray(existing) && remove(existing, refValue); + } else { + if (!isArray(existing)) { + if (_isString) { + refs[ref] = [refValue]; + if (canSetSetupRef(ref)) { + setupState[ref] = refs[ref]; + } + } else { + ref.value = [refValue]; + if (rawRef.k) refs[rawRef.k] = ref.value; + } + } else if (!existing.includes(refValue)) { + existing.push(refValue); + } + } + } else if (_isString) { + refs[ref] = value; + if (canSetSetupRef(ref)) { + setupState[ref] = value; + } + } else if (_isRef) { + ref.value = value; + if (rawRef.k) refs[rawRef.k] = value; + } else { + warn$1("Invalid template ref type:", ref, `(${typeof ref})`); + } + }; + if (value) { + doSet.id = -1; + queuePostRenderEffect(doSet, parentSuspense); + } else { + doSet(); + } + } else { + warn$1("Invalid template ref type:", ref, `(${typeof ref})`); + } + } +} + +let hasLoggedMismatchError = false; +const logMismatchError = () => { + if (hasLoggedMismatchError) { + return; + } + console.error("Hydration completed but contains mismatches."); + hasLoggedMismatchError = true; +}; +const isSVGContainer = (container) => container.namespaceURI.includes("svg") && container.tagName !== "foreignObject"; +const isMathMLContainer = (container) => container.namespaceURI.includes("MathML"); +const getContainerType = (container) => { + if (container.nodeType !== 1) return void 0; + if (isSVGContainer(container)) return "svg"; + if (isMathMLContainer(container)) return "mathml"; + return void 0; +}; +const isComment = (node) => node.nodeType === 8; +function createHydrationFunctions(rendererInternals) { + const { + mt: mountComponent, + p: patch, + o: { + patchProp, + createText, + nextSibling, + parentNode, + remove, + insert, + createComment + } + } = rendererInternals; + const hydrate = (vnode, container) => { + if (!container.hasChildNodes()) { + warn$1( + `Attempting to hydrate existing markup but container is empty. Performing full mount instead.` + ); + patch(null, vnode, container); + flushPostFlushCbs(); + container._vnode = vnode; + return; + } + hydrateNode(container.firstChild, vnode, null, null, null); + flushPostFlushCbs(); + container._vnode = vnode; + }; + const hydrateNode = (node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized = false) => { + optimized = optimized || !!vnode.dynamicChildren; + const isFragmentStart = isComment(node) && node.data === "["; + const onMismatch = () => handleMismatch( + node, + vnode, + parentComponent, + parentSuspense, + slotScopeIds, + isFragmentStart + ); + const { type, ref, shapeFlag, patchFlag } = vnode; + let domType = node.nodeType; + vnode.el = node; + { + def(node, "__vnode", vnode, true); + def(node, "__vueParentComponent", parentComponent, true); + } + if (patchFlag === -2) { + optimized = false; + vnode.dynamicChildren = null; + } + let nextNode = null; + switch (type) { + case Text: + if (domType !== 3) { + if (vnode.children === "") { + insert(vnode.el = createText(""), parentNode(node), node); + nextNode = node; + } else { + nextNode = onMismatch(); + } + } else { + if (node.data !== vnode.children) { + warn$1( + `Hydration text mismatch in`, + node.parentNode, + ` + - rendered on server: ${JSON.stringify( + node.data + )} + - expected on client: ${JSON.stringify(vnode.children)}` + ); + logMismatchError(); + node.data = vnode.children; + } + nextNode = nextSibling(node); + } + break; + case Comment: + if (isTemplateNode(node)) { + nextNode = nextSibling(node); + replaceNode( + vnode.el = node.content.firstChild, + node, + parentComponent + ); + } else if (domType !== 8 || isFragmentStart) { + nextNode = onMismatch(); + } else { + nextNode = nextSibling(node); + } + break; + case Static: + if (isFragmentStart) { + node = nextSibling(node); + domType = node.nodeType; + } + if (domType === 1 || domType === 3) { + nextNode = node; + const needToAdoptContent = !vnode.children.length; + for (let i = 0; i < vnode.staticCount; i++) { + if (needToAdoptContent) + vnode.children += nextNode.nodeType === 1 ? nextNode.outerHTML : nextNode.data; + if (i === vnode.staticCount - 1) { + vnode.anchor = nextNode; + } + nextNode = nextSibling(nextNode); + } + return isFragmentStart ? nextSibling(nextNode) : nextNode; + } else { + onMismatch(); + } + break; + case Fragment: + if (!isFragmentStart) { + nextNode = onMismatch(); + } else { + nextNode = hydrateFragment( + node, + vnode, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + } + break; + default: + if (shapeFlag & 1) { + if ((domType !== 1 || vnode.type.toLowerCase() !== node.tagName.toLowerCase()) && !isTemplateNode(node)) { + nextNode = onMismatch(); + } else { + nextNode = hydrateElement( + node, + vnode, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + } + } else if (shapeFlag & 6) { + vnode.slotScopeIds = slotScopeIds; + const container = parentNode(node); + if (isFragmentStart) { + nextNode = locateClosingAnchor(node); + } else if (isComment(node) && node.data === "teleport start") { + nextNode = locateClosingAnchor(node, node.data, "teleport end"); + } else { + nextNode = nextSibling(node); + } + mountComponent( + vnode, + container, + null, + parentComponent, + parentSuspense, + getContainerType(container), + optimized + ); + if (isAsyncWrapper(vnode)) { + let subTree; + if (isFragmentStart) { + subTree = createVNode(Fragment); + subTree.anchor = nextNode ? nextNode.previousSibling : container.lastChild; + } else { + subTree = node.nodeType === 3 ? createTextVNode("") : createVNode("div"); + } + subTree.el = node; + vnode.component.subTree = subTree; + } + } else if (shapeFlag & 64) { + if (domType !== 8) { + nextNode = onMismatch(); + } else { + nextNode = vnode.type.hydrate( + node, + vnode, + parentComponent, + parentSuspense, + slotScopeIds, + optimized, + rendererInternals, + hydrateChildren + ); + } + } else if (shapeFlag & 128) { + nextNode = vnode.type.hydrate( + node, + vnode, + parentComponent, + parentSuspense, + getContainerType(parentNode(node)), + slotScopeIds, + optimized, + rendererInternals, + hydrateNode + ); + } else { + warn$1("Invalid HostVNode type:", type, `(${typeof type})`); + } + } + if (ref != null) { + setRef(ref, null, parentSuspense, vnode); + } + return nextNode; + }; + const hydrateElement = (el, vnode, parentComponent, parentSuspense, slotScopeIds, optimized) => { + optimized = optimized || !!vnode.dynamicChildren; + const { type, props, patchFlag, shapeFlag, dirs, transition } = vnode; + const forcePatch = type === "input" || type === "option"; + { + if (dirs) { + invokeDirectiveHook(vnode, null, parentComponent, "created"); + } + let needCallTransitionHooks = false; + if (isTemplateNode(el)) { + needCallTransitionHooks = needTransition( + null, + // no need check parentSuspense in hydration + transition + ) && parentComponent && parentComponent.vnode.props && parentComponent.vnode.props.appear; + const content = el.content.firstChild; + if (needCallTransitionHooks) { + transition.beforeEnter(content); + } + replaceNode(content, el, parentComponent); + vnode.el = el = content; + } + if (shapeFlag & 16 && // skip if element has innerHTML / textContent + !(props && (props.innerHTML || props.textContent))) { + let next = hydrateChildren( + el.firstChild, + vnode, + el, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + let hasWarned = false; + while (next) { + if (!isMismatchAllowed(el, 1 /* CHILDREN */)) { + if (!hasWarned) { + warn$1( + `Hydration children mismatch on`, + el, + ` +Server rendered element contains more child nodes than client vdom.` + ); + hasWarned = true; + } + logMismatchError(); + } + const cur = next; + next = next.nextSibling; + remove(cur); + } + } else if (shapeFlag & 8) { + let clientText = vnode.children; + if (clientText[0] === "\n" && (el.tagName === "PRE" || el.tagName === "TEXTAREA")) { + clientText = clientText.slice(1); + } + if (el.textContent !== clientText) { + if (!isMismatchAllowed(el, 0 /* TEXT */)) { + warn$1( + `Hydration text content mismatch on`, + el, + ` + - rendered on server: ${el.textContent} + - expected on client: ${vnode.children}` + ); + logMismatchError(); + } + el.textContent = vnode.children; + } + } + if (props) { + { + const isCustomElement = el.tagName.includes("-"); + for (const key in props) { + if (// #11189 skip if this node has directives that have created hooks + // as it could have mutated the DOM in any possible way + !(dirs && dirs.some((d) => d.dir.created)) && propHasMismatch(el, key, props[key], vnode, parentComponent)) { + logMismatchError(); + } + if (forcePatch && (key.endsWith("value") || key === "indeterminate") || isOn(key) && !isReservedProp(key) || // force hydrate v-bind with .prop modifiers + key[0] === "." || isCustomElement) { + patchProp(el, key, null, props[key], void 0, parentComponent); + } + } + } + } + let vnodeHooks; + if (vnodeHooks = props && props.onVnodeBeforeMount) { + invokeVNodeHook(vnodeHooks, parentComponent, vnode); + } + if (dirs) { + invokeDirectiveHook(vnode, null, parentComponent, "beforeMount"); + } + if ((vnodeHooks = props && props.onVnodeMounted) || dirs || needCallTransitionHooks) { + queueEffectWithSuspense(() => { + vnodeHooks && invokeVNodeHook(vnodeHooks, parentComponent, vnode); + needCallTransitionHooks && transition.enter(el); + dirs && invokeDirectiveHook(vnode, null, parentComponent, "mounted"); + }, parentSuspense); + } + } + return el.nextSibling; + }; + const hydrateChildren = (node, parentVNode, container, parentComponent, parentSuspense, slotScopeIds, optimized) => { + optimized = optimized || !!parentVNode.dynamicChildren; + const children = parentVNode.children; + const l = children.length; + let hasWarned = false; + for (let i = 0; i < l; i++) { + const vnode = optimized ? children[i] : children[i] = normalizeVNode(children[i]); + const isText = vnode.type === Text; + if (node) { + if (isText && !optimized) { + if (i + 1 < l && normalizeVNode(children[i + 1]).type === Text) { + insert( + createText( + node.data.slice(vnode.children.length) + ), + container, + nextSibling(node) + ); + node.data = vnode.children; + } + } + node = hydrateNode( + node, + vnode, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + } else if (isText && !vnode.children) { + insert(vnode.el = createText(""), container); + } else { + if (!isMismatchAllowed(container, 1 /* CHILDREN */)) { + if (!hasWarned) { + warn$1( + `Hydration children mismatch on`, + container, + ` +Server rendered element contains fewer child nodes than client vdom.` + ); + hasWarned = true; + } + logMismatchError(); + } + patch( + null, + vnode, + container, + null, + parentComponent, + parentSuspense, + getContainerType(container), + slotScopeIds + ); + } + } + return node; + }; + const hydrateFragment = (node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized) => { + const { slotScopeIds: fragmentSlotScopeIds } = vnode; + if (fragmentSlotScopeIds) { + slotScopeIds = slotScopeIds ? slotScopeIds.concat(fragmentSlotScopeIds) : fragmentSlotScopeIds; + } + const container = parentNode(node); + const next = hydrateChildren( + nextSibling(node), + vnode, + container, + parentComponent, + parentSuspense, + slotScopeIds, + optimized + ); + if (next && isComment(next) && next.data === "]") { + return nextSibling(vnode.anchor = next); + } else { + logMismatchError(); + insert(vnode.anchor = createComment(`]`), container, next); + return next; + } + }; + const handleMismatch = (node, vnode, parentComponent, parentSuspense, slotScopeIds, isFragment) => { + if (!isMismatchAllowed(node.parentElement, 1 /* CHILDREN */)) { + warn$1( + `Hydration node mismatch: +- rendered on server:`, + node, + node.nodeType === 3 ? `(text)` : isComment(node) && node.data === "[" ? `(start of fragment)` : ``, + ` +- expected on client:`, + vnode.type + ); + logMismatchError(); + } + vnode.el = null; + if (isFragment) { + const end = locateClosingAnchor(node); + while (true) { + const next2 = nextSibling(node); + if (next2 && next2 !== end) { + remove(next2); + } else { + break; + } + } + } + const next = nextSibling(node); + const container = parentNode(node); + remove(node); + patch( + null, + vnode, + container, + next, + parentComponent, + parentSuspense, + getContainerType(container), + slotScopeIds + ); + return next; + }; + const locateClosingAnchor = (node, open = "[", close = "]") => { + let match = 0; + while (node) { + node = nextSibling(node); + if (node && isComment(node)) { + if (node.data === open) match++; + if (node.data === close) { + if (match === 0) { + return nextSibling(node); + } else { + match--; + } + } + } + } + return node; + }; + const replaceNode = (newNode, oldNode, parentComponent) => { + const parentNode2 = oldNode.parentNode; + if (parentNode2) { + parentNode2.replaceChild(newNode, oldNode); + } + let parent = parentComponent; + while (parent) { + if (parent.vnode.el === oldNode) { + parent.vnode.el = parent.subTree.el = newNode; + } + parent = parent.parent; + } + }; + const isTemplateNode = (node) => { + return node.nodeType === 1 && node.tagName === "TEMPLATE"; + }; + return [hydrate, hydrateNode]; +} +function propHasMismatch(el, key, clientValue, vnode, instance) { + let mismatchType; + let mismatchKey; + let actual; + let expected; + if (key === "class") { + actual = el.getAttribute("class"); + expected = normalizeClass(clientValue); + if (!isSetEqual(toClassSet(actual || ""), toClassSet(expected))) { + mismatchType = 2 /* CLASS */; + mismatchKey = `class`; + } + } else if (key === "style") { + actual = el.getAttribute("style") || ""; + expected = isString(clientValue) ? clientValue : stringifyStyle(normalizeStyle(clientValue)); + const actualMap = toStyleMap(actual); + const expectedMap = toStyleMap(expected); + if (vnode.dirs) { + for (const { dir, value } of vnode.dirs) { + if (dir.name === "show" && !value) { + expectedMap.set("display", "none"); + } + } + } + if (instance) { + resolveCssVars(instance, vnode, expectedMap); + } + if (!isMapEqual(actualMap, expectedMap)) { + mismatchType = 3 /* STYLE */; + mismatchKey = "style"; + } + } else if (el instanceof SVGElement && isKnownSvgAttr(key) || el instanceof HTMLElement && (isBooleanAttr(key) || isKnownHtmlAttr(key))) { + if (isBooleanAttr(key)) { + actual = el.hasAttribute(key); + expected = includeBooleanAttr(clientValue); + } else if (clientValue == null) { + actual = el.hasAttribute(key); + expected = false; + } else { + if (el.hasAttribute(key)) { + actual = el.getAttribute(key); + } else if (key === "value" && el.tagName === "TEXTAREA") { + actual = el.value; + } else { + actual = false; + } + expected = isRenderableAttrValue(clientValue) ? String(clientValue) : false; + } + if (actual !== expected) { + mismatchType = 4 /* ATTRIBUTE */; + mismatchKey = key; + } + } + if (mismatchType != null && !isMismatchAllowed(el, mismatchType)) { + const format = (v) => v === false ? `(not rendered)` : `${mismatchKey}="${v}"`; + const preSegment = `Hydration ${MismatchTypeString[mismatchType]} mismatch on`; + const postSegment = ` + - rendered on server: ${format(actual)} + - expected on client: ${format(expected)} + Note: this mismatch is check-only. The DOM will not be rectified in production due to performance overhead. + You should fix the source of the mismatch.`; + { + warn$1(preSegment, el, postSegment); + } + return true; + } + return false; +} +function toClassSet(str) { + return new Set(str.trim().split(/\s+/)); +} +function isSetEqual(a, b) { + if (a.size !== b.size) { + return false; + } + for (const s of a) { + if (!b.has(s)) { + return false; + } + } + return true; +} +function toStyleMap(str) { + const styleMap = /* @__PURE__ */ new Map(); + for (const item of str.split(";")) { + let [key, value] = item.split(":"); + key = key.trim(); + value = value && value.trim(); + if (key && value) { + styleMap.set(key, value); + } + } + return styleMap; +} +function isMapEqual(a, b) { + if (a.size !== b.size) { + return false; + } + for (const [key, value] of a) { + if (value !== b.get(key)) { + return false; + } + } + return true; +} +function resolveCssVars(instance, vnode, expectedMap) { + const root = instance.subTree; + if (instance.getCssVars && (vnode === root || root && root.type === Fragment && root.children.includes(vnode))) { + const cssVars = instance.getCssVars(); + for (const key in cssVars) { + expectedMap.set( + `--${getEscapedCssVarName(key)}`, + String(cssVars[key]) + ); + } + } + if (vnode === root && instance.parent) { + resolveCssVars(instance.parent, instance.vnode, expectedMap); + } +} +const allowMismatchAttr = "data-allow-mismatch"; +const MismatchTypeString = { + [0 /* TEXT */]: "text", + [1 /* CHILDREN */]: "children", + [2 /* CLASS */]: "class", + [3 /* STYLE */]: "style", + [4 /* ATTRIBUTE */]: "attribute" +}; +function isMismatchAllowed(el, allowedType) { + if (allowedType === 0 /* TEXT */ || allowedType === 1 /* CHILDREN */) { + while (el && !el.hasAttribute(allowMismatchAttr)) { + el = el.parentElement; + } + } + const allowedAttr = el && el.getAttribute(allowMismatchAttr); + if (allowedAttr == null) { + return false; + } else if (allowedAttr === "") { + return true; + } else { + const list = allowedAttr.split(","); + if (allowedType === 0 /* TEXT */ && list.includes("children")) { + return true; + } + return allowedAttr.split(",").includes(MismatchTypeString[allowedType]); + } +} + +const requestIdleCallback = getGlobalThis().requestIdleCallback || ((cb) => setTimeout(cb, 1)); +const cancelIdleCallback = getGlobalThis().cancelIdleCallback || ((id) => clearTimeout(id)); +const hydrateOnIdle = (timeout = 1e4) => (hydrate) => { + const id = requestIdleCallback(hydrate, { timeout }); + return () => cancelIdleCallback(id); +}; +function elementIsVisibleInViewport(el) { + const { top, left, bottom, right } = el.getBoundingClientRect(); + const { innerHeight, innerWidth } = window; + return (top > 0 && top < innerHeight || bottom > 0 && bottom < innerHeight) && (left > 0 && left < innerWidth || right > 0 && right < innerWidth); +} +const hydrateOnVisible = (opts) => (hydrate, forEach) => { + const ob = new IntersectionObserver((entries) => { + for (const e of entries) { + if (!e.isIntersecting) continue; + ob.disconnect(); + hydrate(); + break; + } + }, opts); + forEach((el) => { + if (!(el instanceof Element)) return; + if (elementIsVisibleInViewport(el)) { + hydrate(); + ob.disconnect(); + return false; + } + ob.observe(el); + }); + return () => ob.disconnect(); +}; +const hydrateOnMediaQuery = (query) => (hydrate) => { + if (query) { + const mql = matchMedia(query); + if (mql.matches) { + hydrate(); + } else { + mql.addEventListener("change", hydrate, { once: true }); + return () => mql.removeEventListener("change", hydrate); + } + } +}; +const hydrateOnInteraction = (interactions = []) => (hydrate, forEach) => { + if (isString(interactions)) interactions = [interactions]; + let hasHydrated = false; + const doHydrate = (e) => { + if (!hasHydrated) { + hasHydrated = true; + teardown(); + hydrate(); + e.target.dispatchEvent(new e.constructor(e.type, e)); + } + }; + const teardown = () => { + forEach((el) => { + for (const i of interactions) { + el.removeEventListener(i, doHydrate); + } + }); + }; + forEach((el) => { + for (const i of interactions) { + el.addEventListener(i, doHydrate, { once: true }); + } + }); + return teardown; +}; +function forEachElement(node, cb) { + if (isComment(node) && node.data === "[") { + let depth = 1; + let next = node.nextSibling; + while (next) { + if (next.nodeType === 1) { + const result = cb(next); + if (result === false) { + break; + } + } else if (isComment(next)) { + if (next.data === "]") { + if (--depth === 0) break; + } else if (next.data === "[") { + depth++; + } + } + next = next.nextSibling; + } + } else { + cb(node); + } +} + +const isAsyncWrapper = (i) => !!i.type.__asyncLoader; +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function defineAsyncComponent(source) { + if (isFunction(source)) { + source = { loader: source }; + } + const { + loader, + loadingComponent, + errorComponent, + delay = 200, + hydrate: hydrateStrategy, + timeout, + // undefined = never times out + suspensible = true, + onError: userOnError + } = source; + let pendingRequest = null; + let resolvedComp; + let retries = 0; + const retry = () => { + retries++; + pendingRequest = null; + return load(); + }; + const load = () => { + let thisRequest; + return pendingRequest || (thisRequest = pendingRequest = loader().catch((err) => { + err = err instanceof Error ? err : new Error(String(err)); + if (userOnError) { + return new Promise((resolve, reject) => { + const userRetry = () => resolve(retry()); + const userFail = () => reject(err); + userOnError(err, userRetry, userFail, retries + 1); + }); + } else { + throw err; + } + }).then((comp) => { + if (thisRequest !== pendingRequest && pendingRequest) { + return pendingRequest; + } + if (!comp) { + warn$1( + `Async component loader resolved to undefined. If you are using retry(), make sure to return its return value.` + ); + } + if (comp && (comp.__esModule || comp[Symbol.toStringTag] === "Module")) { + comp = comp.default; + } + if (comp && !isObject(comp) && !isFunction(comp)) { + throw new Error(`Invalid async component load result: ${comp}`); + } + resolvedComp = comp; + return comp; + })); + }; + return defineComponent({ + name: "AsyncComponentWrapper", + __asyncLoader: load, + __asyncHydrate(el, instance, hydrate) { + const doHydrate = hydrateStrategy ? () => { + const teardown = hydrateStrategy( + hydrate, + (cb) => forEachElement(el, cb) + ); + if (teardown) { + (instance.bum || (instance.bum = [])).push(teardown); + } + } : hydrate; + if (resolvedComp) { + doHydrate(); + } else { + load().then(() => !instance.isUnmounted && doHydrate()); + } + }, + get __asyncResolved() { + return resolvedComp; + }, + setup() { + const instance = currentInstance; + markAsyncBoundary(instance); + if (resolvedComp) { + return () => createInnerComp(resolvedComp, instance); + } + const onError = (err) => { + pendingRequest = null; + handleError( + err, + instance, + 13, + !errorComponent + ); + }; + if (suspensible && instance.suspense || isInSSRComponentSetup) { + return load().then((comp) => { + return () => createInnerComp(comp, instance); + }).catch((err) => { + onError(err); + return () => errorComponent ? createVNode(errorComponent, { + error: err + }) : null; + }); + } + const loaded = ref(false); + const error = ref(); + const delayed = ref(!!delay); + if (delay) { + setTimeout(() => { + delayed.value = false; + }, delay); + } + if (timeout != null) { + setTimeout(() => { + if (!loaded.value && !error.value) { + const err = new Error( + `Async component timed out after ${timeout}ms.` + ); + onError(err); + error.value = err; + } + }, timeout); + } + load().then(() => { + loaded.value = true; + if (instance.parent && isKeepAlive(instance.parent.vnode)) { + instance.parent.update(); + } + }).catch((err) => { + onError(err); + error.value = err; + }); + return () => { + if (loaded.value && resolvedComp) { + return createInnerComp(resolvedComp, instance); + } else if (error.value && errorComponent) { + return createVNode(errorComponent, { + error: error.value + }); + } else if (loadingComponent && !delayed.value) { + return createVNode(loadingComponent); + } + }; + } + }); +} +function createInnerComp(comp, parent) { + const { ref: ref2, props, children, ce } = parent.vnode; + const vnode = createVNode(comp, props, children); + vnode.ref = ref2; + vnode.ce = ce; + delete parent.vnode.ce; + return vnode; +} + +const isKeepAlive = (vnode) => vnode.type.__isKeepAlive; +const KeepAliveImpl = { + name: `KeepAlive`, + // Marker for special handling inside the renderer. We are not using a === + // check directly on KeepAlive in the renderer, because importing it directly + // would prevent it from being tree-shaken. + __isKeepAlive: true, + props: { + include: [String, RegExp, Array], + exclude: [String, RegExp, Array], + max: [String, Number] + }, + setup(props, { slots }) { + const instance = getCurrentInstance(); + const sharedContext = instance.ctx; + if (!sharedContext.renderer) { + return () => { + const children = slots.default && slots.default(); + return children && children.length === 1 ? children[0] : children; + }; + } + const cache = /* @__PURE__ */ new Map(); + const keys = /* @__PURE__ */ new Set(); + let current = null; + { + instance.__v_cache = cache; + } + const parentSuspense = instance.suspense; + const { + renderer: { + p: patch, + m: move, + um: _unmount, + o: { createElement } + } + } = sharedContext; + const storageContainer = createElement("div"); + sharedContext.activate = (vnode, container, anchor, namespace, optimized) => { + const instance2 = vnode.component; + move(vnode, container, anchor, 0, parentSuspense); + patch( + instance2.vnode, + vnode, + container, + anchor, + instance2, + parentSuspense, + namespace, + vnode.slotScopeIds, + optimized + ); + queuePostRenderEffect(() => { + instance2.isDeactivated = false; + if (instance2.a) { + invokeArrayFns(instance2.a); + } + const vnodeHook = vnode.props && vnode.props.onVnodeMounted; + if (vnodeHook) { + invokeVNodeHook(vnodeHook, instance2.parent, vnode); + } + }, parentSuspense); + { + devtoolsComponentAdded(instance2); + } + }; + sharedContext.deactivate = (vnode) => { + const instance2 = vnode.component; + invalidateMount(instance2.m); + invalidateMount(instance2.a); + move(vnode, storageContainer, null, 1, parentSuspense); + queuePostRenderEffect(() => { + if (instance2.da) { + invokeArrayFns(instance2.da); + } + const vnodeHook = vnode.props && vnode.props.onVnodeUnmounted; + if (vnodeHook) { + invokeVNodeHook(vnodeHook, instance2.parent, vnode); + } + instance2.isDeactivated = true; + }, parentSuspense); + { + devtoolsComponentAdded(instance2); + } + }; + function unmount(vnode) { + resetShapeFlag(vnode); + _unmount(vnode, instance, parentSuspense, true); + } + function pruneCache(filter) { + cache.forEach((vnode, key) => { + const name = getComponentName(vnode.type); + if (name && !filter(name)) { + pruneCacheEntry(key); + } + }); + } + function pruneCacheEntry(key) { + const cached = cache.get(key); + if (cached && (!current || !isSameVNodeType(cached, current))) { + unmount(cached); + } else if (current) { + resetShapeFlag(current); + } + cache.delete(key); + keys.delete(key); + } + watch( + () => [props.include, props.exclude], + ([include, exclude]) => { + include && pruneCache((name) => matches(include, name)); + exclude && pruneCache((name) => !matches(exclude, name)); + }, + // prune post-render after `current` has been updated + { flush: "post", deep: true } + ); + let pendingCacheKey = null; + const cacheSubtree = () => { + if (pendingCacheKey != null) { + if (isSuspense(instance.subTree.type)) { + queuePostRenderEffect(() => { + cache.set(pendingCacheKey, getInnerChild(instance.subTree)); + }, instance.subTree.suspense); + } else { + cache.set(pendingCacheKey, getInnerChild(instance.subTree)); + } + } + }; + onMounted(cacheSubtree); + onUpdated(cacheSubtree); + onBeforeUnmount(() => { + cache.forEach((cached) => { + const { subTree, suspense } = instance; + const vnode = getInnerChild(subTree); + if (cached.type === vnode.type && cached.key === vnode.key) { + resetShapeFlag(vnode); + const da = vnode.component.da; + da && queuePostRenderEffect(da, suspense); + return; + } + unmount(cached); + }); + }); + return () => { + pendingCacheKey = null; + if (!slots.default) { + return current = null; + } + const children = slots.default(); + const rawVNode = children[0]; + if (children.length > 1) { + { + warn$1(`KeepAlive should contain exactly one component child.`); + } + current = null; + return children; + } else if (!isVNode(rawVNode) || !(rawVNode.shapeFlag & 4) && !(rawVNode.shapeFlag & 128)) { + current = null; + return rawVNode; + } + let vnode = getInnerChild(rawVNode); + if (vnode.type === Comment) { + current = null; + return vnode; + } + const comp = vnode.type; + const name = getComponentName( + isAsyncWrapper(vnode) ? vnode.type.__asyncResolved || {} : comp + ); + const { include, exclude, max } = props; + if (include && (!name || !matches(include, name)) || exclude && name && matches(exclude, name)) { + vnode.shapeFlag &= ~256; + current = vnode; + return rawVNode; + } + const key = vnode.key == null ? comp : vnode.key; + const cachedVNode = cache.get(key); + if (vnode.el) { + vnode = cloneVNode(vnode); + if (rawVNode.shapeFlag & 128) { + rawVNode.ssContent = vnode; + } + } + pendingCacheKey = key; + if (cachedVNode) { + vnode.el = cachedVNode.el; + vnode.component = cachedVNode.component; + if (vnode.transition) { + setTransitionHooks(vnode, vnode.transition); + } + vnode.shapeFlag |= 512; + keys.delete(key); + keys.add(key); + } else { + keys.add(key); + if (max && keys.size > parseInt(max, 10)) { + pruneCacheEntry(keys.values().next().value); + } + } + vnode.shapeFlag |= 256; + current = vnode; + return isSuspense(rawVNode.type) ? rawVNode : vnode; + }; + } +}; +const KeepAlive = KeepAliveImpl; +function matches(pattern, name) { + if (isArray(pattern)) { + return pattern.some((p) => matches(p, name)); + } else if (isString(pattern)) { + return pattern.split(",").includes(name); + } else if (isRegExp(pattern)) { + pattern.lastIndex = 0; + return pattern.test(name); + } + return false; +} +function onActivated(hook, target) { + registerKeepAliveHook(hook, "a", target); +} +function onDeactivated(hook, target) { + registerKeepAliveHook(hook, "da", target); +} +function registerKeepAliveHook(hook, type, target = currentInstance) { + const wrappedHook = hook.__wdc || (hook.__wdc = () => { + let current = target; + while (current) { + if (current.isDeactivated) { + return; + } + current = current.parent; + } + return hook(); + }); + injectHook(type, wrappedHook, target); + if (target) { + let current = target.parent; + while (current && current.parent) { + if (isKeepAlive(current.parent.vnode)) { + injectToKeepAliveRoot(wrappedHook, type, target, current); + } + current = current.parent; + } + } +} +function injectToKeepAliveRoot(hook, type, target, keepAliveRoot) { + const injected = injectHook( + type, + hook, + keepAliveRoot, + true + /* prepend */ + ); + onUnmounted(() => { + remove(keepAliveRoot[type], injected); + }, target); +} +function resetShapeFlag(vnode) { + vnode.shapeFlag &= ~256; + vnode.shapeFlag &= ~512; +} +function getInnerChild(vnode) { + return vnode.shapeFlag & 128 ? vnode.ssContent : vnode; +} + +function injectHook(type, hook, target = currentInstance, prepend = false) { + if (target) { + const hooks = target[type] || (target[type] = []); + const wrappedHook = hook.__weh || (hook.__weh = (...args) => { + pauseTracking(); + const reset = setCurrentInstance(target); + const res = callWithAsyncErrorHandling(hook, target, type, args); + reset(); + resetTracking(); + return res; + }); + if (prepend) { + hooks.unshift(wrappedHook); + } else { + hooks.push(wrappedHook); + } + return wrappedHook; + } else { + const apiName = toHandlerKey(ErrorTypeStrings$1[type].replace(/ hook$/, "")); + warn$1( + `${apiName} is called when there is no active component instance to be associated with. Lifecycle injection APIs can only be used during execution of setup().` + (` If you are using async setup(), make sure to register lifecycle hooks before the first await statement.` ) + ); + } +} +const createHook = (lifecycle) => (hook, target = currentInstance) => { + if (!isInSSRComponentSetup || lifecycle === "sp") { + injectHook(lifecycle, (...args) => hook(...args), target); + } +}; +const onBeforeMount = createHook("bm"); +const onMounted = createHook("m"); +const onBeforeUpdate = createHook( + "bu" +); +const onUpdated = createHook("u"); +const onBeforeUnmount = createHook( + "bum" +); +const onUnmounted = createHook("um"); +const onServerPrefetch = createHook( + "sp" +); +const onRenderTriggered = createHook("rtg"); +const onRenderTracked = createHook("rtc"); +function onErrorCaptured(hook, target = currentInstance) { + injectHook("ec", hook, target); +} + +const COMPONENTS = "components"; +const DIRECTIVES = "directives"; +function resolveComponent(name, maybeSelfReference) { + return resolveAsset(COMPONENTS, name, true, maybeSelfReference) || name; +} +const NULL_DYNAMIC_COMPONENT = Symbol.for("v-ndc"); +function resolveDynamicComponent(component) { + if (isString(component)) { + return resolveAsset(COMPONENTS, component, false) || component; + } else { + return component || NULL_DYNAMIC_COMPONENT; + } +} +function resolveDirective(name) { + return resolveAsset(DIRECTIVES, name); +} +function resolveAsset(type, name, warnMissing = true, maybeSelfReference = false) { + const instance = currentRenderingInstance || currentInstance; + if (instance) { + const Component = instance.type; + if (type === COMPONENTS) { + const selfName = getComponentName( + Component, + false + ); + if (selfName && (selfName === name || selfName === camelize(name) || selfName === capitalize(camelize(name)))) { + return Component; + } + } + const res = ( + // local registration + // check instance[type] first which is resolved for options API + resolve(instance[type] || Component[type], name) || // global registration + resolve(instance.appContext[type], name) + ); + if (!res && maybeSelfReference) { + return Component; + } + if (warnMissing && !res) { + const extra = type === COMPONENTS ? ` +If this is a native custom element, make sure to exclude it from component resolution via compilerOptions.isCustomElement.` : ``; + warn$1(`Failed to resolve ${type.slice(0, -1)}: ${name}${extra}`); + } + return res; + } else { + warn$1( + `resolve${capitalize(type.slice(0, -1))} can only be used in render() or setup().` + ); + } +} +function resolve(registry, name) { + return registry && (registry[name] || registry[camelize(name)] || registry[capitalize(camelize(name))]); +} + +function renderList(source, renderItem, cache, index) { + let ret; + const cached = cache && cache[index]; + const sourceIsArray = isArray(source); + if (sourceIsArray || isString(source)) { + const sourceIsReactiveArray = sourceIsArray && isReactive(source); + let needsWrap = false; + if (sourceIsReactiveArray) { + needsWrap = !isShallow(source); + source = shallowReadArray(source); + } + ret = new Array(source.length); + for (let i = 0, l = source.length; i < l; i++) { + ret[i] = renderItem( + needsWrap ? toReactive(source[i]) : source[i], + i, + void 0, + cached && cached[i] + ); + } + } else if (typeof source === "number") { + if (!Number.isInteger(source)) { + warn$1(`The v-for range expect an integer value but got ${source}.`); + } + ret = new Array(source); + for (let i = 0; i < source; i++) { + ret[i] = renderItem(i + 1, i, void 0, cached && cached[i]); + } + } else if (isObject(source)) { + if (source[Symbol.iterator]) { + ret = Array.from( + source, + (item, i) => renderItem(item, i, void 0, cached && cached[i]) + ); + } else { + const keys = Object.keys(source); + ret = new Array(keys.length); + for (let i = 0, l = keys.length; i < l; i++) { + const key = keys[i]; + ret[i] = renderItem(source[key], key, i, cached && cached[i]); + } + } + } else { + ret = []; + } + if (cache) { + cache[index] = ret; + } + return ret; +} + +function createSlots(slots, dynamicSlots) { + for (let i = 0; i < dynamicSlots.length; i++) { + const slot = dynamicSlots[i]; + if (isArray(slot)) { + for (let j = 0; j < slot.length; j++) { + slots[slot[j].name] = slot[j].fn; + } + } else if (slot) { + slots[slot.name] = slot.key ? (...args) => { + const res = slot.fn(...args); + if (res) res.key = slot.key; + return res; + } : slot.fn; + } + } + return slots; +} + +function renderSlot(slots, name, props = {}, fallback, noSlotted) { + if (currentRenderingInstance.ce || currentRenderingInstance.parent && isAsyncWrapper(currentRenderingInstance.parent) && currentRenderingInstance.parent.ce) { + if (name !== "default") props.name = name; + return openBlock(), createBlock( + Fragment, + null, + [createVNode("slot", props, fallback && fallback())], + 64 + ); + } + let slot = slots[name]; + if (slot && slot.length > 1) { + warn$1( + `SSR-optimized slot function detected in a non-SSR-optimized render function. You need to mark this component with $dynamic-slots in the parent template.` + ); + slot = () => []; + } + if (slot && slot._c) { + slot._d = false; + } + openBlock(); + const validSlotContent = slot && ensureValidVNode(slot(props)); + const slotKey = props.key || // slot content array of a dynamic conditional slot may have a branch + // key attached in the `createSlots` helper, respect that + validSlotContent && validSlotContent.key; + const rendered = createBlock( + Fragment, + { + key: (slotKey && !isSymbol(slotKey) ? slotKey : `_${name}`) + // #7256 force differentiate fallback content from actual content + (!validSlotContent && fallback ? "_fb" : "") + }, + validSlotContent || (fallback ? fallback() : []), + validSlotContent && slots._ === 1 ? 64 : -2 + ); + if (!noSlotted && rendered.scopeId) { + rendered.slotScopeIds = [rendered.scopeId + "-s"]; + } + if (slot && slot._c) { + slot._d = true; + } + return rendered; +} +function ensureValidVNode(vnodes) { + return vnodes.some((child) => { + if (!isVNode(child)) return true; + if (child.type === Comment) return false; + if (child.type === Fragment && !ensureValidVNode(child.children)) + return false; + return true; + }) ? vnodes : null; +} + +function toHandlers(obj, preserveCaseIfNecessary) { + const ret = {}; + if (!isObject(obj)) { + warn$1(`v-on with no argument expects an object value.`); + return ret; + } + for (const key in obj) { + ret[preserveCaseIfNecessary && /[A-Z]/.test(key) ? `on:${key}` : toHandlerKey(key)] = obj[key]; + } + return ret; +} + +const getPublicInstance = (i) => { + if (!i) return null; + if (isStatefulComponent(i)) return getComponentPublicInstance(i); + return getPublicInstance(i.parent); +}; +const publicPropertiesMap = ( + // Move PURE marker to new line to workaround compiler discarding it + // due to type annotation + /* @__PURE__ */ extend(/* @__PURE__ */ Object.create(null), { + $: (i) => i, + $el: (i) => i.vnode.el, + $data: (i) => i.data, + $props: (i) => shallowReadonly(i.props) , + $attrs: (i) => shallowReadonly(i.attrs) , + $slots: (i) => shallowReadonly(i.slots) , + $refs: (i) => shallowReadonly(i.refs) , + $parent: (i) => getPublicInstance(i.parent), + $root: (i) => getPublicInstance(i.root), + $host: (i) => i.ce, + $emit: (i) => i.emit, + $options: (i) => resolveMergedOptions(i) , + $forceUpdate: (i) => i.f || (i.f = () => { + queueJob(i.update); + }), + $nextTick: (i) => i.n || (i.n = nextTick.bind(i.proxy)), + $watch: (i) => instanceWatch.bind(i) + }) +); +const isReservedPrefix = (key) => key === "_" || key === "$"; +const hasSetupBinding = (state, key) => state !== EMPTY_OBJ && !state.__isScriptSetup && hasOwn(state, key); +const PublicInstanceProxyHandlers = { + get({ _: instance }, key) { + if (key === "__v_skip") { + return true; + } + const { ctx, setupState, data, props, accessCache, type, appContext } = instance; + if (key === "__isVue") { + return true; + } + let normalizedProps; + if (key[0] !== "$") { + const n = accessCache[key]; + if (n !== void 0) { + switch (n) { + case 1 /* SETUP */: + return setupState[key]; + case 2 /* DATA */: + return data[key]; + case 4 /* CONTEXT */: + return ctx[key]; + case 3 /* PROPS */: + return props[key]; + } + } else if (hasSetupBinding(setupState, key)) { + accessCache[key] = 1 /* SETUP */; + return setupState[key]; + } else if (data !== EMPTY_OBJ && hasOwn(data, key)) { + accessCache[key] = 2 /* DATA */; + return data[key]; + } else if ( + // only cache other properties when instance has declared (thus stable) + // props + (normalizedProps = instance.propsOptions[0]) && hasOwn(normalizedProps, key) + ) { + accessCache[key] = 3 /* PROPS */; + return props[key]; + } else if (ctx !== EMPTY_OBJ && hasOwn(ctx, key)) { + accessCache[key] = 4 /* CONTEXT */; + return ctx[key]; + } else if (shouldCacheAccess) { + accessCache[key] = 0 /* OTHER */; + } + } + const publicGetter = publicPropertiesMap[key]; + let cssModule, globalProperties; + if (publicGetter) { + if (key === "$attrs") { + track(instance.attrs, "get", ""); + markAttrsAccessed(); + } else if (key === "$slots") { + track(instance, "get", key); + } + return publicGetter(instance); + } else if ( + // css module (injected by vue-loader) + (cssModule = type.__cssModules) && (cssModule = cssModule[key]) + ) { + return cssModule; + } else if (ctx !== EMPTY_OBJ && hasOwn(ctx, key)) { + accessCache[key] = 4 /* CONTEXT */; + return ctx[key]; + } else if ( + // global properties + globalProperties = appContext.config.globalProperties, hasOwn(globalProperties, key) + ) { + { + return globalProperties[key]; + } + } else if (currentRenderingInstance && (!isString(key) || // #1091 avoid internal isRef/isVNode checks on component instance leading + // to infinite warning loop + key.indexOf("__v") !== 0)) { + if (data !== EMPTY_OBJ && isReservedPrefix(key[0]) && hasOwn(data, key)) { + warn$1( + `Property ${JSON.stringify( + key + )} must be accessed via $data because it starts with a reserved character ("$" or "_") and is not proxied on the render context.` + ); + } else if (instance === currentRenderingInstance) { + warn$1( + `Property ${JSON.stringify(key)} was accessed during render but is not defined on instance.` + ); + } + } + }, + set({ _: instance }, key, value) { + const { data, setupState, ctx } = instance; + if (hasSetupBinding(setupState, key)) { + setupState[key] = value; + return true; + } else if (setupState.__isScriptSetup && hasOwn(setupState, key)) { + warn$1(`Cannot mutate + - - -

- + -
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b/examples/server/public_legacy/completion.js new file mode 100644 index 0000000000..30df7c2fa7 --- /dev/null +++ b/examples/server/public_legacy/completion.js @@ -0,0 +1,209 @@ +const paramDefaults = { + stream: true, + n_predict: 500, + temperature: 0.2, + stop: [""] +}; + +let generation_settings = null; + + +// Completes the prompt as a generator. Recommended for most use cases. +// +// Example: +// +// import { llama } from '/completion.js' +// +// const request = llama("Tell me a joke", {n_predict: 800}) +// for await (const chunk of request) { +// document.write(chunk.data.content) +// } +// +export async function* llama(prompt, params = {}, config = {}) { + let controller = config.controller; + const api_url = config.api_url?.replace(/\/+$/, '') || ""; + + if (!controller) { + controller = new AbortController(); + } + + const completionParams = { ...paramDefaults, ...params, prompt }; + + const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { + method: 'POST', + body: JSON.stringify(completionParams), + headers: { + 'Connection': 'keep-alive', + 'Content-Type': 'application/json', + 'Accept': 'text/event-stream', + ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) + }, + signal: controller.signal, + }); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + + let content = ""; + let leftover = ""; // Buffer for partially read lines + + try { + let cont = true; + + while (cont) { + const result = await reader.read(); + if (result.done) { + break; + } + + // Add any leftover data to the current chunk of data + const text = leftover + decoder.decode(result.value); + + // Check if the last character is a line break + const endsWithLineBreak = text.endsWith('\n'); + + // Split the text into lines + let lines = text.split('\n'); + + // If the text doesn't end with a line break, then the last line is incomplete + // Store it in leftover to be added to the next chunk of data + if (!endsWithLineBreak) { + leftover = lines.pop(); + } else { + leftover = ""; // Reset leftover if we have a line break at the end + } + + // Parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const line of lines) { + const match = regex.exec(line); + if (match) { + result[match[1]] = match[2]; + if (result.data === '[DONE]') { + cont = false; + break; + } + + // since we know this is llama.cpp, let's just decode the json in data + if (result.data) { + result.data = JSON.parse(result.data); + content += result.data.content; + + // yield + yield result; + + // if we got a stop token from server, we will break here + if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } + cont = false; + break; + } + } + if (result.error) { + try { + result.error = JSON.parse(result.error); + if (result.error.message.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); + } + } catch(e) { + console.error(`llama.cpp error ${result.error}`) + } + } + } + } + } + } catch (e) { + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } + throw e; + } + finally { + controller.abort(); + } + + return content; +} + +// Call llama, return an event target that you can subscribe to +// +// Example: +// +// import { llamaEventTarget } from '/completion.js' +// +// const conn = llamaEventTarget(prompt) +// conn.addEventListener("message", (chunk) => { +// document.write(chunk.detail.content) +// }) +// +export const llamaEventTarget = (prompt, params = {}, config = {}) => { + const eventTarget = new EventTarget(); + (async () => { + let content = ""; + for await (const chunk of llama(prompt, params, config)) { + if (chunk.data) { + content += chunk.data.content; + eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); + } + if (chunk.data.generation_settings) { + eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); + } + if (chunk.data.timings) { + eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); + } + } + eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); + })(); + return eventTarget; +} + +// Call llama, return a promise that resolves to the completed text. This does not support streaming +// +// Example: +// +// llamaPromise(prompt).then((content) => { +// document.write(content) +// }) +// +// or +// +// const content = await llamaPromise(prompt) +// document.write(content) +// +export const llamaPromise = (prompt, params = {}, config = {}) => { + return new Promise(async (resolve, reject) => { + let content = ""; + try { + for await (const chunk of llama(prompt, params, config)) { + content += chunk.data.content; + } + resolve(content); + } catch (error) { + reject(error); + } + }); +}; + +/** + * (deprecated) + */ +export const llamaComplete = async (params, controller, callback) => { + for await (const chunk of llama(params.prompt, params, { controller })) { + callback(chunk); + } +} + +// Get the model info from the server. This is useful for getting the context window and so on. +export const llamaModelInfo = async (config = {}) => { + if (!generation_settings) { + const api_url = config.api_url?.replace(/\/+$/, '') || ""; + const props = await fetch(`${api_url}/props`).then(r => r.json()); + generation_settings = props.default_generation_settings; + } + return generation_settings; +} diff --git a/examples/server/public/favicon.ico b/examples/server/public_legacy/favicon.ico similarity index 100% rename from examples/server/public/favicon.ico rename to examples/server/public_legacy/favicon.ico diff --git a/examples/server/public/index-new.html b/examples/server/public_legacy/index-new.html similarity index 95% rename from examples/server/public/index-new.html rename to examples/server/public_legacy/index-new.html index c87dd8f1e1..8bfa380e57 100644 --- a/examples/server/public/index-new.html +++ b/examples/server/public_legacy/index-new.html @@ -40,10 +40,15 @@ repeat_last_n: 0, // 0 = disable penalty, -1 = context size repeat_penalty: 1.0, // 1.0 = disabled penalize_nl: false, // true only useful for infinite completion + dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well + dry_base: 1.75, // 0.0 = disabled + dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well + dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) top_k: 0, // <= 0 to use vocab size top_p: 1.0, // 1.0 = disabled min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4 - tfs_z: 1.0, // 1.0 = disabled + xtc_probability: 0.0, // 0 = disabled; + xtc_threshold: 0.1, // > 0.5 disables XTC; typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -831,11 +836,16 @@ return html`
${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })} ${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })} - ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} - ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })} + ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })} + ${FloatField({ label: "DRY Penalty Multiplier", title: "Set the DRY repetition penalty multiplier. Default is 0.0, which disables DRY.", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })} + ${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })} + ${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })} + ${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })} ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
@@ -1132,12 +1142,15 @@ document.addEventListener('DOMContentLoaded', (event) => { const snapSettings = { temperature: { snapValue: 1.0, snapRangeMultiplier: 6 }, min_p: { snapValue: 0.05, snapRangeMultiplier: 2 }, + xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 }, + xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 }, top_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, - tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 }, typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 }, presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 }, frequency_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 }, + dry_multiplier: { snapValue: 0.0, snapRangeMultiplier: 4 }, + dry_base: { snapValue: 1.75, snapRangeMultiplier: 4 }, }; // add an event listener for each slider Object.keys(snapSettings).forEach(sliderName => { diff --git a/examples/server/public_legacy/index.html b/examples/server/public_legacy/index.html new file mode 100644 index 0000000000..a95f5c6df8 --- /dev/null +++ b/examples/server/public_legacy/index.html @@ -0,0 +1,1303 @@ + + + + + + llama.cpp - chat + + + + + + + +
+ +
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7267f3f9c7..e67bb15c17 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public_legacy/json-schema-to-grammar.mjs @@ -529,7 +529,7 @@ export class SchemaConverter { return joinSeq(); }; - return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space") + return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space") } _notStrings(strings) { diff --git a/examples/server/public_legacy/loading.html b/examples/server/public_legacy/loading.html new file mode 100644 index 0000000000..c3fd19a0f5 --- /dev/null +++ b/examples/server/public_legacy/loading.html @@ -0,0 +1,12 @@ + + + + + + +
+ The model is loading. Please wait.
+ The user interface will appear soon. +
+ + diff --git a/examples/server/public/prompt-formats.js b/examples/server/public_legacy/prompt-formats.js similarity index 100% rename from examples/server/public/prompt-formats.js rename to examples/server/public_legacy/prompt-formats.js diff --git a/examples/server/public/style.css b/examples/server/public_legacy/style.css old mode 100755 new mode 100644 similarity index 100% rename from examples/server/public/style.css rename to examples/server/public_legacy/style.css diff --git a/examples/server/public/system-prompts.js b/examples/server/public_legacy/system-prompts.js similarity index 100% rename from examples/server/public/system-prompts.js rename to examples/server/public_legacy/system-prompts.js diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public_legacy/theme-beeninorder.css similarity index 100% rename from examples/server/public/theme-beeninorder.css rename to examples/server/public_legacy/theme-beeninorder.css diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public_legacy/theme-ketivah.css similarity index 100% rename from examples/server/public/theme-ketivah.css rename to examples/server/public_legacy/theme-ketivah.css diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public_legacy/theme-mangotango.css similarity index 100% rename from examples/server/public/theme-mangotango.css rename to examples/server/public_legacy/theme-mangotango.css diff --git a/examples/server/public/theme-playground.css b/examples/server/public_legacy/theme-playground.css similarity index 100% rename from examples/server/public/theme-playground.css rename to examples/server/public_legacy/theme-playground.css diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public_legacy/theme-polarnight.css similarity index 100% rename from examples/server/public/theme-polarnight.css rename to examples/server/public_legacy/theme-polarnight.css diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public_legacy/theme-snowstorm.css similarity index 100% rename from examples/server/public/theme-snowstorm.css rename to examples/server/public_legacy/theme-snowstorm.css diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f809c46d5a..b8e003be97 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -14,22 +14,13 @@ #define MIMETYPE_JSON "application/json; charset=utf-8" // auto generated files (update with ./deps.sh) -#include "colorthemes.css.hpp" -#include "style.css.hpp" -#include "theme-beeninorder.css.hpp" -#include "theme-ketivah.css.hpp" -#include "theme-mangotango.css.hpp" -#include "theme-playground.css.hpp" -#include "theme-polarnight.css.hpp" -#include "theme-snowstorm.css.hpp" #include "index.html.hpp" -#include "index-new.html.hpp" -#include "index.js.hpp" #include "completion.js.hpp" -#include "system-prompts.js.hpp" -#include "prompt-formats.js.hpp" -#include "json-schema-to-grammar.mjs.hpp" #include "loading.html.hpp" +#include "deps_daisyui.min.css.hpp" +#include "deps_markdown-it.js.hpp" +#include "deps_tailwindcss.js.hpp" +#include "deps_vue.esm-browser.js.hpp" #include #include @@ -43,21 +34,6 @@ #include #include -#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) - -#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) - -#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) - using json = nlohmann::ordered_json; enum stop_type { @@ -68,6 +44,7 @@ enum stop_type { // state diagram: https://github.com/ggerganov/llama.cpp/pull/9283 enum slot_state { SLOT_STATE_IDLE, + SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future SLOT_STATE_PROCESSING_PROMPT, SLOT_STATE_DONE_PROMPT, SLOT_STATE_GENERATING, @@ -79,7 +56,7 @@ enum server_state { }; enum server_task_type { - SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_INFERENCE, SERVER_TASK_TYPE_CANCEL, SERVER_TASK_TYPE_NEXT_RESPONSE, SERVER_TASK_TYPE_METRICS, @@ -89,21 +66,22 @@ enum server_task_type { SERVER_TASK_TYPE_SET_LORA, }; -enum server_task_cmpl_type { - SERVER_TASK_CMPL_TYPE_NORMAL, - SERVER_TASK_CMPL_TYPE_EMBEDDING, - SERVER_TASK_CMPL_TYPE_RERANK, - SERVER_TASK_CMPL_TYPE_INFILL, +enum server_task_inf_type { + SERVER_TASK_INF_TYPE_COMPLETION, + SERVER_TASK_INF_TYPE_EMBEDDING, + SERVER_TASK_INF_TYPE_RERANK, + SERVER_TASK_INF_TYPE_INFILL, }; struct server_task { int id = -1; // to be filled by server_queue int id_target = -1; // used by SERVER_TASK_TYPE_CANCEL + llama_tokens prompt_tokens; server_task_type type; json data; - server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; // utility function static std::unordered_set get_list_id(const std::vector & tasks) { @@ -124,6 +102,12 @@ struct server_task_result { bool error; }; +struct server_static_file { + const unsigned char * data; + unsigned int size; + const char * mime_type; +}; + struct slot_params { bool stream = true; bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt @@ -131,14 +115,12 @@ struct slot_params { int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half int32_t n_predict = -1; // new tokens to predict + int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters int64_t t_max_prompt_ms = -1; // TODO: implement int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit std::vector antiprompt; - - json input_prefix; - json input_suffix; }; struct server_slot { @@ -163,19 +145,20 @@ struct server_slot { int32_t i_batch = -1; int32_t n_predict = -1; // TODO: disambiguate from params.n_predict + // n_prompt_tokens may not be equal to prompt_tokens.size(), because prompt maybe truncated int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; - json prompt; // can be either a string, array of strings or array of token ids + // input prompt tokens + llama_tokens prompt_tokens; - // when a task is submitted, we first tokenize the prompt and store it here - std::vector prompt_tokens; + size_t last_nl_pos = 0; std::string generated_text; - std::vector cache_tokens; + llama_tokens cache_tokens; std::vector generated_token_probs; - server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; bool has_next_token = true; bool has_new_line = false; @@ -213,6 +196,7 @@ struct server_slot { SLT_DBG(*this, "%s", "\n"); n_prompt_tokens = 0; + last_nl_pos = 0; generated_text = ""; has_new_line = false; truncated = false; @@ -223,7 +207,7 @@ struct server_slot { n_past = 0; n_sent_text = 0; n_sent_token_probs = 0; - cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + inf_type = SERVER_TASK_INF_TYPE_COMPLETION; generated_token_probs.clear(); } @@ -260,6 +244,7 @@ struct server_slot { if (is_processing()) { SLT_INF(*this, "stop processing: n_past = %d, truncated = %d\n", n_past, truncated); + t_last_used = ggml_time_us(); t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; state = SLOT_STATE_IDLE; callback_on_release(id); @@ -390,8 +375,8 @@ struct server_queue { std::condition_variable condition_tasks; // callback functions - std::function callback_new_task; - std::function callback_update_slots; + std::function callback_new_task; + std::function callback_update_slots; // Add a new task to the end of the queue int post(server_task task, bool front = false) { @@ -443,7 +428,7 @@ struct server_queue { } // Register function to process a new task - void on_new_task(std::function callback) { + void on_new_task(std::function callback) { callback_new_task = std::move(callback); } @@ -493,7 +478,7 @@ struct server_queue { lock.unlock(); QUE_DBG("processing task, id = %d\n", task.id); - callback_new_task(task); + callback_new_task(std::move(task)); } // all tasks in the current loop is processed, slots data is now ready @@ -656,17 +641,12 @@ struct server_context { bool load_model(const common_params & params_) { params = params_; - // reserve one extra sequence (seq_id == 0) for extra features - params.n_parallel += 1; - common_init_result llama_init = common_init_from_params(params); model = llama_init.model; ctx = llama_init.context; loras = llama_init.lora_adapters; - params.n_parallel -= 1; // but be sneaky about it - if (model == nullptr) { SRV_ERR("failed to load model, '%s'\n", params.model.c_str()); return false; @@ -681,11 +661,16 @@ struct server_context { } bool validate_model_chat_template() const { - llama_chat_message chat[] = {{"user", "test"}}; - - const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0); - - return res > 0; + std::vector model_template(2048, 0); // longest known template is about 1200 bytes + std::string template_key = "tokenizer.chat_template"; + int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); + if (res >= 0) { + llama_chat_message chat[] = {{"user", "test"}}; + std::string tmpl = std::string(model_template.data(), model_template.size()); + int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0); + return chat_res > 0; + } + return false; } void init() { @@ -728,42 +713,6 @@ struct server_context { metrics.init(); } - std::vector tokenize(const json & json_prompt, bool add_special, bool parse_special) const { - // If `add_bos` is true, we only add BOS, when json_prompt is a string, - // or the first element of the json_prompt array is a string. - std::vector prompt_tokens; - - if (json_prompt.is_array()) { - bool first = true; - for (const auto & p : json_prompt) { - if (p.is_string()) { - auto s = p.template get(); - - std::vector p; - if (first) { - p = common_tokenize(ctx, s, add_special, parse_special); - first = false; - } else { - p = common_tokenize(ctx, s, false, parse_special); - } - - prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } else { - if (first) { - first = false; - } - - prompt_tokens.push_back(p.template get()); - } - } - } else { - auto s = json_prompt.template get(); - prompt_tokens = common_tokenize(ctx, s, add_special, parse_special); - } - - return prompt_tokens; - } - server_slot * get_slot_by_id(int id) { for (server_slot & slot : slots) { if (slot.id == id) { @@ -774,12 +723,12 @@ struct server_context { return nullptr; } - server_slot * get_available_slot(const std::string & prompt) { + server_slot * get_available_slot(const server_task & task) { server_slot * ret = nullptr; // find the slot that has at least n% prompt similarity - if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) { - int max_lcp_len = 0; + if (ret == nullptr && slot_prompt_similarity != 0.0f) { + int lcs_len = 0; float similarity = 0; for (server_slot & slot : slots) { @@ -788,32 +737,27 @@ struct server_context { continue; } - // skip the slot if it does not contains prompt - if (!slot.prompt.is_string()) { + // skip the slot if it does not contains cached tokens + if (slot.cache_tokens.empty()) { continue; } - // current slot's prompt - std::string slot_prompt = slot.prompt.get(); + // length of the Longest Common Subsequence between the current slot's prompt and the input prompt + int cur_lcs_len = longest_common_subsequence(slot.cache_tokens, task.prompt_tokens); - // length of the current slot's prompt - int slot_prompt_len = slot_prompt.size(); - - // length of the Longest Common Prefix between the current slot's prompt and the input prompt - int lcp_len = common_part(slot_prompt, prompt); - - // fraction of the common substring length compared to the current slot's prompt length - similarity = static_cast(lcp_len) / slot_prompt_len; + // fraction of the common subsequence length compared to the current slot's prompt length + float cur_similarity = static_cast(cur_lcs_len) / static_cast(slot.cache_tokens.size()); // select the current slot if the criteria match - if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) { - max_lcp_len = lcp_len; + if (cur_lcs_len > lcs_len && cur_similarity > slot_prompt_similarity) { + lcs_len = cur_lcs_len; + similarity = cur_similarity; ret = &slot; } } if (ret != nullptr) { - SLT_DBG(*ret, "selected slot by lcp similarity, max_lcp_len = %d, similarity = %f\n", max_lcp_len, similarity); + SLT_DBG(*ret, "selected slot by lcs similarity, lcs_len = %d, similarity = %f\n", lcs_len, similarity); } } @@ -855,32 +799,57 @@ struct server_context { slot.oaicompat_model = ""; } - slot.params.stream = json_value(data, "stream", false); - slot.params.cache_prompt = json_value(data, "cache_prompt", false); - slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); - slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); - slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); - slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); - slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); - slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); - slot.sparams.seed = json_value(data, "seed", default_sparams.seed); - slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); - slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); - //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement - slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms); + slot.params.stream = json_value(data, "stream", false); + slot.params.cache_prompt = json_value(data, "cache_prompt", false); + slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); + slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent); + slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); + slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); + slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); + slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot.sparams.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier); + slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base); + slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length); + slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n); + slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep); + slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); + slot.sparams.seed = json_value(data, "seed", default_sparams.seed); + slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); + //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement + slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms); + + if (slot.sparams.dry_base < 1.0f) + { + slot.sparams.dry_base = default_sparams.dry_base; + } + + // sequence breakers for DRY + { + // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format + // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 + + if (data.contains("dry_sequence_breakers")) { + slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (slot.sparams.dry_sequence_breakers.empty()) { + send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST); + return false; + } + } + } // process "json_schema" and "grammar" if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) { @@ -905,39 +874,6 @@ struct server_context { SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.n_predict, slot.n_predict); } - // infill - slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); - slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); - - // get prompt - if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) { - const auto & prompt = data.find("prompt"); - if (prompt == data.end()) { - send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); - return false; - } - - if ((prompt->is_string()) || - (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) || - (prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) { - slot.prompt = *prompt; - } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) { - slot.prompt = prompt->at(0); - } else if (prompt->is_array() && prompt->size() > 1) { - // array of strings - for (const auto & el : *prompt) { - if (!el.is_string()) { - send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST); - return false; - } - } - slot.prompt = *prompt; - } else { - send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST); - return false; - } - } - { slot.sparams.logit_bias.clear(); @@ -991,14 +927,22 @@ struct server_context { { const auto & samplers = data.find("samplers"); - if (samplers != data.end() && samplers->is_array()) { - std::vector sampler_names; - for (const auto & name : *samplers) { - if (name.is_string()) { - sampler_names.emplace_back(name); + if (samplers != data.end()) { + if (samplers->is_array()) { + std::vector sampler_names; + for (const auto & name : *samplers) { + if (name.is_string()) { + sampler_names.emplace_back(name); + } } + slot.sparams.samplers = common_sampler_types_from_names(sampler_names, false); + } else if (samplers->is_string()){ + std::string sampler_string; + for (const auto & name : *samplers) { + sampler_string += name; + } + slot.sparams.samplers = common_sampler_types_from_chars(sampler_string); } - slot.sparams.samplers = common_sampler_types_from_names(sampler_names, false); } else { slot.sparams.samplers = default_sparams.samplers; } @@ -1017,8 +961,7 @@ struct server_context { } } - slot.state = SLOT_STATE_PROCESSING_PROMPT; - slot.prompt_tokens.clear(); + slot.state = SLOT_STATE_STARTED; SLT_INF(slot, "%s", "processing task\n"); @@ -1068,22 +1011,21 @@ struct server_context { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); - bool is_stop_full = false; + bool send_text = true; size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); if (stop_pos != std::string::npos) { - is_stop_full = true; slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); - } else { - is_stop_full = false; + } else if (slot.has_next_token) { stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); + send_text = stop_pos == std::string::npos; } // check if there is any token to predict - if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { + if (send_text) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); @@ -1108,13 +1050,48 @@ struct server_context { SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict); } - // if we have already seen a new line, we stop after a certain time limit - if (slot.has_new_line && slot.params.t_max_predict_ms > 0 && - (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { - slot.stopped_limit = true; - slot.has_next_token = false; + if (slot.has_new_line) { + // if we have already seen a new line, we stop after a certain time limit + if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { + slot.stopped_limit = true; + slot.has_next_token = false; - SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + } + + // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent + if (slot.params.n_indent > 0) { + // check the current indentation + // TODO: improve by not doing it more than once for each new line + if (slot.last_nl_pos > 0) { + size_t pos = slot.last_nl_pos; + + int n_indent = 0; + while (pos < slot.generated_text.size() && (slot.generated_text[pos] == ' ' || slot.generated_text[pos] == '\t')) { + n_indent++; + pos++; + } + + if (pos < slot.generated_text.size() && n_indent < slot.params.n_indent) { + slot.stopped_limit = true; + slot.has_next_token = false; + + // cut the last line + slot.generated_text.erase(pos, std::string::npos); + + SLT_DBG(slot, "stopped by indentation limit, n_decoded = %d, n_indent = %d\n", slot.n_decoded, n_indent); + } + } + + // find the next new line + { + const size_t pos = slot.generated_text.find('\n', slot.last_nl_pos); + + if (pos != std::string::npos) { + slot.last_nl_pos = pos + 1; + } + } + } } // check if there is a new line in the generated text @@ -1176,12 +1153,18 @@ struct server_context { {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, - {"tfs_z", slot.sparams.tfs_z}, + {"xtc_probability", slot.sparams.xtc_probability}, + {"xtc_threshold", slot.sparams.xtc_threshold}, {"typical_p", slot.sparams.typ_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, {"repeat_penalty", slot.sparams.penalty_repeat}, {"presence_penalty", slot.sparams.penalty_present}, {"frequency_penalty", slot.sparams.penalty_freq}, + {"dry_multiplier", slot.sparams.dry_multiplier}, + {"dry_base", slot.sparams.dry_base}, + {"dry_allowed_length", slot.sparams.dry_allowed_length}, + {"dry_penalty_last_n", slot.sparams.dry_penalty_last_n}, + {"dry_sequence_breakers", slot.sparams.dry_sequence_breakers}, {"mirostat", slot.sparams.mirostat}, {"mirostat_tau", slot.sparams.mirostat_tau}, {"mirostat_eta", slot.sparams.mirostat_eta}, @@ -1234,7 +1217,7 @@ struct server_context { }; if (slot.sparams.n_probs > 0) { - const std::vector to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); + const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); @@ -1270,7 +1253,7 @@ struct server_context { {"tokens_predicted", slot.n_decoded}, {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, - {"prompt", slot.prompt}, + {"prompt", common_detokenize(ctx, slot.prompt_tokens)}, {"has_new_line", slot.has_new_line}, {"truncated", slot.truncated}, {"stopped_eos", slot.stopped_eos}, @@ -1285,7 +1268,7 @@ struct server_context { if (slot.sparams.n_probs > 0) { std::vector probs; if (!slot.params.stream && slot.stopped_word) { - const std::vector stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); + const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); probs = std::vector( @@ -1310,16 +1293,16 @@ struct server_context { void send_embedding(const server_slot & slot, const llama_batch & batch) { server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + res.id = slot.id_task; + res.error = false; + res.stop = true; const int n_embd = llama_n_embd(model); std::vector embd_res(n_embd, 0.0f); for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { continue; } @@ -1354,12 +1337,12 @@ struct server_context { void send_rerank(const server_slot & slot, const llama_batch & batch) { server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + res.id = slot.id_task; + res.error = false; + res.stop = true; for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { continue; } @@ -1394,19 +1377,17 @@ struct server_context { // Functions to create new task(s) and receive result(s) // - std::vector create_tasks_cmpl(json data, server_task_cmpl_type cmpl_type) { + // break the input "prompt" into multiple tasks if needed, then format and tokenize the input prompt(s) + std::vector create_tasks_inference(json data, server_task_inf_type inf_type) { std::vector tasks; - auto create_task = [&](json & task_data, bool replace_prompt, json prompt) { + auto create_task = [&](json & task_data, llama_tokens & prompt_tokens) { + SRV_DBG("create task, n_tokens = %d\n", (int) prompt_tokens.size()); server_task task; - task.id = queue_tasks.get_new_id(); - task.cmpl_type = cmpl_type; - task.type = SERVER_TASK_TYPE_COMPLETION; - if (replace_prompt) { - task.data = task_data; - task.data["prompt"] = std::move(prompt); - } else { - task.data = std::move(task_data); - } + task.id = queue_tasks.get_new_id(); + task.inf_type = inf_type; + task.type = SERVER_TASK_TYPE_INFERENCE; + task.data = task_data; + task.prompt_tokens = std::move(prompt_tokens); tasks.push_back(std::move(task)); }; @@ -1415,41 +1396,49 @@ struct server_context { throw std::runtime_error(error_msg); } - json prompt = data.at("prompt"); - - // if the prompt is a singleton (i.e. a string or a list of tokens), we only need to create single task - if (prompt.is_string() || json_is_array_of_numbers(prompt)) { - data["index"] = 0; - create_task(data, false, nullptr); - } else if (prompt.is_array()) { - // otherwise, it's a multiple-prompt task, we break it into smaller tasks - std::vector prompts = prompt; - if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { - // prompts[0] is the question - // the rest are the answers/documents - SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) prompts.size() - 1); - for (size_t i = 1; i < prompts.size(); i++) { - json qd; - qd.push_back(prompts[0]); - qd.push_back(prompts[i]); - data["index"] = i - 1; - create_task(data, true, qd); - } - } else { - SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) prompts.size()); - for (size_t i = 0; i < prompts.size(); i++) { - const auto & e = prompts[i]; - if (e.is_string() || json_is_array_of_numbers(e)) { + // because llama_tokenize api is thread-safe, we can tokenize the prompt from HTTP thread + bool add_special = inf_type != SERVER_TASK_INF_TYPE_RERANK && inf_type != SERVER_TASK_INF_TYPE_INFILL; + std::vector tokenized_prompts = tokenize_input_prompts(ctx, data.at("prompt"), add_special, true); + switch (inf_type) { + case SERVER_TASK_INF_TYPE_RERANK: + { + // prompts[0] is the question + // the rest are the answers/documents + GGML_ASSERT(tokenized_prompts.size() > 1); + SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) tokenized_prompts.size() - 1); + for (size_t i = 1; i < tokenized_prompts.size(); i++) { + data["index"] = i - 1; + auto tokens = format_rerank(model, tokenized_prompts[0], tokenized_prompts[i]); + create_task(data, tokens); + } + } break; + case SERVER_TASK_INF_TYPE_INFILL: + { + SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { data["index"] = i; - create_task(data, true, e); - } else { - throw std::runtime_error(error_msg); + auto tokens = format_infill( + ctx, + data.at("input_prefix"), + data.at("input_suffix"), + data.at("input_extra"), + params.n_batch, + params.n_predict, + slots[0].n_ctx, // TODO: there should be a better way + params.spm_infill, + tokenized_prompts[i] + ); + create_task(data, tokens); + } + } break; + default: + { + SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + data["index"] = i; + create_task(data, tokenized_prompts[i]); } } - } - } else { - // invalid case - throw std::runtime_error(error_msg); } return tasks; @@ -1471,7 +1460,7 @@ struct server_context { queue_tasks.post(cancel_tasks, true); } - // receive the results from task(s) created by create_tasks_cmpl + // receive the results from task(s) created by create_tasks_inference void receive_cmpl_results( const std::unordered_set & id_tasks, const std::function&)> & result_handler, @@ -1495,7 +1484,7 @@ struct server_context { result_handler(results); } - // receive the results from task(s) created by create_tasks_cmpl, in stream mode + // receive the results from task(s) created by create_tasks_inference, in stream mode void receive_cmpl_results_stream( const std::unordered_set & id_tasks, const std::function & result_handler, const @@ -1526,24 +1515,13 @@ struct server_context { // Functions to process the task // - void process_single_task(const server_task & task) { + void process_single_task(server_task task) { switch (task.type) { - case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFERENCE: { const int id_slot = json_value(task.data, "id_slot", -1); - server_slot * slot; - - if (id_slot != -1) { - slot = get_slot_by_id(id_slot); - } else { - std::string prompt; - if (task.data.contains("prompt") && task.data.at("prompt").is_string()) { - prompt = json_value(task.data, "prompt", std::string()); - } - - slot = get_available_slot(prompt); - } + server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); if (slot == nullptr) { // if no slot is available, we defer this task for processing later @@ -1560,9 +1538,10 @@ struct server_context { slot->reset(); - slot->id_task = task.id; - slot->cmpl_type = task.cmpl_type; - slot->index = json_value(task.data, "index", 0); + slot->id_task = task.id; + slot->inf_type = task.inf_type; + slot->index = json_value(task.data, "index", 0); + slot->prompt_tokens = std::move(task.prompt_tokens); if (!launch_slot_with_task(*slot, task)) { SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id); @@ -1592,11 +1571,11 @@ struct server_context { for (server_slot & slot : slots) { json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["id_task"] = slot.id_task; - slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; - slot_data["next_token"] = { + slot_data["id"] = slot.id; + slot_data["id_task"] = slot.id_task; + slot_data["is_processing"] = slot.is_processing(); + slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens); + slot_data["next_token"] = { {"has_next_token", slot.has_next_token}, {"has_new_line", slot.has_new_line}, {"n_remain", slot.n_remaining}, @@ -1607,10 +1586,10 @@ struct server_context { {"stopping_word", slot.stopping_word}, }; - if (slot_data["state"] == SLOT_STATE_IDLE) { - n_idle_slots++; - } else { + if (slot.is_processing()) { n_processing_slots++; + } else { + n_idle_slots++; } slots_data.push_back(slot_data); @@ -1672,7 +1651,7 @@ struct server_context { std::string filename = task.data.at("filename"); std::string filepath = task.data.at("filepath"); - const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); + const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count); const int64_t t_end = ggml_time_us(); const double t_save_ms = (t_end - t_start) / 1000.0; @@ -1714,7 +1693,7 @@ struct server_context { slot->cache_tokens.resize(slot->n_ctx); size_t token_count = 0; - size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); + size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); if (nread == 0) { slot->cache_tokens.resize(0); send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); @@ -1722,9 +1701,6 @@ struct server_context { } slot->cache_tokens.resize(token_count); - // TODO: maybe detokenize the slot->cache_tokens instead? - slot->prompt = string_format("[restored %d tokens from file]", (int) token_count); - const int64_t t_end = ggml_time_us(); const double t_restore_ms = (t_end - t_start) / 1000.0; @@ -1760,7 +1736,7 @@ struct server_context { // Erase token cache const size_t n_erased = slot->cache_tokens.size(); - llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1); + llama_kv_cache_seq_rm(ctx, slot->id, -1, -1); slot->cache_tokens.clear(); server_task_result result; @@ -1837,8 +1813,8 @@ struct server_context { SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); - llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); - llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past, -n_discard); if (slot.params.cache_prompt) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1865,7 +1841,7 @@ struct server_context { slot.i_batch = batch.n_tokens; - common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true); + common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true); slot.n_past += 1; @@ -1891,80 +1867,19 @@ struct server_context { if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { // this slot still has a prompt to be processed - if (slot.state == SLOT_STATE_PROCESSING_PROMPT) { + if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { auto & prompt_tokens = slot.prompt_tokens; - // we haven't tokenized the prompt yet - do it now: - if (prompt_tokens.empty()) { - SLT_INF(slot, "tokenizing prompt, len = %d\n", (int) slot.prompt.size()); - + // TODO: maybe move branch to outside of this loop in the future + if (slot.state == SLOT_STATE_STARTED) { slot.t_start_process_prompt = ggml_time_us(); slot.t_start_generation = 0; - switch (slot.cmpl_type) { - case SERVER_TASK_CMPL_TYPE_NORMAL: - case SERVER_TASK_CMPL_TYPE_EMBEDDING: - { - prompt_tokens = tokenize(slot.prompt, llama_add_bos_token(model), true); - } break; - case SERVER_TASK_CMPL_TYPE_RERANK: - { - // require slot.prompt to be array of 2 strings - if (!slot.prompt.is_array() || slot.prompt.size() != 2) { - SLT_ERR(slot, "%s", "invalid prompt for rerank task\n"); - slot.release(); - send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST); - continue; - } - - // prompt: [BOS]query[EOS][SEP]doc[EOS] - prompt_tokens.clear(); - prompt_tokens.push_back(llama_token_bos(model)); - { - const auto part = tokenize(slot.prompt[0], false, false); - prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end()); - } - prompt_tokens.push_back(llama_token_eos(model)); - prompt_tokens.push_back(llama_token_sep(model)); - { - const auto part = tokenize(slot.prompt[1], false, false); - prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end()); - } - prompt_tokens.push_back(llama_token_eos(model)); - } break; - case SERVER_TASK_CMPL_TYPE_INFILL: - { - auto prefix_tokens = tokenize(slot.params.input_prefix, false, false); - auto suffix_tokens = tokenize(slot.params.input_suffix, false, false); - - // for now pick context to fit in a single batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_suffix_take = std::min(suffix_tokens.size(), n_batch/4); - const int n_prefix_take = std::min(prefix_tokens.size(), (n_batch - 3) - n_suffix_take); - - prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take); - suffix_tokens.resize(n_suffix_take); - - prefix_tokens.insert(prefix_tokens.begin(), llama_token_fim_pre(model)); - suffix_tokens.insert(suffix_tokens.begin(), llama_token_fim_suf(model)); - - auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens; - auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens; - - if (llama_add_bos_token(model)) { - embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); - } - - embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - embd_inp.push_back(llama_token_fim_mid(model)); - - prompt_tokens = std::move(embd_inp); - } break; - } - slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); + slot.state = SLOT_STATE_PROCESSING_PROMPT; - SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); + SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); // print prompt tokens (for debugging) if (1) { @@ -1989,13 +1904,18 @@ struct server_context { continue; } - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { - // this prompt is too large to process - discard it + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { if (slot.n_prompt_tokens > n_ubatch) { slot.release(); send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); continue; } + + if (slot.n_prompt_tokens > slot.n_ctx) { + slot.release(); + send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_SERVER); + continue; + } } else { if (!params.ctx_shift) { // if context shift is disabled, we make sure prompt size is smaller than KV size @@ -2012,14 +1932,14 @@ struct server_context { } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - // if input prompt is too big, truncate it (if group attention self-extend is disabled) + // if input prompt is too big, truncate it if (slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - std::vector new_tokens( + llama_tokens new_tokens( prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); @@ -2038,15 +1958,52 @@ struct server_context { GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } - common_sampler_reset(slot.smpl); - if (slot.params.cache_prompt) { // reuse any previously computed tokens that are common with the new prompt - slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens); - // push the prompt into the sampling context (do not apply grammar) - for (int i = 0; i < slot.n_past; ++i) { - common_sampler_accept(slot.smpl, slot.cache_tokens[i], false); + // reuse chunks from the cached prompt by shifting their KV cache in the new position + if (params.n_cache_reuse > 0) { + size_t head_c = slot.n_past; // cache + size_t head_p = slot.n_past; // current prompt + + SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params.n_cache_reuse, slot.n_past); + + while (head_c < slot.cache_tokens.size() && + head_p < prompt_tokens.size()) { + + size_t n_match = 0; + while (head_c + n_match < slot.cache_tokens.size() && + head_p + n_match < prompt_tokens.size() && + slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { + + n_match++; + } + + if (n_match >= (size_t) params.n_cache_reuse) { + SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match); + //for (size_t i = head_p; i < head_p + n_match; i++) { + // SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + //} + + const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; + + llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c); + llama_kv_cache_seq_add(ctx, slot.id, head_c, -1, kv_shift); + + for (size_t i = 0; i < n_match; i++) { + slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; + slot.n_past++; + } + + head_c += n_match; + head_p += n_match; + } else { + head_c += 1; + } + } + + SLT_DBG(slot, "after context reuse, new slot.n_past = %d\n", slot.n_past); } } } @@ -2062,7 +2019,7 @@ struct server_context { } // non-causal tasks require to fit the entire prompt in the physical batch - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { // cannot fit the prompt in the current batch - will try next iter if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { continue; @@ -2071,8 +2028,8 @@ struct server_context { // check that we are in the right batch_type, if not defer the slot const bool slot_type = - slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || - slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK ? 1 : 0; + slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || + slot.inf_type == SERVER_TASK_INF_TYPE_RERANK ? 1 : 0; if (batch_type == -1) { batch_type = slot_type; @@ -2081,14 +2038,12 @@ struct server_context { } // keep only the common part - if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) { + if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) { // could not partially delete (likely using a non-Transformer model) - llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); + llama_kv_cache_seq_rm(ctx, slot.id, -1, -1); // there is no common part left slot.n_past = 0; - - common_sampler_reset(slot.smpl); } SLT_INF(slot, "kv cache rm [%d, end)\n", slot.n_past); @@ -2098,7 +2053,7 @@ struct server_context { // add prompt tokens for processing in the current batch while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { - common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false); + common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -2116,6 +2071,13 @@ struct server_context { GGML_ASSERT(batch.n_tokens > 0); + common_sampler_reset(slot.smpl); + + // Process all prompt tokens through sampler system + for (int i = 0; i < slot.n_prompt_tokens; ++i) { + common_sampler_accept(slot.smpl, prompt_tokens[i], false); + } + // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; @@ -2154,7 +2116,6 @@ struct server_context { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); @@ -2186,7 +2147,7 @@ struct server_context { } if (slot.state == SLOT_STATE_DONE_PROMPT) { - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING) { // prompt evaluated for embedding send_embedding(slot, batch_view); slot.release(); @@ -2194,7 +2155,7 @@ struct server_context { continue; // continue loop of slots } - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { send_rerank(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -2312,6 +2273,16 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); LOG_INF("\n"); + // static files + std::map static_files = { + { "/", { index_html, index_html_len, "text/html; charset=utf-8" }}, + { "/completion.js", { completion_js, completion_js_len, "text/javascript; charset=utf-8" }}, + { "/deps_daisyui.min.css", { deps_daisyui_min_css, deps_daisyui_min_css_len, "text/css; charset=utf-8" }}, + { "/deps_markdown-it.js", { deps_markdown_it_js, deps_markdown_it_js_len, "text/javascript; charset=utf-8" }}, + { "/deps_tailwindcss.js", { deps_tailwindcss_js, deps_tailwindcss_js_len, "text/javascript; charset=utf-8" }}, + { "/deps_vue.esm-browser.js", { deps_vue_esm_browser_js, deps_vue_esm_browser_js_len, "text/javascript; charset=utf-8" }}, + }; + std::unique_ptr svr; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT if (params.ssl_file_key != "" && params.ssl_file_cert != "") { @@ -2334,16 +2305,6 @@ int main(int argc, char ** argv) { std::atomic state{SERVER_STATE_LOADING_MODEL}; svr->set_default_headers({{"Server", "llama.cpp"}}); - - // CORS preflight - svr->Options(R"(.*)", [](const httplib::Request &, httplib::Response & res) { - // Access-Control-Allow-Origin is already set by middleware - res.set_header("Access-Control-Allow-Credentials", "true"); - res.set_header("Access-Control-Allow-Methods", "POST"); - res.set_header("Access-Control-Allow-Headers", "*"); - return res.set_content("", "text/html"); // blank response, no data - }); - svr->set_logger(log_server_request); auto res_error = [](httplib::Response & res, const json & error_data) { @@ -2402,7 +2363,7 @@ int main(int argc, char ** argv) { // Middlewares // - auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { + auto middleware_validate_api_key = [¶ms, &res_error, &static_files](const httplib::Request & req, httplib::Response & res) { static const std::unordered_set public_endpoints = { "/health", "/models", @@ -2414,8 +2375,8 @@ int main(int argc, char ** argv) { return true; } - // If path is public, skip validation - if (public_endpoints.find(req.path) != public_endpoints.end()) { + // If path is public or is static file, skip validation + if (public_endpoints.find(req.path) != public_endpoints.end() || static_files.find(req.path) != static_files.end()) { return true; } @@ -2441,7 +2402,7 @@ int main(int argc, char ** argv) { auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); if (current_state == SERVER_STATE_LOADING_MODEL) { - auto tmp = string_split(req.path, '.'); + auto tmp = string_split(req.path, '.'); if (req.path == "/" || tmp.back() == "html") { res.set_content(reinterpret_cast(loading_html), loading_html_len, "text/html; charset=utf-8"); res.status = 503; @@ -2456,6 +2417,14 @@ int main(int argc, char ** argv) { // register server middlewares svr->set_pre_routing_handler([&middleware_validate_api_key, &middleware_server_state](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + // If this is OPTIONS request, skip validation because browsers don't include Authorization header + if (req.method == "OPTIONS") { + res.set_header("Access-Control-Allow-Credentials", "true"); + res.set_header("Access-Control-Allow-Methods", "GET, POST"); + res.set_header("Access-Control-Allow-Headers", "*"); + res.set_content("", "text/html"); // blank response, no data + return httplib::Server::HandlerResponse::Handled; // skip further processing + } if (!middleware_server_state(req, res)) { return httplib::Server::HandlerResponse::Handled; } @@ -2748,13 +2717,13 @@ int main(int argc, char ** argv) { res_ok(res, {{ "success", true }}); }; - const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) { - if (ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) { + if (ctx_server.params.embedding) { + res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } - std::vector tasks = ctx_server.create_tasks_cmpl(data, cmpl_type); + std::vector tasks = ctx_server.create_tasks_inference(data, inf_type); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -2800,10 +2769,11 @@ int main(int argc, char ** argv) { const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) { json data = json::parse(req.body); - return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res); + return handle_completions_generic(SERVER_TASK_INF_TYPE_COMPLETION, data, res); }; const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + // check model compatibility std::string err; if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) { err += "prefix token is missing. "; @@ -2814,26 +2784,54 @@ int main(int argc, char ** argv) { if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) { err += "middle token is missing. "; } - if (!err.empty()) { res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED)); return; } json data = json::parse(req.body); - return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res); + + // validate input + if (!data.contains("input_prefix")) { + res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (!data.contains("input_suffix")) { + res_error(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (data.contains("input_extra") && !data.at("input_extra").is_array()) { + res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST)); + return; + } + json input_extra = json_value(data, "input_extra", json::array()); + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + if (!chunk.contains("text") || !chunk.at("text").is_string()) { + res_error(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST)); + return; + } + // filename is optional + if (chunk.contains("filename") && !chunk.at("filename").is_string()) { + res_error(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + data["input_extra"] = input_extra; // default to empty array if it's not exist + + return handle_completions_generic(SERVER_TASK_INF_TYPE_INFILL, data, res); }; // TODO: maybe merge this function with "handle_completions_generic" const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) { - if (ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + if (ctx_server.params.embedding) { + res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); - std::vector tasks = ctx_server.create_tasks_cmpl(data, SERVER_TASK_CMPL_TYPE_NORMAL); + std::vector tasks = ctx_server.create_tasks_inference(data, SERVER_TASK_INF_TYPE_COMPLETION); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -2906,7 +2904,7 @@ int main(int argc, char ** argv) { const bool add_special = json_value(body, "add_special", false); const bool with_pieces = json_value(body, "with_pieces", false); - std::vector tokens = ctx_server.tokenize(body.at("content"), add_special, true); + llama_tokens tokens = tokenize_mixed(ctx_server.ctx, body.at("content"), add_special, true); if (with_pieces) { for (const auto& token : tokens) { @@ -2943,7 +2941,7 @@ int main(int argc, char ** argv) { std::string content; if (body.count("tokens") != 0) { - const std::vector tokens = body.at("tokens"); + const llama_tokens tokens = body.at("tokens"); content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); } @@ -2952,11 +2950,6 @@ int main(int argc, char ** argv) { }; const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - // TODO: somehow clean up this checks in the future - if (!ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings` and without `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); - return; - } const json body = json::parse(req.body); bool is_openai = false; @@ -2977,7 +2970,7 @@ int main(int argc, char ** argv) { json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_EMBEDDING); + std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_EMBEDDING); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -3008,10 +3001,11 @@ int main(int argc, char ** argv) { }; const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - if (!ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + if (!ctx_server.params.reranking || ctx_server.params.embedding) { + res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED)); return; } + const json body = json::parse(req.body); // TODO: implement @@ -3054,7 +3048,7 @@ int main(int argc, char ** argv) { json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK); + std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_RERANK); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -3126,13 +3120,6 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK }; - auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { - return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(content), len, mime_type); - return false; - }; - }; - // // Router // @@ -3140,33 +3127,20 @@ int main(int argc, char ** argv) { // register static assets routes if (!params.public_path.empty()) { // Set the base directory for serving static files - svr->set_base_dir(params.public_path); - } - - if (!params.api_keys.empty()) { - // for now, if API key is set, web UI is unusable - svr->Get("/", [&](const httplib::Request &, httplib::Response & res) { - return res.set_content("Web UI is disabled because API key is set.", "text/html; charset=utf-8"); - }); + bool is_found = svr->set_mount_point("/", params.public_path); + if (!is_found) { + LOG_ERR("%s: static assets path not found: %s\n", __func__, params.public_path.c_str()); + return 1; + } } else { // using embedded static files - svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); - svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); - svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); - svr->Get("/json-schema-to-grammar.mjs", handle_static_file(json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); - - // add new-ui files - svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8")); - svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8")); - svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8")); - svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8")); - svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8")); + for (const auto & it : static_files) { + const server_static_file & static_file = it.second; + svr->Get(it.first.c_str(), [&static_file](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(static_file.data), static_file.size, static_file.mime_type); + return false; + }); + } } // register API routes @@ -3257,6 +3231,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.on_new_task(std::bind( &server_context::process_single_task, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_update_slots(std::bind( &server_context::update_slots, &ctx_server)); @@ -3264,7 +3239,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.terminate(); }; - LOG_INF("%s: server is listening on %s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); + LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); ctx_server.queue_tasks.start_loop(); diff --git a/examples/server/tests/features/infill.feature b/examples/server/tests/features/infill.feature new file mode 100644 index 0000000000..a0bbfef777 --- /dev/null +++ b/examples/server/tests/features/infill.feature @@ -0,0 +1,36 @@ +@llama.cpp +@infill +Feature: llama.cpp server + + # The current model is made by adding FIM tokens to the existing stories260K + # We may want to use a better model in the future, maybe something like SmolLM 360M + + Background: Server startup + Given a server listening on localhost:8080 + And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models + And a model file test-model-infill.gguf + And a model alias tinyllama-infill + And 42 as server seed + And 1024 as batch size + And 1024 as ubatch size + And 2048 KV cache size + And 64 max tokens to predict + And 0.0 temperature + Then the server is starting + Then the server is healthy + + Scenario: Infill without input_extra + Given a prompt "Complete this" + And an infill input extra none none + And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" + And an infill input suffix "}\n" + And an infill request with no api error + Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird + + Scenario: Infill with input_extra + Given a prompt "Complete this" + And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n" + And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" + And an infill input suffix "}\n" + And an infill request with no api error + Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room" diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index 0a3c5cc775..ef30007c3e 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -64,5 +64,5 @@ Feature: Security | localhost | Access-Control-Allow-Origin | localhost | | web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr | | origin | Access-Control-Allow-Credentials | true | - | web.mydomain.fr | Access-Control-Allow-Methods | POST | + | web.mydomain.fr | Access-Control-Allow-Methods | GET, POST | | web.mydomain.fr | Access-Control-Allow-Headers | * | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 540a2ecd56..687b163f48 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -80,6 +80,11 @@ def step_server_config(context, server_fqdn: str, server_port: str): context.lora_file = None context.disable_ctx_shift = False + # infill + context.infill_input_extra = None + context.infill_input_suffix = '' + context.infill_input_prefix = '' + context.tasks_result = [] context.concurrent_tasks = [] context.prompts = [] @@ -255,13 +260,13 @@ async def step_wait_for_server_status(context, expecting_status: Literal['health async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str): match expected_slot_status_string: case 'idle': - expected_slot_status = 0 + expected_slot_status = False case 'busy': - expected_slot_status = 1 + expected_slot_status = True case _: assert False, "unknown status" - expected_slots = [{'id': slot_id, 'state': expected_slot_status} + expected_slots = [{'id': slot_id, 'is_processing': expected_slot_status} for slot_id in range(context.n_slots)] await request_slots_status(context, expected_slots) @@ -291,6 +296,28 @@ async def step_request_completion(context, api_error: Literal['raised'] | str): assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}" +@step('an infill request with {api_error} api error') +@async_run_until_complete +async def step_request_completion(context, api_error: Literal['raised'] | str): + if api_error != 'no': + raise ValueError(f'api_error={api_error} is not yet implemented') + payload = { + "prompt": context.prompts[0], + "input_suffix": context.infill_input_suffix, + "input_prefix": context.infill_input_prefix, + "n_predict": context.n_predict, + "seed": context.seed, + "temperature": context.temperature, + } + if context.infill_input_extra is not None: + payload['input_extra'] = context.infill_input_extra + async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: + async with session.post(f'{context.base_url}/infill', + json=payload) as response: + assert response.status == 200 + context.tasks_result = [await response.json()] + + @step('{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): context.completion = context.tasks_result.pop() @@ -539,6 +566,25 @@ def step_a_prompt_prompt(context, prompt): context.n_prompts = len(context.prompts) +# TODO: allow this to be repeated +@step('an infill input extra {filename} {text}') +def step_infill_input_extra(context, filename, text): + if filename == 'none': + context.infill_input_extra = None + else: + context.infill_input_extra = [{'filename': filename, 'text': text}] + + +@step('an infill input suffix {text}') +def step_infill_input_suffix(context, text): + context.infill_input_suffix = text + + +@step('an infill input prefix {text}') +def step_infill_input_prefix(context, text): + context.infill_input_prefix = text + + @step('{num_prompts:d} prompts {prompt} with seed {seed:d}') def step_many_prompts(context, num_prompts, prompt, seed): if context.seed is None: @@ -1308,8 +1354,8 @@ async def wait_for_slots_status(context, if status_code == 503 and status_code == expected_http_status_code: return if status_code == 200 and status_code == expected_http_status_code: - n_slots_idle = sum(1 if slot["state"] == 0 else 0 for slot in slots) - n_slots_processing = sum(1 if slot["state"] != 0 else 0 for slot in slots) + n_slots_idle = sum(1 if not slot["is_processing"] else 0 for slot in slots) + n_slots_processing = sum(1 if slot["is_processing"] else 0 for slot in slots) if ((slots_idle is None or slots_idle == n_slots_idle) and (slots_processing is None or slots_processing == n_slots_processing)): return diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html index 8334bcde50..2797c37c96 100644 --- a/examples/server/themes/buttons-top/index.html +++ b/examples/server/themes/buttons-top/index.html @@ -226,7 +226,6 @@ top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -788,7 +787,6 @@
More options
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html index 8361c57749..dbe23c4024 100644 --- a/examples/server/themes/wild/index.html +++ b/examples/server/themes/wild/index.html @@ -229,7 +229,6 @@ top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -791,7 +790,6 @@
More options
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index ad99e95742..c47ed3e47a 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -24,6 +24,22 @@ #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::ordered_json; +using llama_tokens = std::vector; + +#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) + +#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) + +#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) // https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 enum error_type { @@ -52,9 +68,237 @@ static T json_value(const json & body, const std::string & key, const T & defaul } // -// chat template utils +// tokenizer and input processing utils // +static bool json_is_array_of_numbers(const json & data) { + if (data.is_array()) { + for (const auto & e : data) { + if (!e.is_number_integer()) { + return false; + } + } + return true; + } + return false; +} + +// is array having BOTH numbers & strings? +static bool json_is_array_of_mixed_numbers_strings(const json & data) { + bool seen_string = false; + bool seen_number = false; + if (data.is_array()) { + for (const auto & e : data) { + seen_string |= e.is_string(); + seen_number |= e.is_number_integer(); + if (seen_number && seen_string) { + return true; + } + } + } + return false; +} + +/** + * this handles 2 cases: + * - only string, example: "string" + * - mixed string and tokens, example: [12, 34, "string", 56, 78] + */ +static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { + // If `add_bos` is true, we only add BOS, when json_prompt is a string, + // or the first element of the json_prompt array is a string. + llama_tokens prompt_tokens; + + if (json_prompt.is_array()) { + bool first = true; + for (const auto & p : json_prompt) { + if (p.is_string()) { + auto s = p.template get(); + + llama_tokens p; + if (first) { + p = common_tokenize(ctx, s, add_special, parse_special); + first = false; + } else { + p = common_tokenize(ctx, s, false, parse_special); + } + + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); + } else { + if (first) { + first = false; + } + + prompt_tokens.push_back(p.template get()); + } + } + } else { + auto s = json_prompt.template get(); + prompt_tokens = common_tokenize(ctx, s, add_special, parse_special); + } + + return prompt_tokens; +} + +/** + * break the input "prompt" object into multiple prompt if needed, then tokenize them + * this supports these cases: + * - "prompt": "string" + * - "prompt": [12, 34, 56] + * - "prompt": [12, 34, "string", 56, 78] + * and multiple prompts (multi-tasks): + * - "prompt": ["string1", "string2"] + * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]] + */ +static std::vector tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { + std::vector result; + if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { + // string or mixed + result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special)); + } else if (json_is_array_of_numbers(json_prompt)) { + // array of tokens + result.push_back(json_prompt.get()); + } else if (json_prompt.is_array()) { + // array of prompts + result.reserve(json_prompt.size()); + for (const auto & p : json_prompt) { + if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) { + result.push_back(tokenize_mixed(ctx, p, add_special, parse_special)); + } else if (json_is_array_of_numbers(p)) { + // array of tokens + result.push_back(p.get()); + } else { + throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens"); + } + } + } else { + throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts"); + } + return result; +} + +// +// template utils +// + +// format rerank task: [BOS]query[EOS][SEP]doc[EOS] +static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) { + llama_tokens result; + result.reserve(doc.size() + query.size() + 4); + result.push_back(llama_token_bos(model)); + result.insert(result.end(), query.begin(), query.end()); + result.push_back(llama_token_eos(model)); + result.push_back(llama_token_sep(model)); + result.insert(result.end(), doc.begin(), doc.end()); + result.push_back(llama_token_eos(model)); + return result; +} + +// format infill task +static llama_tokens format_infill( + const llama_context * ctx, + const json & input_prefix, + const json & input_suffix, + const json & input_extra, + const int n_batch, + const int n_predict, + const int n_ctx, + const bool spm_infill, + const llama_tokens & tokens_prompt + ) { + // TODO: optimize this block by reducing memory allocations and movement + + // use FIM repo-level pattern: + // ref: https://arxiv.org/pdf/2409.12186 + // + // [FIM_REP]myproject + // [FIM_SEP]filename0 + // extra chunk 0 + // [FIM_SEP]filename1 + // extra chunk 1 + // ... + // [FIM_SEP]filename + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt + // + llama_tokens extra_tokens; + extra_tokens.reserve(n_ctx); + + auto model = llama_get_model(ctx); + auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false); + auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false); + + if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { + // TODO: make project name an input + static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false); + + extra_tokens.push_back(llama_token_fim_rep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); + } + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + const std::string text = json_value(chunk, "text", std::string()); + const std::string filename = json_value(chunk, "filename", std::string("tmp")); + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } else { + // chunk separator in binary form to avoid confusing the AI + static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; + static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false); + + extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); + } + + const auto chunk_tokens = common_tokenize(ctx, text, false, false); + extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); + } + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + // TODO: current filename + static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } + + // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4)); + const int n_suffix_take = std::min(tokens_suffix.size(), std::max(0, (n_batch/4) - (2 + tokens_prompt.size()))); + + SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take)); + + // fill the rest of the context with extra chunks + const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); + + tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); + tokens_suffix.resize(n_suffix_take); + + tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model)); + tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); + tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model)); + + auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; + auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; + + if (llama_add_bos_token(model)) { + embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); + } + + SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); + + // put the extra context before the FIM prefix + embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); + + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + embd_inp.push_back(llama_token_fim_mid(model)); + + return embd_inp; +} + // Format given chat. If tmpl is empty, we take the template from model metadata inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { std::vector chat; @@ -195,18 +439,60 @@ static std::string gen_chatcmplid() { // other common utils // -static size_t common_part(const std::vector & a, const std::vector & b) { +static size_t longest_common_prefix(const llama_tokens & a, const llama_tokens & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} return i; } -static size_t common_part(const std::string & a, const std::string & b) { - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} +static size_t longest_common_subsequence(const llama_tokens & a, const llama_tokens & b) { + // check for empty sequences + if (a.empty() || b.empty()) { + return 0; + } - return i; + // get the lengths of the input sequences + size_t a_len = a.size(); + size_t b_len = b.size(); + + // initialize the maximum length of the longest common subsequence (LCS) + size_t max_length = 0; + + // use two rows instead of a 2D matrix to optimize space + std::vector prev_row(b_len + 1, 0); + std::vector curr_row(b_len + 1, 0); + + // iterate through the elements of a + for (size_t i = 1; i <= a_len; i++) { + // iterate through the elements of b + for (size_t j = 1; j <= b_len; j++) { + // if elements at the current positions match + if (a[i - 1] == b[j - 1]) { + // if it's the first element of either sequences, set LCS length to 1 + if (i == 1 || j == 1) { + curr_row[j] = 1; + } else { + // increment LCS length by 1 compared to the previous element + curr_row[j] = prev_row[j - 1] + 1; + } + + // update max_length if necessary + if (curr_row[j] > max_length) { + max_length = curr_row[j]; + } + } else { + // reset LCS length if elements don't match + curr_row[j] = 0; + } + } + + // update the previous row for the next iteration + prev_row = curr_row; + } + + // return the maximum length of the LCS + return max_length; } static bool ends_with(const std::string & str, const std::string & suffix) { @@ -229,18 +515,6 @@ static size_t find_partial_stop_string(const std::string &stop, const std::strin return std::string::npos; } -static bool json_is_array_of_numbers(const json & data) { - if (data.is_array()) { - for (const auto & e : data) { - if (!e.is_number()) { - return false; - } - } - return true; - } - return false; -} - // TODO: reuse llama_detokenize template static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { @@ -360,9 +634,9 @@ static json oaicompat_completion_params_parse( // Handle "logprobs" field // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future - if (body.contains("logprobs")) { + if (json_value(body, "logprobs", false)) { llama_params["n_probs"] = json_value(body, "top_logprobs", 20); - } else if (body.contains("top_logprobs")) { + } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) { throw std::runtime_error("top_logprobs requires logprobs to be set to true"); } @@ -375,7 +649,7 @@ static json oaicompat_completion_params_parse( } // Copy remaining properties to llama_params - // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint. + // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint. // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp for (const auto & item : body.items()) { // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" diff --git a/examples/simple-chat/CMakeLists.txt b/examples/simple-chat/CMakeLists.txt new file mode 100644 index 0000000000..87723533b3 --- /dev/null +++ b/examples/simple-chat/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-simple-chat) +add_executable(${TARGET} simple-chat.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/simple-chat/README.md b/examples/simple-chat/README.md new file mode 100644 index 0000000000..f0099ce3dd --- /dev/null +++ b/examples/simple-chat/README.md @@ -0,0 +1,7 @@ +# llama.cpp/example/simple-chat + +The purpose of this example is to demonstrate a minimal usage of llama.cpp to create a simple chat program using the chat template from the GGUF file. + +```bash +./llama-simple-chat -m Meta-Llama-3.1-8B-Instruct.gguf -c 2048 +... diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp new file mode 100644 index 0000000000..5f99731637 --- /dev/null +++ b/examples/simple-chat/simple-chat.cpp @@ -0,0 +1,197 @@ +#include "llama.h" +#include +#include +#include +#include +#include + +static void print_usage(int, char ** argv) { + printf("\nexample usage:\n"); + printf("\n %s -m model.gguf [-c context_size] [-ngl n_gpu_layers]\n", argv[0]); + printf("\n"); +} + +int main(int argc, char ** argv) { + std::string model_path; + int ngl = 99; + int n_ctx = 2048; + + // parse command line arguments + for (int i = 1; i < argc; i++) { + try { + if (strcmp(argv[i], "-m") == 0) { + if (i + 1 < argc) { + model_path = argv[++i]; + } else { + print_usage(argc, argv); + return 1; + } + } else if (strcmp(argv[i], "-c") == 0) { + if (i + 1 < argc) { + n_ctx = std::stoi(argv[++i]); + } else { + print_usage(argc, argv); + return 1; + } + } else if (strcmp(argv[i], "-ngl") == 0) { + if (i + 1 < argc) { + ngl = std::stoi(argv[++i]); + } else { + print_usage(argc, argv); + return 1; + } + } else { + print_usage(argc, argv); + return 1; + } + } catch (std::exception & e) { + fprintf(stderr, "error: %s\n", e.what()); + print_usage(argc, argv); + return 1; + } + } + if (model_path.empty()) { + print_usage(argc, argv); + return 1; + } + + // only print errors + llama_log_set([](enum ggml_log_level level, const char * text, void * /* user_data */) { + if (level >= GGML_LOG_LEVEL_ERROR) { + fprintf(stderr, "%s", text); + } + }, nullptr); + + // initialize the model + llama_model_params model_params = llama_model_default_params(); + model_params.n_gpu_layers = ngl; + + llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params); + if (!model) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return 1; + } + + // initialize the context + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = n_ctx; + ctx_params.n_batch = n_ctx; + + llama_context * ctx = llama_new_context_with_model(model, ctx_params); + if (!ctx) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + // initialize the sampler + llama_sampler * smpl = llama_sampler_chain_init(llama_sampler_chain_default_params()); + llama_sampler_chain_add(smpl, llama_sampler_init_min_p(0.05f, 1)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp(0.8f)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED)); + + // helper function to evaluate a prompt and generate a response + auto generate = [&](const std::string & prompt) { + std::string response; + + // tokenize the prompt + const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true); + std::vector prompt_tokens(n_prompt_tokens); + if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) { + GGML_ABORT("failed to tokenize the prompt\n"); + } + + // prepare a batch for the prompt + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); + llama_token new_token_id; + while (true) { + // check if we have enough space in the context to evaluate this batch + int n_ctx = llama_n_ctx(ctx); + int n_ctx_used = llama_get_kv_cache_used_cells(ctx); + if (n_ctx_used + batch.n_tokens > n_ctx) { + printf("\033[0m\n"); + fprintf(stderr, "context size exceeded\n"); + exit(0); + } + + if (llama_decode(ctx, batch)) { + GGML_ABORT("failed to decode\n"); + } + + // sample the next token + new_token_id = llama_sampler_sample(smpl, ctx, -1); + + // is it an end of generation? + if (llama_token_is_eog(model, new_token_id)) { + break; + } + + // convert the token to a string, print it and add it to the response + char buf[256]; + int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + GGML_ABORT("failed to convert token to piece\n"); + } + std::string piece(buf, n); + printf("%s", piece.c_str()); + fflush(stdout); + response += piece; + + // prepare the next batch with the sampled token + batch = llama_batch_get_one(&new_token_id, 1); + } + + return response; + }; + + std::vector messages; + std::vector formatted(llama_n_ctx(ctx)); + int prev_len = 0; + while (true) { + // get user input + printf("\033[32m> \033[0m"); + std::string user; + std::getline(std::cin, user); + + if (user.empty()) { + break; + } + + // add the user input to the message list and format it + messages.push_back({"user", strdup(user.c_str())}); + int new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size()); + if (new_len > (int)formatted.size()) { + formatted.resize(new_len); + new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size()); + } + if (new_len < 0) { + fprintf(stderr, "failed to apply the chat template\n"); + return 1; + } + + // remove previous messages to obtain the prompt to generate the response + std::string prompt(formatted.begin() + prev_len, formatted.begin() + new_len); + + // generate a response + printf("\033[33m"); + std::string response = generate(prompt); + printf("\n\033[0m"); + + // add the response to the messages + messages.push_back({"assistant", strdup(response.c_str())}); + prev_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), false, nullptr, 0); + if (prev_len < 0) { + fprintf(stderr, "failed to apply the chat template\n"); + return 1; + } + } + + // free resources + for (auto & msg : messages) { + free(const_cast(msg.content)); + } + llama_sampler_free(smpl); + llama_free(ctx); + llama_free_model(model); + + return 0; +} diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index be91b2891d..59760fe95d 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -138,7 +138,7 @@ int main(int argc, char ** argv) { // prepare a batch for the prompt - llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size(), 0, 0); + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); // main loop @@ -175,7 +175,7 @@ int main(int argc, char ** argv) { fflush(stdout); // prepare the next batch with the sampled token - batch = llama_batch_get_one(&new_token_id, 1, n_pos, 0); + batch = llama_batch_get_one(&new_token_id, 1); n_decode += 1; } diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 33b469e8f5..d00cd15cb1 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -39,6 +39,11 @@ int main(int argc, char ** argv) { return 1; } + if (params.n_predict < -1) { + LOG_ERR("%s: --n-predict must be >= -1\n", __func__); + return 1; + } + common_init(); if (params.model_draft.empty()) { @@ -155,9 +160,9 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt with both models - llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); - llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0)); + llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1)); + llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input)); const auto t_enc_end = ggml_time_us(); @@ -180,8 +185,6 @@ int main(int argc, char ** argv) { // target model sampling context (reuse the llama_context's sampling instance) struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams); - struct llama_sampler * softmax = llama_sampler_init_softmax(); - // draft sequence data std::vector drafts(n_seq_dft); @@ -190,8 +193,8 @@ int main(int argc, char ** argv) { drafts[s].smpl = common_sampler_init(model_dft, params.sparams); } - llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1); - llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft); + llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1); + llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, n_seq_dft); const auto t_dec_start = ggml_time_us(); @@ -264,11 +267,12 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < dist_tgt.size; i++) { if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) { p_tgt = dist_tgt.data[i].p; + break; } + } + for (size_t i = 0; i < dist_dft.size; i++) { if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) { p_dft = dist_dft.data[i].p; - } - if (p_tgt && p_dft) { break; } } @@ -443,7 +447,7 @@ int main(int argc, char ** argv) { ++n_past_dft; } - if (n_predict > params.n_predict || has_eos) { + if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) { break; } @@ -626,7 +630,6 @@ int main(int argc, char ** argv) { common_sampler_free(drafts[s].smpl); } - llama_sampler_free(softmax); llama_batch_free(batch_dft); llama_free(ctx_tgt); diff --git a/flake.lock b/flake.lock index 3fb6ced51f..d114f4422a 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1727826117, - "narHash": "sha256-K5ZLCyfO/Zj9mPFldf3iwS6oZStJcU4tSpiXTMYaaL0=", + "lastModified": 1730504689, + "narHash": "sha256-hgmguH29K2fvs9szpq2r3pz2/8cJd2LPS+b4tfNFCwE=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "3d04084d54bedc3d6b8b736c70ef449225c361b1", + "rev": "506278e768c2a08bec68eb62932193e341f55c90", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1728018373, - "narHash": "sha256-NOiTvBbRLIOe5F6RbHaAh6++BNjsb149fGZd1T4+KBg=", + "lastModified": 1732014248, + "narHash": "sha256-y/MEyuJ5oBWrWAic/14LaIr/u5E0wRVzyYsouYY3W6w=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "bc947f541ae55e999ffdb4013441347d83b00feb", + "rev": "23e89b7da85c3640bbc2173fe04f4bd114342367", "type": "github" }, "original": { @@ -36,14 +36,14 @@ }, "nixpkgs-lib": { "locked": { - "lastModified": 1727825735, - "narHash": "sha256-0xHYkMkeLVQAMa7gvkddbPqpxph+hDzdu1XdGPJR+Os=", + "lastModified": 1730504152, + "narHash": "sha256-lXvH/vOfb4aGYyvFmZK/HlsNsr/0CVWlwYvo2rxJk3s=", "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz" }, "original": { "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz" } }, "root": { diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 89fdf9d1c1..2d32da1b6d 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -92,6 +92,7 @@ else() endif() option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF) +option(GGML_CPU_AARCH64 "ggml: use runtime weight conversion of Q4_0 to Q4_X_X" ON) option(GGML_AVX "ggml: enable AVX" ${INS_ENB}) option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB}) @@ -99,12 +100,16 @@ option(GGML_AVX512 "ggml: enable AVX512" OFF) option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF) option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF) option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF) +option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF) +option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF) +option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF) option(GGML_FMA "ggml: enable FMA" ${INS_ENB}) if (NOT MSVC) option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512 endif() option(GGML_LASX "ggml: enable lasx" ON) option(GGML_LSX "ggml: enable lsx" ON) +option(GGML_RVV "ggml: enable rvv" ON) option(GGML_SVE "ggml: enable SVE" OFF) if (WIN32) @@ -113,6 +118,7 @@ endif() # ggml core set(GGML_SCHED_MAX_COPIES "4" CACHE STRING "ggml: max input copies for pipeline parallelism") +option(GGML_CPU "ggml: enable CPU backend" ON) # 3rd party libs / backends option(GGML_ACCELERATE "ggml: enable Accelerate framework" ON) @@ -123,14 +129,9 @@ option(GGML_LLAMAFILE "ggml: use LLAMAFILE" option(GGML_CUDA "ggml: use CUDA" OFF) option(GGML_MUSA "ggml: use MUSA" OFF) -option(GGML_CUDA_FORCE_DMMV "ggml: use dmmv instead of mmvq CUDA kernels" OFF) option(GGML_CUDA_FORCE_MMQ "ggml: use mmq kernels instead of cuBLAS" OFF) option(GGML_CUDA_FORCE_CUBLAS "ggml: always use cuBLAS instead of mmq kernels" OFF) -set (GGML_CUDA_DMMV_X "32" CACHE STRING "ggml: x stride for dmmv CUDA kernels") -set (GGML_CUDA_MMV_Y "1" CACHE STRING "ggml: y block size for mmv CUDA kernels") option(GGML_CUDA_F16 "ggml: use 16 bit floats for some calculations" OFF) -set (GGML_CUDA_KQUANTS_ITER "2" CACHE STRING - "ggml: iters./thread per block for Q2_K/Q6_K") set (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "ggml: max. batch size for using peer access") option(GGML_CUDA_NO_PEER_COPY "ggml: do not use peer to peer copies" OFF) @@ -138,7 +139,7 @@ option(GGML_CUDA_NO_VMM "ggml: do not try to use CUDA VMM" option(GGML_CUDA_FA_ALL_QUANTS "ggml: compile all quants for FlashAttention" OFF) option(GGML_CUDA_GRAPHS "ggml: use CUDA graphs (llama.cpp only)" ${GGML_CUDA_GRAPHS_DEFAULT}) -option(GGML_HIPBLAS "ggml: use hipBLAS" OFF) +option(GGML_HIP "ggml: use HIP" OFF) option(GGML_HIP_UMA "ggml: use HIP unified memory architecture" OFF) option(GGML_VULKAN "ggml: use Vulkan" OFF) option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF) @@ -150,6 +151,7 @@ option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF) option(GGML_KOMPUTE "ggml: use Kompute" OFF) option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT}) +option(GGML_METAL_USE_BF16 "ggml: use bfloat if available" OFF) option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF) option(GGML_METAL_SHADER_DEBUG "ggml: compile Metal with -fno-fast-math" OFF) option(GGML_METAL_EMBED_LIBRARY "ggml: embed Metal library" ${GGML_METAL}) @@ -158,10 +160,13 @@ set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)") option(GGML_OPENMP "ggml: use OpenMP" ON) option(GGML_RPC "ggml: use RPC" OFF) +option(GGML_AMX "ggml: use AMX" OFF) option(GGML_SYCL "ggml: use SYCL" OFF) option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF) set (GGML_SYCL_TARGET "INTEL" CACHE STRING "ggml: sycl target device") +set (GGML_SYCL_DEVICE_ARCH "" CACHE STRING + "ggml: sycl device architecture") # extra artifacts option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE}) @@ -214,13 +219,14 @@ include(CMakePackageConfigHelpers) # all public headers set(GGML_PUBLIC_HEADERS include/ggml.h + include/ggml-cpu.h include/ggml-alloc.h include/ggml-backend.h include/ggml-blas.h include/ggml-cann.h include/ggml-cuda.h - include/ggml.h include/ggml-kompute.h + include/ggml-opt.h include/ggml-metal.h include/ggml-rpc.h include/ggml-sycl.h @@ -230,15 +236,14 @@ set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") #if (GGML_METAL) # set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal") #endif() -install(TARGETS ggml PUBLIC_HEADER) - -if (BUILD_SHARED_LIBS) - install(TARGETS ggml LIBRARY) -endif() +install(TARGETS ggml LIBRARY PUBLIC_HEADER) +install(TARGETS ggml-base LIBRARY) +# FIXME: this should be done in the backend cmake files if (GGML_METAL) + # FIXME: does this need to be installed with GGML_METAL_EMBED_LIBRARY? install( - FILES src/ggml-metal.metal + FILES src/ggml-metal/ggml-metal.metal PERMISSIONS OWNER_READ OWNER_WRITE diff --git a/ggml/include/ggml-amx.h b/ggml/include/ggml-amx.h new file mode 100644 index 0000000000..042d6d9199 --- /dev/null +++ b/ggml/include/ggml-amx.h @@ -0,0 +1,25 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + + +#ifdef __cplusplus +extern "C" { +#endif + +// buffer_type API +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); + +GGML_BACKEND_API bool ggml_backend_is_amx(ggml_backend_t backend); + +// backend API +GGML_BACKEND_API ggml_backend_t ggml_backend_amx_init(void); + +GGML_BACKEND_API void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads); + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_amx_reg(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index 5933b8e8f6..cef164764b 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -3,6 +3,20 @@ #include "ggml.h" #include "ggml-alloc.h" +#ifdef GGML_BACKEND_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef GGML_BACKEND_BUILD +# define GGML_BACKEND_API __declspec(dllexport) extern +# else +# define GGML_BACKEND_API __declspec(dllimport) extern +# endif +# else +# define GGML_BACKEND_API __attribute__ ((visibility ("default"))) extern +# endif +#else +# define GGML_BACKEND_API extern +#endif + #ifdef __cplusplus extern "C" { #endif @@ -72,7 +86,7 @@ extern "C" { GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - // "offset" refers to the offset of the tensor data for setting/getting data + // "offset" refers to the offset in tensor->data for setting/getting data GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size); @@ -114,11 +128,12 @@ extern "C" { // enum ggml_backend_dev_type { + // CPU device using system memory GGML_BACKEND_DEVICE_TYPE_CPU, + // GPU device using dedicated memory GGML_BACKEND_DEVICE_TYPE_GPU, - // devices with full capabilities (excludes backends such as BLAS that only support matrix multiplication) - GGML_BACKEND_DEVICE_TYPE_CPU_FULL, - GGML_BACKEND_DEVICE_TYPE_GPU_FULL + // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX) + GGML_BACKEND_DEVICE_TYPE_ACCEL }; // functionality supported by the device @@ -167,10 +182,14 @@ extern "C" { GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index); GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name); + // Common functions that may be obtained using ggml_backend_reg_get_proc_address - // Functions that may be obtained using ggml_backend_reg_get_proc_address - typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *); - typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t, int); + // Split buffer type for tensor parallelism + typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split); + // Set the number of threads for the backend + typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads); + // Get additional buffer types provided by the device (returns a NULL-terminated array) + typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device); // // Backend registry @@ -192,7 +211,7 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params); // = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params) GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params); - // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU_FULL) OR ggml_backend_dev_by_type(CPU_FULL), NULL) + // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL) GGML_API ggml_backend_t ggml_backend_init_best(void); // @@ -223,14 +242,20 @@ extern "C" { ggml_backend_sched_reserve(sched, reserve_graph); // compute - graph = build_graph(sched); - ggml_backend_sched_graph_compute(sched, graph); + graph = build_graph(sched); // the graph and its tensors are single-use in terms of allocation, multi-use in terms of computation + for (int i = 0; i < 10; ++i) { + ggml_backend_sched_graph_compute(sched, graph); // on the first iteration the graph is allocated automatically + } // if there are graph inputs: - ggml_backend_sched_reset(sched); - ggml_backend_sched_alloc_graph(sched, graph); - ggml_backend_tensor_set(input_tensor, ...); - ggml_backend_sched_graph_compute(sched, graph); + graph = build_graph(sched); // get a new graph that is not allocated (the metadata for the old graph is freed once ggml_free is called) + ggml_backend_sched_reset(sched); // clear the allocation of the previous graph + ggml_backend_sched_alloc_graph(sched, graph); // explicitly allocate the new graph but do not execute it + ggml_backend_tensor_set(input_tensor, ...); // copy data to the newly allocated graph tensors + ggml_backend_sched_graph_compute(sched, graph); // execute the graph + + // as an alternative to the above it is also possible to assign the inputs to a dedicated context and + // allocate them statically via ggml_backend_alloc_ctx_tensors } */ @@ -245,7 +270,7 @@ extern "C" { // typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); - // Initialize a backend scheduler + // Initialize a backend scheduler, backends with low index are given priority over backends with high index GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel); GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); @@ -270,7 +295,9 @@ extern "C" { GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph); GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched); - // Reset all assignments and allocators - must be called before changing the node backends + // Reset all assignments and allocators - must be called before changing the node backends or allocating a new graph. + // This in effect deallocates all tensors that were previously allocated and leaves them with dangling pointers. + // The correct way to use this API is to discard the deallocated tensors and create new ones. GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute @@ -300,27 +327,10 @@ extern "C" { GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor); - // - // CPU backend - // - - GGML_API ggml_backend_t ggml_backend_cpu_init(void); - - GGML_API bool ggml_backend_is_cpu (ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads); - GGML_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool); - GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); - - // Create a backend buffer from an existing pointer + // CPU buffer types are always available GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); - GGML_API ggml_backend_reg_t ggml_backend_cpu_reg(void); - -#ifdef GGML_USE_CPU_HBM - GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); -#endif - #ifdef __cplusplus } #endif diff --git a/ggml/include/ggml-blas.h b/ggml/include/ggml-blas.h index 25b2e637fb..87a81b3634 100644 --- a/ggml/include/ggml-blas.h +++ b/ggml/include/ggml-blas.h @@ -9,15 +9,15 @@ extern "C" { #endif // backend API -GGML_API ggml_backend_t ggml_backend_blas_init(void); +GGML_BACKEND_API ggml_backend_t ggml_backend_blas_init(void); -GGML_API bool ggml_backend_is_blas(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_blas(ggml_backend_t backend); // number of threads used for conversion to float // for openblas and blis, this will also set the number of threads used for blas operations -GGML_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads); +GGML_BACKEND_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads); -GGML_API ggml_backend_reg_t ggml_backend_blas_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_blas_reg(void); #ifdef __cplusplus diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h index 95bdaf10d1..b469e228d0 100644 --- a/ggml/include/ggml-cann.h +++ b/ggml/include/ggml-cann.h @@ -34,6 +34,8 @@ extern "C" { */ #define GGML_CANN_MAX_DEVICES 16 +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cann_reg(void); + /** * @brief Initializes the CANN backend for a specified device. * @@ -44,7 +46,7 @@ extern "C" { * @param device The index of the device to initialize. * @return A pointer to the initialized backend instance, or nullptr on failure. */ -GGML_API ggml_backend_t ggml_backend_cann_init(int32_t device); +GGML_BACKEND_API ggml_backend_t ggml_backend_cann_init(int32_t device); /** * @brief Checks if a given backend is a CANN backend. @@ -55,7 +57,7 @@ GGML_API ggml_backend_t ggml_backend_cann_init(int32_t device); * @param backend The backend instance to check. * @return True if the backend is a CANN backend, false otherwise. */ -GGML_API bool ggml_backend_is_cann(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_cann(ggml_backend_t backend); /** * @brief Retrieves the CANN buffer type for a specified device. @@ -67,7 +69,7 @@ GGML_API bool ggml_backend_is_cann(ggml_backend_t backend); * @return A pointer to the buffer type interface for the specified device, or * nullptr if the device index is out of range. */ -GGML_API ggml_backend_buffer_type_t +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_buffer_type(int32_t device); /** @@ -78,14 +80,14 @@ ggml_backend_cann_buffer_type(int32_t device); * * @return The number of CANN devices available. */ -GGML_API int32_t ggml_backend_cann_get_device_count(void); +GGML_BACKEND_API int32_t ggml_backend_cann_get_device_count(void); /** * @brief pinned host buffer for use with the CPU backend for faster copies between CPU and NPU. * * @return A pointer to the host buffer type interface. */ -GGML_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); /** * @brief Retrieves the description of a specific CANN device. @@ -97,7 +99,7 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); * @param description Pointer to a buffer where the description will be written. * @param description_size Size of the description buffer. */ -GGML_API void ggml_backend_cann_get_device_description( +GGML_BACKEND_API void ggml_backend_cann_get_device_description( int32_t device, char* description, size_t description_size); /** @@ -112,7 +114,7 @@ GGML_API void ggml_backend_cann_get_device_description( * @param total Pointer to a variable where the total memory size will be * stored. */ -GGML_API void ggml_backend_cann_get_device_memory(int32_t device, +GGML_BACKEND_API void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, size_t* total); diff --git a/ggml/include/ggml-cpp.h b/ggml/include/ggml-cpp.h new file mode 100644 index 0000000000..219361af43 --- /dev/null +++ b/ggml/include/ggml-cpp.h @@ -0,0 +1,38 @@ +#pragma once + +#ifndef __cplusplus +#error "This header is for C++ only" +#endif + +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include + +// Smart pointers for ggml types + +// ggml + +struct ggml_context_deleter { void operator()(ggml_context * ctx) { ggml_free(ctx); } }; +struct gguf_context_deleter { void operator()(gguf_context * ctx) { gguf_free(ctx); } }; + +typedef std::unique_ptr ggml_context_ptr; +typedef std::unique_ptr gguf_context_ptr; + +// ggml-alloc + +struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } }; + +typedef std::unique_ptr ggml_gallocr_ptr; + +// ggml-backend + +struct ggml_backend_deleter { void operator()(ggml_backend_t backend) { ggml_backend_free(backend); } }; +struct ggml_backend_buffer_deleter { void operator()(ggml_backend_buffer_t buffer) { ggml_backend_buffer_free(buffer); } }; +struct ggml_backend_event_deleter { void operator()(ggml_backend_event_t event) { ggml_backend_event_free(event); } }; +struct ggml_backend_sched_deleter { void operator()(ggml_backend_sched_t sched) { ggml_backend_sched_free(sched); } }; + +typedef std::unique_ptr ggml_backend_ptr; +typedef std::unique_ptr ggml_backend_buffer_ptr; +typedef std::unique_ptr ggml_backend_event_ptr; +typedef std::unique_ptr ggml_backend_sched_ptr; diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h new file mode 100644 index 0000000000..7571ef9798 --- /dev/null +++ b/ggml/include/ggml-cpu.h @@ -0,0 +1,177 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + // Scheduling priorities + enum ggml_sched_priority { + GGML_SCHED_PRIO_NORMAL, + GGML_SCHED_PRIO_MEDIUM, + GGML_SCHED_PRIO_HIGH, + GGML_SCHED_PRIO_REALTIME + }; + + // Threadpool params + // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults + struct ggml_threadpool_params { + bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings) + int n_threads; // number of threads + enum ggml_sched_priority prio; // thread priority + uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling) + bool strict_cpu; // strict cpu placement + bool paused; // start in paused state + }; + + struct ggml_threadpool; // forward declaration, see ggml.c + + typedef struct ggml_threadpool * ggml_threadpool_t; + + // the compute plan that needs to be prepared for ggml_graph_compute() + // since https://github.com/ggerganov/ggml/issues/287 + struct ggml_cplan { + size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()` + uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()` + + int n_threads; + struct ggml_threadpool * threadpool; + + // abort ggml_graph_compute when true + ggml_abort_callback abort_callback; + void * abort_callback_data; + }; + + // numa strategies + enum ggml_numa_strategy { + GGML_NUMA_STRATEGY_DISABLED = 0, + GGML_NUMA_STRATEGY_DISTRIBUTE = 1, + GGML_NUMA_STRATEGY_ISOLATE = 2, + GGML_NUMA_STRATEGY_NUMACTL = 3, + GGML_NUMA_STRATEGY_MIRROR = 4, + GGML_NUMA_STRATEGY_COUNT + }; + + GGML_BACKEND_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems + GGML_BACKEND_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node + + GGML_BACKEND_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); + GGML_BACKEND_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); + + GGML_BACKEND_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); + GGML_BACKEND_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); + + GGML_BACKEND_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); + GGML_BACKEND_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); + + GGML_BACKEND_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); + GGML_BACKEND_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value); + + GGML_BACKEND_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); + GGML_BACKEND_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); + + GGML_BACKEND_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); + GGML_BACKEND_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value); + + GGML_BACKEND_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads); + GGML_BACKEND_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads); + GGML_BACKEND_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1); + GGML_BACKEND_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params); + GGML_BACKEND_API void ggml_threadpool_free (struct ggml_threadpool * threadpool); + GGML_BACKEND_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool); + GGML_BACKEND_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool); + GGML_BACKEND_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool); + + // ggml_graph_plan() has to be called before ggml_graph_compute() + // when plan.work_size > 0, caller must allocate memory for plan.work_data + GGML_BACKEND_API struct ggml_cplan ggml_graph_plan( + const struct ggml_cgraph * cgraph, + int n_threads, /* = GGML_DEFAULT_N_THREADS */ + struct ggml_threadpool * threadpool /* = NULL */ ); + GGML_BACKEND_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); + + // same as ggml_graph_compute() but the work data is allocated as a part of the context + // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data + GGML_BACKEND_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); + + // + // system info + // + + // x86 + GGML_BACKEND_API int ggml_cpu_has_sse3 (void); + GGML_BACKEND_API int ggml_cpu_has_ssse3 (void); + GGML_BACKEND_API int ggml_cpu_has_avx (void); + GGML_BACKEND_API int ggml_cpu_has_avx2 (void); + GGML_BACKEND_API int ggml_cpu_has_f16c (void); + GGML_BACKEND_API int ggml_cpu_has_fma (void); + GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void); + GGML_BACKEND_API int ggml_cpu_has_avx512 (void); + GGML_BACKEND_API int ggml_cpu_has_avx512_vbmi(void); + GGML_BACKEND_API int ggml_cpu_has_avx512_vnni(void); + GGML_BACKEND_API int ggml_cpu_has_avx512_bf16(void); + GGML_BACKEND_API int ggml_cpu_has_amx_int8 (void); + // ARM + GGML_BACKEND_API int ggml_cpu_has_neon (void); + GGML_BACKEND_API int ggml_cpu_has_arm_fma (void); + GGML_BACKEND_API int ggml_cpu_has_fp16_va (void); + GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void); + GGML_BACKEND_API int ggml_cpu_has_sve (void); + GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes + // other + GGML_BACKEND_API int ggml_cpu_has_riscv_v (void); + GGML_BACKEND_API int ggml_cpu_has_vsx (void); + GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void); + GGML_BACKEND_API int ggml_cpu_has_llamafile (void); + + // Internal types and functions exposed for tests and benchmarks + + typedef void (*ggml_from_float_to_mat_t) + (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs); + typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, + const void * GGML_RESTRICT y, size_t by, int nrc); + typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, + const void * GGML_RESTRICT y, int nr, int nc); + typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, + const void * GGML_RESTRICT y, int nr, int nc); + + struct ggml_type_traits_cpu { + ggml_from_float_t from_float; + ggml_from_float_to_mat_t from_float_to_mat; + ggml_vec_dot_t vec_dot; + enum ggml_type vec_dot_type; + int64_t nrows; // number of rows to process simultaneously + int64_t ncols; // number of columns to process simultaneously + ggml_gemv_t gemv; + ggml_gemm_t gemm; + }; + + GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type); + + GGML_BACKEND_API void ggml_cpu_init(void); + + // + // CPU backend + // + + GGML_BACKEND_API ggml_backend_t ggml_backend_cpu_init(void); + + GGML_BACKEND_API bool ggml_backend_is_cpu (ggml_backend_t backend); + GGML_BACKEND_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads); + GGML_BACKEND_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool); + GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); + + GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void); + +#ifdef GGML_USE_CPU_HBM + GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); +#endif + + GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void); + GGML_BACKEND_API bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-cuda.h b/ggml/include/ggml-cuda.h index f44d8f4e64..22ad2c0096 100644 --- a/ggml/include/ggml-cuda.h +++ b/ggml/include/ggml-cuda.h @@ -7,7 +7,7 @@ extern "C" { #endif -#ifdef GGML_USE_HIPBLAS +#ifdef GGML_USE_HIP #define GGML_CUDA_NAME "ROCm" #define GGML_CUBLAS_NAME "hipBLAS" #elif defined(GGML_USE_MUSA) @@ -20,27 +20,27 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 // backend API -GGML_API ggml_backend_t ggml_backend_cuda_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_cuda_init(int device); -GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend); // device buffer -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); -GGML_API int ggml_backend_cuda_get_device_count(void); -GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); +GGML_BACKEND_API int ggml_backend_cuda_get_device_count(void); +GGML_BACKEND_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_BACKEND_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); -GGML_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); -GGML_API void ggml_backend_cuda_unregister_host_buffer(void * buffer); +GGML_BACKEND_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); +GGML_BACKEND_API void ggml_backend_cuda_unregister_host_buffer(void * buffer); -GGML_API ggml_backend_reg_t ggml_backend_cuda_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cuda_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-kompute.h b/ggml/include/ggml-kompute.h index 171465456a..154aa56a74 100644 --- a/ggml/include/ggml-kompute.h +++ b/ggml/include/ggml-kompute.h @@ -11,6 +11,8 @@ extern "C" { #endif +#define GGML_KOMPUTE_MAX_DEVICES 16 + struct ggml_vk_device { int index; int type; // same as VkPhysicalDeviceType @@ -35,11 +37,13 @@ struct ggml_vk_device ggml_vk_current_device(void); // forward declaration typedef struct ggml_backend * ggml_backend_t; -GGML_API ggml_backend_t ggml_backend_kompute_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_kompute_init(int device); -GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_kompute(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_kompute_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-metal.h b/ggml/include/ggml-metal.h index b8d3f678b7..669c1f84ae 100644 --- a/ggml/include/ggml-metal.h +++ b/ggml/include/ggml-metal.h @@ -39,27 +39,27 @@ extern "C" { // user-code should use only these functions // -GGML_API ggml_backend_t ggml_backend_metal_init(void); +GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void); -GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend); GGML_DEPRECATED( - GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size), + GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size), "obsoleted by the new device interface - https://github.com/ggerganov/llama.cpp/pull/9713"); -GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data); +GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data); -GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); // helper to check if the device supports a specific family // ideally, the user code should be doing these checks // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf -GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family); +GGML_BACKEND_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family); // capture all command buffers committed the next time `ggml_backend_graph_compute` is called -GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); +GGML_BACKEND_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); -GGML_API ggml_backend_reg_t ggml_backend_metal_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_metal_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-opt.h b/ggml/include/ggml-opt.h new file mode 100644 index 0000000000..eb5eab9de6 --- /dev/null +++ b/ggml/include/ggml-opt.h @@ -0,0 +1,216 @@ +// This file contains functionality for training models using GGML. +// It is not strictly needed vs. just vanilla GGML but it provides a more high-level interface for common needs such as datasets. +// At the bottom of this file especially there are relatively high-level functions that are suitable use or adaptation in user code. +// +// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de) + +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#include + +#ifdef __cplusplus +extern "C" { +#endif + + struct ggml_opt_dataset; + struct ggml_opt_context; + struct ggml_opt_result; + + typedef struct ggml_opt_dataset * ggml_opt_dataset_t; + typedef struct ggml_opt_context * ggml_opt_context_t; + typedef struct ggml_opt_result * ggml_opt_result_t; + + // ====== Loss ====== + + // built-in loss types, i.e. the built-in quantities minimized by the optimizer + // custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value + enum ggml_opt_loss_type { + GGML_OPT_LOSS_TYPE_MEAN, + GGML_OPT_LOSS_TYPE_SUM, + GGML_OPT_LOSS_TYPE_CROSS_ENTROPY, + GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, + }; + + // ====== Dataset ====== + + GGML_API ggml_opt_dataset_t ggml_opt_dataset_init( + int64_t ne_datapoint, // number of elements per datapoint + int64_t ne_label, // number of elements per label + int64_t ndata, // total number of datapoints/labels + int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied) + GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset); + + // get underlying tensors that store the data + GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata] + GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata] + + // shuffle idata first datapoints from dataset with RNG from opt_ctx, shuffle all datapoints if idata is negative + GGML_API void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata); + + // get batch at position ibatch from dataset and copy the data to data_batch and labels_batch + GGML_API void ggml_opt_dataset_get_batch( + ggml_opt_dataset_t dataset, + struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch] + struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch] + int64_t ibatch); + + // ====== Model / Context ====== + + enum ggml_opt_build_type { + GGML_OPT_BUILD_TYPE_FORWARD, + GGML_OPT_BUILD_TYPE_GRAD, + GGML_OPT_BUILD_TYPE_OPT, + }; + + // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss + struct ggml_opt_optimizer_params { + // AdamW optimizer parameters + struct { + float alpha; // learning rate + float beta1; + float beta2; + float eps; // epsilon for numerical stability + float wd; // weight decay for AdamW, use 0.0f to disable + } adamw; + }; + + // callback to calculate optimizer parameters prior to a backward pass + // userdata can be used to pass arbitrary data + typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata); + + // returns the default optimizer params (constant) + // userdata is not used + GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata); + + // parameters for initializing a new optimization context + struct ggml_opt_params { + ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs + + struct ggml_context * ctx_compute; // created in user code, holds non-static tensors + + // the forward graph is defined by inputs and outputs + // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts + struct ggml_tensor * inputs; + struct ggml_tensor * outputs; + + enum ggml_opt_loss_type loss_type; + enum ggml_opt_build_type build_type; + + int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done + + ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters + void * get_opt_pars_ud; // userdata for calculating optimizer parameters + }; + + // get parameters for an optimization context with defaults set where possible + // parameters for which no sensible defaults exist are supplied as arguments to this function + GGML_API ggml_opt_params ggml_opt_default_params( + ggml_backend_sched_t backend_sched, + struct ggml_context * ctx_compute, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs, + enum ggml_opt_loss_type loss_type); + + GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params); + GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx); + + // set gradients to zero, initilize loss, and optionally reset the optimizer + GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer); + + // get underlying tensors that store data + GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor + GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor + GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against + GGML_API struct ggml_tensor * ggml_opt_loss( ggml_opt_context_t opt_ctx); // scalar tensor that contains the loss + GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs + GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels + + GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node); + + // ====== Optimization Result ====== + + GGML_API ggml_opt_result_t ggml_opt_result_init(); + GGML_API void ggml_opt_result_free(ggml_opt_result_t result); + GGML_API void ggml_opt_result_reset(ggml_opt_result_t result); + + // get data from result, uncertainties are optional and can be ignored by passing NULL + GGML_API void ggml_opt_result_ndata( ggml_opt_result_t result, int64_t * ndata); // writes 1 value, number of datapoints + GGML_API void ggml_opt_result_loss( ggml_opt_result_t result, double * loss, double * unc); // writes 1 value + GGML_API void ggml_opt_result_pred( ggml_opt_result_t result, int32_t * pred); // writes ndata values + GGML_API void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc); // writes 1 value + + // ====== Computation ====== + + // do forward pass, increment result if not NULL + GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + + // do forward pass, increment result if not NULL, do backward pass + GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + + // ############################################################################ + // ## The high-level functions start here. They do not depend on any private ## + // ## functions or structs and can be copied to and adapted for user code. ## + // ############################################################################ + + // ====== Intended Usage ====== + // + // 1. Select the appropriate loss for your problem. + // 2. Create a dataset and set the data for the "data" tensor. Also set the "labels" tensor if your loss needs them. + // Setting the shard size to 1 will be fine, it's the granularity with which data is shuffled/loaded (bigger values are faster). + // 3. Create a GGML graph for your model with no_alloc == true. Use two separate contexts for the tensors. + // The first context should contain the model parameters and inputs and be allocated statically in user code. + // The second context should contain all other tensors and will be (re)allocated automatically. + // Due to this automated allocation the data of the second context is not defined when accessed in user code. + // Note that the second dimension of the inputs/outputs are interpreted as the number of datapoints in those tensors. + // 4. Call ggml_opt_fit. If you need more control you can use ggml_opt_epoch instead. + + // signature for a callback while evaluating opt_ctx on dataset, called after an evaluation + typedef void (*ggml_opt_epoch_callback)( + bool train, // true after training evaluation, false after validation evaluation + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, // result associated with the dataset subsection + int64_t ibatch, // number of batches that have been evaluated so far + int64_t ibatch_max, // total number of batches in this dataset subsection + int64_t t_start_us); // time at which the evaluation on the dataset subsection was started + + // do training on front of dataset, do evaluation only on back of dataset + GGML_API void ggml_opt_epoch( + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, // result to increment during training, ignored if NULL + ggml_opt_result_t result_eval, // result to increment during evaluation, ignored if NULL + int64_t idata_split, // data index at which to split training and evaluation + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval); + + // callback that prints a progress bar on stderr + GGML_API void ggml_opt_epoch_callback_progress_bar( + bool train, + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + int64_t ibatch, + int64_t ibatch_max, + int64_t t_start_us); + + // fit model defined by inputs and outputs to dataset + GGML_API void ggml_opt_fit( + ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs + ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs + ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch] + ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used + ggml_opt_dataset_t dataset, // dataset with data and optionally also labels + enum ggml_opt_loss_type loss_type, // loss to minimize + ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t) + int64_t nepoch, // how many times the dataset should be iterated over + int64_t nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs + float val_split, // fraction of the dataset to use for validation, must be in [0.0f, 1.0f) + bool silent); // whether or not info prints to stderr should be suppressed + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index d579673682..ade6c3b0ef 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -10,18 +10,18 @@ extern "C" { #define GGML_RPC_MAX_SERVERS 16 // backend API -GGML_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint); -GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend); +GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint); +GGML_BACKEND_API bool ggml_backend_is_rpc(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); -GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); +GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); -GGML_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); +GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); -GGML_API ggml_backend_reg_t ggml_backend_rpc_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void); -GGML_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); +GGML_BACKEND_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); #ifdef __cplusplus } diff --git a/ggml/include/ggml-sycl.h b/ggml/include/ggml-sycl.h index 03b698e61b..5ce349a880 100644 --- a/ggml/include/ggml-sycl.h +++ b/ggml/include/ggml-sycl.h @@ -17,26 +17,33 @@ extern "C" { #endif // backend API -GGML_API ggml_backend_t ggml_backend_sycl_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_sycl_init(int device); + +GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend); // devide buffer -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); -GGML_API void ggml_backend_sycl_print_sycl_devices(void); -GGML_API void ggml_sycl_get_gpu_list(int *id_list, int max_len); -GGML_API void ggml_sycl_get_device_description(int device, char *description, size_t description_size); -GGML_API int ggml_backend_sycl_get_device_count(); -GGML_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); +GGML_BACKEND_API void ggml_backend_sycl_print_sycl_devices(void); +GGML_BACKEND_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len); +GGML_BACKEND_API void ggml_backend_sycl_get_device_description(int device, + char *description, + size_t description_size); +GGML_BACKEND_API int ggml_backend_sycl_get_device_count(); +GGML_BACKEND_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); // SYCL doesn't support registering host memory, keep here for reference -// GGML_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); -// GGML_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); +// GGML_BACKEND_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); +// GGML_BACKEND_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_sycl_reg(void); + #ifdef __cplusplus } #endif diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h index e074042efa..53cdba072c 100644 --- a/ggml/include/ggml-vulkan.h +++ b/ggml/include/ggml-vulkan.h @@ -10,19 +10,21 @@ extern "C" { #define GGML_VK_NAME "Vulkan" #define GGML_VK_MAX_DEVICES 16 -GGML_API void ggml_vk_instance_init(void); +GGML_BACKEND_API void ggml_vk_instance_init(void); // backend API -GGML_API ggml_backend_t ggml_backend_vk_init(size_t dev_num); +GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num); -GGML_API bool ggml_backend_is_vk(ggml_backend_t backend); -GGML_API int ggml_backend_vk_get_device_count(void); -GGML_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); +GGML_BACKEND_API bool ggml_backend_is_vk(ggml_backend_t backend); +GGML_BACKEND_API int ggml_backend_vk_get_device_count(void); +GGML_BACKEND_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); +GGML_BACKEND_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); -GGML_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_vk_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 4508da4fb3..69e6a24344 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -176,15 +176,15 @@ #ifdef GGML_SHARED # if defined(_WIN32) && !defined(__MINGW32__) # ifdef GGML_BUILD -# define GGML_API __declspec(dllexport) +# define GGML_API __declspec(dllexport) extern # else -# define GGML_API __declspec(dllimport) +# define GGML_API __declspec(dllimport) extern # endif # else -# define GGML_API __attribute__ ((visibility ("default"))) +# define GGML_API __attribute__ ((visibility ("default"))) extern # endif #else -# define GGML_API +# define GGML_API extern #endif // TODO: support for clang @@ -217,7 +217,6 @@ #define GGML_MAX_DIMS 4 #define GGML_MAX_PARAMS 2048 -#define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 #define GGML_MAX_N_THREADS 512 #define GGML_MAX_OP_PARAMS 64 @@ -510,7 +509,7 @@ extern "C" { GGML_OP_WIN_UNPART, GGML_OP_GET_REL_POS, GGML_OP_ADD_REL_POS, - GGML_OP_RWKV_WKV, + GGML_OP_RWKV_WKV6, GGML_OP_UNARY, @@ -559,10 +558,10 @@ extern "C" { enum ggml_log_level { GGML_LOG_LEVEL_NONE = 0, - GGML_LOG_LEVEL_INFO = 1, - GGML_LOG_LEVEL_WARN = 2, - GGML_LOG_LEVEL_ERROR = 3, - GGML_LOG_LEVEL_DEBUG = 4, + GGML_LOG_LEVEL_DEBUG = 1, + GGML_LOG_LEVEL_INFO = 2, + GGML_LOG_LEVEL_WARN = 3, + GGML_LOG_LEVEL_ERROR = 4, GGML_LOG_LEVEL_CONT = 5, // continue previous log }; @@ -574,6 +573,13 @@ extern "C" { GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) }; + struct ggml_init_params { + // memory pool + size_t mem_size; // bytes + void * mem_buffer; // if NULL, memory will be allocated internally + bool no_alloc; // don't allocate memory for the tensor data + }; + // n-dimensional tensor struct ggml_tensor { enum ggml_type type; @@ -596,7 +602,6 @@ extern "C" { int32_t flags; - struct ggml_tensor * grad; struct ggml_tensor * src[GGML_MAX_SRC]; // source tensor and offset for views @@ -609,7 +614,7 @@ extern "C" { void * extra; // extra things e.g. for ggml-cuda.cu - // char padding[4]; + char padding[8]; }; static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor); @@ -619,66 +624,6 @@ extern "C" { // If it returns true, the computation is aborted typedef bool (*ggml_abort_callback)(void * data); - // Scheduling priorities - enum ggml_sched_priority { - GGML_SCHED_PRIO_NORMAL, - GGML_SCHED_PRIO_MEDIUM, - GGML_SCHED_PRIO_HIGH, - GGML_SCHED_PRIO_REALTIME - }; - - // Threadpool params - // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults - struct ggml_threadpool_params { - bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings) - int n_threads; // number of threads - enum ggml_sched_priority prio; // thread priority - uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling) - bool strict_cpu; // strict cpu placement - bool paused; // start in paused state - }; - - struct ggml_threadpool; // forward declaration, see ggml.c - - typedef struct ggml_threadpool * ggml_threadpool_t; - - // the compute plan that needs to be prepared for ggml_graph_compute() - // since https://github.com/ggerganov/ggml/issues/287 - struct ggml_cplan { - size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()` - uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()` - - int n_threads; - struct ggml_threadpool * threadpool; - - // abort ggml_graph_compute when true - ggml_abort_callback abort_callback; - void * abort_callback_data; - }; - - // scratch buffer - struct ggml_scratch { - size_t offs; - size_t size; - void * data; - }; - - struct ggml_init_params { - // memory pool - size_t mem_size; // bytes - void * mem_buffer; // if NULL, memory will be allocated internally - bool no_alloc; // don't allocate memory for the tensor data - }; - - // numa strategies - enum ggml_numa_strategy { - GGML_NUMA_STRATEGY_DISABLED = 0, - GGML_NUMA_STRATEGY_DISTRIBUTE = 1, - GGML_NUMA_STRATEGY_ISOLATE = 2, - GGML_NUMA_STRATEGY_NUMACTL = 3, - GGML_NUMA_STRATEGY_MIRROR = 4, - GGML_NUMA_STRATEGY_COUNT - }; // // GUID @@ -701,9 +646,6 @@ extern "C" { // accepts a UTF-8 path, even on Windows GGML_API FILE * ggml_fopen(const char * fname, const char * mode); - GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems - GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node - GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); @@ -760,12 +702,12 @@ extern "C" { // main - GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); - GGML_API void ggml_free(struct ggml_context * ctx); + GGML_API struct ggml_context * ggml_init (struct ggml_init_params params); + GGML_API void ggml_reset(struct ggml_context * ctx); + GGML_API void ggml_free (struct ggml_context * ctx); GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); - GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch); GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx); GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc); @@ -805,8 +747,7 @@ extern "C" { int64_t ne2, int64_t ne3); - GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); - GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); + GGML_API void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes); GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src); @@ -816,35 +757,25 @@ extern "C" { GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); - GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); - GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); - GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); - // Converts a flat index into coordinates - GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3); + GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3); - GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); - GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); - - GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); - GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value); - - GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); - GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); - - GGML_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); - GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value); + GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); - GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); - GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name); GGML_ATTRIBUTE_FORMAT(2, 3) GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...); + // Tensor flags + GGML_API void ggml_set_input(struct ggml_tensor * tensor); + GGML_API void ggml_set_output(struct ggml_tensor * tensor); + GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API void ggml_set_loss(struct ggml_tensor * tensor); + // // operations on tensors with backpropagation // @@ -1558,7 +1489,7 @@ extern "C" { "use ggml_rope_ext_inplace instead"); // compute correction dims for YaRN RoPE scaling - void ggml_rope_yarn_corr_dims( + GGML_API void ggml_rope_yarn_corr_dims( int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy @@ -1814,6 +1745,9 @@ extern "C" { struct ggml_tensor * a, enum ggml_prec prec); + GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec( + const struct ggml_tensor * a); + // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, @@ -1887,7 +1821,7 @@ extern "C" { struct ggml_tensor * pw, struct ggml_tensor * ph); - GGML_API struct ggml_tensor * ggml_rwkv_wkv( + GGML_API struct ggml_tensor * ggml_rwkv_wkv6( struct ggml_context * ctx, struct ggml_tensor * k, struct ggml_tensor * v, @@ -2050,31 +1984,20 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * grad, - float alpha, - float beta1, - float beta2, - float eps, - float wd); // weight decay + struct ggml_tensor * m, + struct ggml_tensor * v, + struct ggml_tensor * adamw_params); // parameters such a the learning rate // // automatic differentiation // - GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); - GGML_API void ggml_set_loss(struct ggml_tensor * tensor); - - GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); - GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate); - - GGML_API void ggml_build_opt_adamw( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - float alpha, - float beta1, - float beta2, - float eps, - float wd); // weight decay + GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); + GGML_API void ggml_build_backward_expand( + struct ggml_context * ctx_static, // context for static gradients (loss + gradient accumulation) + struct ggml_context * ctx_compute, // context for gradient computation + struct ggml_cgraph * cgraph, + bool accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static // graph allocation in a context GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false @@ -2094,28 +2017,9 @@ extern "C" { GGML_API size_t ggml_graph_overhead(void); GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads); - GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads); - GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads); - GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1); - GGML_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params); - GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool); - GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool); - GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool); - GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool); - - // ggml_graph_plan() has to be called before ggml_graph_compute() - // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan( - const struct ggml_cgraph * cgraph, - int n_threads, /* = GGML_DEFAULT_N_THREADS */ - struct ggml_threadpool * threadpool /* = NULL */ ); - GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - - // same as ggml_graph_compute() but the work data is allocated as a part of the context - // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data - GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); - - GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); + GGML_API struct ggml_tensor * ggml_graph_get_tensor (const struct ggml_cgraph * cgraph, const char * name); + GGML_API struct ggml_tensor * ggml_graph_get_grad (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node); + GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node); GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); @@ -2126,201 +2030,14 @@ extern "C" { // dump the graph into a file using the dot format GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename); - // build gradient checkpointing backward graph gb for gf using provided checkpoints - // gb_tmp will contain original backward graph with rewritten backward process nodes, - // but without the second forward pass nodes. - GGML_API void ggml_build_backward_gradient_checkpointing( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - struct ggml_cgraph * gb_tmp, - struct ggml_tensor * * checkpoints, - int n_checkpoints); - // - // optimization - // - - // optimization methods - enum ggml_opt_type { - GGML_OPT_TYPE_ADAM, - GGML_OPT_TYPE_LBFGS, - }; - - // linesearch methods - enum ggml_linesearch { - GGML_LINESEARCH_DEFAULT = 1, - - GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0, - GGML_LINESEARCH_BACKTRACKING_WOLFE = 1, - GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2, - }; - - // optimization return values - enum ggml_opt_result { - GGML_OPT_RESULT_OK = 0, - GGML_OPT_RESULT_DID_NOT_CONVERGE, - GGML_OPT_RESULT_NO_CONTEXT, - GGML_OPT_RESULT_INVALID_WOLFE, - GGML_OPT_RESULT_FAIL, - GGML_OPT_RESULT_CANCEL, - - GGML_LINESEARCH_FAIL = -128, - GGML_LINESEARCH_MINIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_ITERATIONS, - GGML_LINESEARCH_INVALID_PARAMETERS, - }; - - typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel); + // TODO these functions were sandwiched in the old optimization interface, is there a better place for them? typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data); // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); - // optimization parameters - // - // see ggml.c (ggml_opt_default_params) for default values - // - struct ggml_opt_params { - enum ggml_opt_type type; - - size_t graph_size; - - int n_threads; - - // delta-based convergence test - // - // if past == 0 - disabled - // if past > 0: - // stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|) - // - int past; - float delta; - - // maximum number of iterations without improvement - // - // if 0 - disabled - // if > 0: - // assume convergence if no cost improvement in this number of iterations - // - int max_no_improvement; - - bool print_forward_graph; - bool print_backward_graph; - - int n_gradient_accumulation; - - // ADAM parameters - struct { - int n_iter; - - float sched; // schedule multiplier (fixed, decay or warmup) - float decay; // weight decay for AdamW, use 0.0f to disable - int decay_min_ndim; // minimum number of tensor dimension to apply weight decay - float alpha; // learning rate - float beta1; - float beta2; - float eps; // epsilon for numerical stability - float eps_f; // epsilon for convergence test - float eps_g; // epsilon for convergence test - float gclip; // gradient clipping - } adam; - - // LBFGS parameters - struct { - int m; // number of corrections to approximate the inv. Hessian - int n_iter; - int max_linesearch; - - float eps; // convergence tolerance - float ftol; // line search tolerance - float wolfe; - float min_step; - float max_step; - - enum ggml_linesearch linesearch; - } lbfgs; - }; - - struct ggml_opt_context { - struct ggml_context * ctx; - struct ggml_opt_params params; - - int iter; - int64_t nx; // number of parameter elements - - bool just_initialized; - - float loss_before; - float loss_after; - - struct { - struct ggml_tensor * g; // current gradient - struct ggml_tensor * m; // first moment - struct ggml_tensor * v; // second moment - struct ggml_tensor * pf; // past function values - float fx_best; - float fx_prev; - int n_no_improvement; - } adam; - - struct { - struct ggml_tensor * x; // current parameters - struct ggml_tensor * xp; // previous parameters - struct ggml_tensor * g; // current gradient - struct ggml_tensor * gp; // previous gradient - struct ggml_tensor * d; // search direction - struct ggml_tensor * pf; // past function values - struct ggml_tensor * lmal; // the L-BFGS memory alpha - struct ggml_tensor * lmys; // the L-BFGS memory ys - struct ggml_tensor * lms; // the L-BFGS memory s - struct ggml_tensor * lmy; // the L-BFGS memory y - float fx_best; - float step; - int j; - int k; - int end; - int n_no_improvement; - } lbfgs; - }; - - GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type); - - // optimize the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt( - struct ggml_context * ctx, - struct ggml_opt_params params, - struct ggml_tensor * f); - - // initialize optimizer context - GGML_API void ggml_opt_init( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - int64_t nx); - - // continue optimizing the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt_resume( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f); - - // continue optimizing the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt_resume_g( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data); - - // - // tensor flags - // - GGML_API void ggml_set_input(struct ggml_tensor * tensor); - GGML_API void ggml_set_output(struct ggml_tensor * tensor); + GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); // // quantization @@ -2477,47 +2194,6 @@ extern "C" { GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx); GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data); - // - // system info - // - - GGML_API int ggml_cpu_has_avx (void); - GGML_API int ggml_cpu_has_avx_vnni (void); - GGML_API int ggml_cpu_has_avx2 (void); - GGML_API int ggml_cpu_has_avx512 (void); - GGML_API int ggml_cpu_has_avx512_vbmi(void); - GGML_API int ggml_cpu_has_avx512_vnni(void); - GGML_API int ggml_cpu_has_avx512_bf16(void); - GGML_API int ggml_cpu_has_fma (void); - GGML_API int ggml_cpu_has_neon (void); - GGML_API int ggml_cpu_has_sve (void); - GGML_API int ggml_cpu_has_arm_fma (void); - GGML_API int ggml_cpu_has_metal (void); - GGML_API int ggml_cpu_has_f16c (void); - GGML_API int ggml_cpu_has_fp16_va (void); - GGML_API int ggml_cpu_has_wasm_simd (void); - GGML_API int ggml_cpu_has_blas (void); - GGML_API int ggml_cpu_has_cuda (void); - GGML_API int ggml_cpu_has_vulkan (void); - GGML_API int ggml_cpu_has_kompute (void); - GGML_API int ggml_cpu_has_gpublas (void); - GGML_API int ggml_cpu_has_sse3 (void); - GGML_API int ggml_cpu_has_ssse3 (void); - GGML_API int ggml_cpu_has_riscv_v (void); - GGML_API int ggml_cpu_has_sycl (void); - GGML_API int ggml_cpu_has_rpc (void); - GGML_API int ggml_cpu_has_vsx (void); - GGML_API int ggml_cpu_has_matmul_int8(void); - GGML_API int ggml_cpu_has_cann (void); - GGML_API int ggml_cpu_has_llamafile (void); - - // get the sve vector length in bytes - GGML_API int ggml_cpu_get_sve_cnt(void); - - // - // Internal types and functions exposed for tests and benchmarks - // - #ifdef __cplusplus // restrict not standard in C++ #define GGML_RESTRICT @@ -2526,14 +2202,6 @@ extern "C" { #endif typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - typedef void (*ggml_from_float_to_mat_t) - (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs); - typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, - const void * GGML_RESTRICT y, size_t by, int nrc); - typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, - const void * GGML_RESTRICT y, int nr, int nc); - typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, - const void * GGML_RESTRICT y, int nr, int nc); struct ggml_type_traits { const char * type_name; @@ -2542,15 +2210,7 @@ extern "C" { size_t type_size; bool is_quantized; ggml_to_float_t to_float; - ggml_from_float_t from_float; ggml_from_float_t from_float_ref; - ggml_from_float_to_mat_t from_float_to_mat; - ggml_vec_dot_t vec_dot; - enum ggml_type vec_dot_type; - int64_t nrows; // number of rows to process simultaneously - int64_t ncols; // number of columns to process simultaneously - ggml_gemv_t gemv; - ggml_gemm_t gemm; }; GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type); diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 676f85a369..8df0e85c0d 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -1,7 +1,5 @@ include(CheckCXXCompilerFlag) -unset(GGML_CDEF_PUBLIC) - add_compile_definitions(GGML_SCHED_MAX_COPIES=${GGML_SCHED_MAX_COPIES}) # enable libstdc++ assertions for debug builds @@ -26,903 +24,6 @@ if (NOT MSVC) endif() endif() -unset(GGML_EXTRA_LIBS_PRIVATE) -unset(GGML_EXTRA_LIBS_PUBLIC) - -if (APPLE AND GGML_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate) - if (ACCELERATE_FRAMEWORK) - message(STATUS "Accelerate framework found") - - add_compile_definitions(GGML_USE_ACCELERATE) - add_compile_definitions(ACCELERATE_NEW_LAPACK) - add_compile_definitions(ACCELERATE_LAPACK_ILP64) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${ACCELERATE_FRAMEWORK}) - else() - message(WARNING "Accelerate framework not found") - endif() -endif() - -if (GGML_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - - message(STATUS "Metal framework found") - set(GGML_HEADERS_METAL ../include/ggml-metal.h) - set(GGML_SOURCES_METAL ggml-metal.m) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_METAL) - if (GGML_METAL_NDEBUG) - add_compile_definitions(GGML_METAL_NDEBUG) - endif() - - # copy ggml-common.h and ggml-metal.metal to bin directory - configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) - configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) - - if (GGML_METAL_EMBED_LIBRARY) - enable_language(ASM) - - add_compile_definitions(GGML_METAL_EMBED_LIBRARY) - - set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h") - set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") - - file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") - - # merge ggml-common.h and ggml-metal.metal into a single file - set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s") - set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal") - - add_custom_command( - OUTPUT ${METALLIB_EMBED_ASM} - COMMAND echo "Embedding Metal library" - COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED} - COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM} - COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM} - COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM} - COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM} - COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM} - COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM} - DEPENDS ggml-metal.metal ggml-common.h - COMMENT "Generate assembly for embedded Metal library" - ) - - list(APPEND GGML_SOURCES_METAL ${METALLIB_EMBED_ASM}) - else() - if (GGML_METAL_SHADER_DEBUG) - # custom command to do the following: - # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib - # - # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works - # disabling fast math is needed in order to pass tests/test-backend-ops - # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 - # note: unfortunately, we have to call it default.metallib instead of ggml.metallib - # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 - set(XC_FLAGS -fno-fast-math -fno-inline -g) - else() - set(XC_FLAGS -O3) - endif() - - # Append macOS metal versioning flags - if (GGML_METAL_MACOSX_VERSION_MIN) - message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation") - list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN}) - endif() - - if (GGML_METAL_STD) - message(STATUS "Adding -std=${GGML_METAL_STD} flag to metal compilation") - list (APPEND XC_FLAGS -std=${GGML_METAL_STD}) - endif() - - add_custom_command( - OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air - COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal - DEPENDS ggml-metal.metal ggml-common.h - COMMENT "Compiling Metal kernels" - ) - - add_custom_target( - ggml-metal ALL - DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - ) - endif() # GGML_METAL_EMBED_LIBRARY - - list(APPEND GGML_EXTRA_LIBS_PRIVATE - ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} - ${METALKIT_FRAMEWORK} - ) -endif() - -if (GGML_MUSA) - set(CMAKE_C_COMPILER clang) - set(CMAKE_C_EXTENSIONS OFF) - set(CMAKE_CXX_COMPILER clang++) - set(CMAKE_CXX_EXTENSIONS OFF) - - set(GGML_CUDA ON) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_MUSA) -endif() - -if (GGML_OPENMP) - find_package(OpenMP) - if (OpenMP_FOUND) - message(STATUS "OpenMP found") - - add_compile_definitions(GGML_USE_OPENMP) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX) - - if (GGML_MUSA) - list(APPEND GGML_EXTRA_INCLUDES "/usr/lib/llvm-14/lib/clang/14.0.0/include") - list(APPEND GGML_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-14/lib/libomp.so") - endif() - else() - message(WARNING "OpenMP not found") - endif() -endif() - -if (GGML_BLAS) - if (GGML_STATIC) - set(BLA_STATIC ON) - endif() - #if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) - # set(BLA_SIZEOF_INTEGER 8) - #endif() - - set(BLA_VENDOR ${GGML_BLAS_VENDOR}) - find_package(BLAS) - - if (BLAS_FOUND) - message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") - - if (("${BLAS_INCLUDE_DIRS}" STREQUAL "") AND NOT (${GGML_BLAS_VENDOR} MATCHES "Apple")) - # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake. - # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268 - find_package(PkgConfig REQUIRED) - if (${GGML_BLAS_VENDOR} MATCHES "Generic") - pkg_check_modules(DepBLAS blas) - elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS") - # As of openblas v0.3.22, the 64-bit is named openblas64.pc - pkg_check_modules(DepBLAS openblas64) - if (NOT DepBLAS_FOUND) - pkg_check_modules(DepBLAS openblas) - endif() - elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME") - add_compile_definitions(GGML_BLAS_USE_BLIS) - pkg_check_modules(DepBLAS blis) - elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS") - pkg_check_modules(DepBLAS blas-atlas) - elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS") - pkg_check_modules(DepBLAS flexiblas_api) - elseif (${GGML_BLAS_VENDOR} MATCHES "Intel") - add_compile_definitions(GGML_BLAS_USE_MKL) - # all Intel* libraries share the same include path - pkg_check_modules(DepBLAS mkl-sdl) - elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC") - # this doesn't provide pkg-config - # suggest to assign BLAS_INCLUDE_DIRS on your own - if ("${NVHPC_VERSION}" STREQUAL "") - message(WARNING "Better to set NVHPC_VERSION") - else() - set(DepBLAS_FOUND ON) - set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include") - endif() - endif() - if (DepBLAS_FOUND) - set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS}) - else() - message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically" - " detected by pkgconfig, trying to find cblas.h from possible paths...") - find_path(BLAS_INCLUDE_DIRS - NAMES cblas.h - HINTS - /usr/include - /usr/local/include - /usr/include/openblas - /opt/homebrew/opt/openblas/include - /usr/local/opt/openblas/include - /usr/include/x86_64-linux-gnu/openblas/include - ) - endif() - endif() - - message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") - - add_compile_options(${BLAS_LINKER_FLAGS}) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_BLAS) - - if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel")) - add_compile_definitions(GGML_BLAS_USE_MKL) - endif() - - set(GGML_HEADERS_BLAS ../include/ggml-blas.h) - set(GGML_SOURCES_BLAS ggml-blas.cpp) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${BLAS_LIBRARIES}) - list(APPEND GGML_EXTRA_INCLUDES ${BLAS_INCLUDE_DIRS}) - else() - message(WARNING "BLAS not found, please refer to " - "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" - " to set correct GGML_BLAS_VENDOR") - endif() -endif() - -if (GGML_LLAMAFILE) - message(STATUS "Using llamafile") - - add_compile_definitions(GGML_USE_LLAMAFILE) - - set(GGML_HEADERS_LLAMAFILE llamafile/sgemm.h) - set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp) -endif() - -if (GGML_CUDA) - cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES - - if (GGML_MUSA) - list(APPEND CMAKE_MODULE_PATH "/usr/local/musa/cmake/") - find_package(MUSAToolkit) - set(CUDAToolkit_FOUND ${MUSAToolkit_FOUND}) - else() - find_package(CUDAToolkit) - endif() - - if (CUDAToolkit_FOUND) - message(STATUS "CUDA found") - - if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - # 52 == lowest CUDA 12 standard - # 60 == FP16 CUDA intrinsics - # 61 == integer CUDA intrinsics - # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster - if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75") - else() - set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75") - #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work - endif() - endif() - message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") - - if (GGML_MUSA) - set(CMAKE_CUDA_COMPILER ${MUSAToolkit_MCC_EXECUTABLE}) - else() - enable_language(CUDA) - endif() - - file(GLOB GGML_HEADERS_CUDA "ggml-cuda/*.cuh") - list(APPEND GGML_HEADERS_CUDA "../include/ggml-cuda.h") - - file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") - list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") - file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - - if (GGML_CUDA_FA_ALL_QUANTS) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) - else() - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - endif() - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA) - - add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y}) - add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER}) - add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) - - if (GGML_CUDA_GRAPHS) - add_compile_definitions(GGML_CUDA_USE_GRAPHS) - endif() - - if (GGML_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - - if (GGML_CUDA_FORCE_CUBLAS) - add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) - endif() - - if (GGML_CUDA_NO_VMM) - add_compile_definitions(GGML_CUDA_NO_VMM) - endif() - - if (DEFINED GGML_CUDA_DMMV_Y) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_DMMV_Y}) # for backwards compatibility - endif() - - if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) - add_compile_definitions(GGML_CUDA_F16) - endif() - - if (GGML_CUDA_NO_PEER_COPY) - add_compile_definitions(GGML_CUDA_NO_PEER_COPY) - endif() - - if (GGML_MUSA) - set_source_files_properties(${GGML_SOURCES_CUDA} PROPERTIES LANGUAGE CXX) - foreach(SOURCE ${GGML_SOURCES_CUDA}) - set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS "-x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22") - endforeach() - endif() - - if (GGML_STATIC) - if (WIN32) - # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt) - else () - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musart_static MUSA::mublas_static) - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) - endif() - endif() - else() - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musart MUSA::mublas) - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt) - endif() - endif() - - if (GGML_CUDA_NO_VMM) - # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) - else() - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musa_driver) # required by muDeviceGetAttribute(), muMemGetAllocationGranularity(...), ... - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ... - endif() - endif() - else() - message(WARNING "CUDA not found") - endif() -endif() - -if (GGML_HIPBLAS) - if (NOT EXISTS $ENV{ROCM_PATH}) - if (NOT EXISTS /opt/rocm) - set(ROCM_PATH /usr) - else() - set(ROCM_PATH /opt/rocm) - endif() - else() - set(ROCM_PATH $ENV{ROCM_PATH}) - endif() - - list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) - list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") - - # CMake on Windows doesn't support the HIP language yet - if (WIN32) - set(CXX_IS_HIPCC TRUE) - else() - string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") - endif() - - if (CXX_IS_HIPCC) - if (LINUX) - if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") - endif() - - message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." - " Prefer setting the HIP compiler directly. See README for details.") - endif() - else() - # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. - if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) - set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) - endif() - cmake_minimum_required(VERSION 3.21) - enable_language(HIP) - endif() - - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) - - message(STATUS "HIP and hipBLAS found") - - file(GLOB GGML_HEADERS_ROCM "ggml-cuda/*.cuh") - list(APPEND GGML_HEADERS_ROCM "../include/ggml-cuda.h") - - file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") - list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") - file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - - if (GGML_CUDA_FA_ALL_QUANTS) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) - else() - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - endif() - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA) - - add_compile_definitions(GGML_USE_HIPBLAS) - add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y}) - add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER}) - - if (GGML_HIP_UMA) - add_compile_definitions(GGML_HIP_UMA) - endif() - - if (GGML_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - - if (GGML_CUDA_FORCE_CUBLAS) - add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) - endif() - - if (GGML_CUDA_NO_PEER_COPY) - add_compile_definitions(GGML_CUDA_NO_PEER_COPY) - endif() - - if (CXX_IS_HIPCC) - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) - list(APPEND GGML_EXTRA_LIBS_PRIVATE hip::device) - else() - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) - endif() - - if (GGML_STATIC) - message(FATAL_ERROR "Static linking not supported for HIP/ROCm") - endif() - - list(APPEND GGML_EXTRA_LIBS_PUBLIC hip::host roc::rocblas roc::hipblas) -endif() - -if (GGML_SYCL) - if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA|AMD)$") - message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL, NVIDIA, or AMD") - endif() - - check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL) - - if (DEFINED ENV{ONEAPI_ROOT}) - message(STATUS "Using oneAPI Release SYCL compiler (icpx).") - elseif(SUPPORTS_SYCL) - message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}. - If you expected the oneAPI Release compiler, please install oneAPI & source it, like: - source /opt/intel/oneapi/setvars.sh") - else() - message(FATAL_ERROR, "C++ compiler lacks SYCL support.") - endif() - message(STATUS "SYCL found") - #todo: AOT - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_SYCL) - - if (GGML_SYCL_F16) - if (GGML_SYCL_TARGET STREQUAL "AMD") - message(WARNING "AMD target does not entirely support FP16 in the SYCL backend.") - endif() - add_compile_definitions(GGML_SYCL_F16) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_SYCL_FORCE_MMQ) - endif() - - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl") - - if (GGML_SYCL_TARGET STREQUAL "NVIDIA") - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - # INFO: Allowed Sub_group_sizes are not consistent through all - # hip targets. For example, 64 is used for certain models, but the backend - # does not support it. - # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32) - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) - else() - add_compile_definitions(GGML_SYCL_WARP_SIZE=16) - endif() - - file(GLOB GGML_HEADERS_SYCL "ggml-sycl/*.hpp") - list(APPEND GGML_HEADERS_SYCL "../include/ggml-sycl.h") - - file(GLOB GGML_SOURCES_SYCL "ggml-sycl/*.cpp") - list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp") - - find_package(DNNL) - message("-- DNNL found:" ${DNNL_FOUND}) - - if (GGML_SYCL_TARGET STREQUAL "INTEL") - add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND}) - else() - add_compile_definitions(GGML_SYCL_DNNL=0) - endif() - - if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") - list(APPEND GGML_EXTRA_LIBS_PRIVATE DNNL::dnnl) - endif() - - if (WIN32) - find_package(IntelSYCL REQUIRED) - find_package(MKL REQUIRED) - list(APPEND GGML_EXTRA_LIBS_PRIVATE IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) - else() - if (GGML_SYCL_TARGET STREQUAL "INTEL") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) - elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl pthread m dl onemkl) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - if (GGML_SYCL_HIP_TARGET STREQUAL "") - message(ERROR "Can't enable SYCL hip backend, GGML_SYCL_HIP_TARGET has not been set.") - endif() - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=amdgcn-amd-amdhsa -Xsycl-target-backend --offload-arch=${GGML_SYCL_HIP_TARGET}") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl pthread m dl onemkl) - endif() - endif() -endif() - -if (GGML_RPC) - message(STATUS "RPC found") - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_RPC) - - if (WIN32) - list(APPEND GGML_EXTRA_LIBS_PRIVATE ws2_32) - endif() - - set(GGML_HEADERS_RPC ../include/ggml-rpc.h) - set(GGML_SOURCES_RPC ggml-rpc.cpp) -endif() - -if (GGML_VULKAN) - find_package(Vulkan COMPONENTS glslc REQUIRED) - - if (Vulkan_FOUND) - message(STATUS "Vulkan found") - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_VULKAN) - - # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build - # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector - if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) - endif() - - if (GGML_VULKAN_CHECK_RESULTS) - add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) - endif() - - if (GGML_VULKAN_DEBUG) - add_compile_definitions(GGML_VULKAN_DEBUG) - endif() - - if (GGML_VULKAN_MEMORY_DEBUG) - add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) - endif() - - if (GGML_VULKAN_SHADER_DEBUG_INFO) - add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) - endif() - - if (GGML_VULKAN_PERF) - add_compile_definitions(GGML_VULKAN_PERF) - endif() - - if (GGML_VULKAN_VALIDATE) - add_compile_definitions(GGML_VULKAN_VALIDATE) - endif() - - if (GGML_VULKAN_RUN_TESTS) - add_compile_definitions(GGML_VULKAN_RUN_TESTS) - endif() - - add_subdirectory(vulkan-shaders) - - set (_ggml_vk_genshaders_cmd vulkan-shaders-gen) - set (_ggml_vk_header ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp) - set (_ggml_vk_source ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp) - set (_ggml_vk_input_dir ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders) - set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv) - - file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp") - - add_custom_command( - OUTPUT ${_ggml_vk_header} - ${_ggml_vk_source} - - COMMAND ${_ggml_vk_genshaders_cmd} - --glslc ${Vulkan_GLSLC_EXECUTABLE} - --input-dir ${_ggml_vk_input_dir} - --output-dir ${_ggml_vk_output_dir} - --target-hpp ${_ggml_vk_header} - --target-cpp ${_ggml_vk_source} - --no-clean - - DEPENDS ${_ggml_vk_shader_deps} - COMMENT "Generate vulkan shaders" - ) - - set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header}) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source}) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE Vulkan::Vulkan) - list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR}) - else() - message(WARNING "Vulkan not found") - endif() -endif() - -if (GGML_KOMPUTE) - add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) - - find_package(Vulkan COMPONENTS glslc REQUIRED) - find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) - - if (NOT glslc_executable) - message(FATAL_ERROR "glslc not found") - endif() - - function(compile_shader) - set(options) - set(oneValueArgs) - set(multiValueArgs SOURCES) - cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - foreach(source ${compile_shader_SOURCES}) - get_filename_component(filename ${source} NAME) - set(spv_file ${filename}.spv) - add_custom_command( - OUTPUT ${spv_file} - DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp - COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} - COMMENT "Compiling ${source} to ${spv_file}" - ) - - get_filename_component(RAW_FILE_NAME ${spv_file} NAME) - set(FILE_NAME "shader${RAW_FILE_NAME}") - string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) - string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) - string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") - set(OUTPUT_HEADER_FILE "${HEADER_FILE}") - message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") - if(CMAKE_GENERATOR MATCHES "Visual Studio") - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" - ) - else() - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" - ) - endif() - endforeach() - endfunction() - - if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") - message(STATUS "Kompute found") - set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level") - add_subdirectory(kompute) - - # Compile our shaders - compile_shader(SOURCES - kompute-shaders/op_scale.comp - kompute-shaders/op_scale_8.comp - kompute-shaders/op_add.comp - kompute-shaders/op_addrow.comp - kompute-shaders/op_mul.comp - kompute-shaders/op_silu.comp - kompute-shaders/op_relu.comp - kompute-shaders/op_gelu.comp - kompute-shaders/op_softmax.comp - kompute-shaders/op_norm.comp - kompute-shaders/op_rmsnorm.comp - kompute-shaders/op_diagmask.comp - kompute-shaders/op_mul_mat_mat_f32.comp - kompute-shaders/op_mul_mat_f16.comp - kompute-shaders/op_mul_mat_q8_0.comp - kompute-shaders/op_mul_mat_q4_0.comp - kompute-shaders/op_mul_mat_q4_1.comp - kompute-shaders/op_mul_mat_q6_k.comp - kompute-shaders/op_getrows_f32.comp - kompute-shaders/op_getrows_f16.comp - kompute-shaders/op_getrows_q4_0.comp - kompute-shaders/op_getrows_q4_1.comp - kompute-shaders/op_getrows_q6_k.comp - kompute-shaders/op_rope_f16.comp - kompute-shaders/op_rope_f32.comp - kompute-shaders/op_cpy_f16_f16.comp - kompute-shaders/op_cpy_f16_f32.comp - kompute-shaders/op_cpy_f32_f16.comp - kompute-shaders/op_cpy_f32_f32.comp - ) - - # Create a custom target for our generated shaders - add_custom_target(generated_shaders DEPENDS - shaderop_scale.h - shaderop_scale_8.h - shaderop_add.h - shaderop_addrow.h - shaderop_mul.h - shaderop_silu.h - shaderop_relu.h - shaderop_gelu.h - shaderop_softmax.h - shaderop_norm.h - shaderop_rmsnorm.h - shaderop_diagmask.h - shaderop_mul_mat_mat_f32.h - shaderop_mul_mat_f16.h - shaderop_mul_mat_q8_0.h - shaderop_mul_mat_q4_0.h - shaderop_mul_mat_q4_1.h - shaderop_mul_mat_q6_k.h - shaderop_getrows_f32.h - shaderop_getrows_f16.h - shaderop_getrows_q4_0.h - shaderop_getrows_q4_1.h - shaderop_getrows_q6_k.h - shaderop_rope_f16.h - shaderop_rope_f32.h - shaderop_cpy_f16_f16.h - shaderop_cpy_f16_f32.h - shaderop_cpy_f32_f16.h - shaderop_cpy_f32_f32.h - ) - - # Create a custom command that depends on the generated_shaders - add_custom_command( - OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp - COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp - DEPENDS generated_shaders - COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp" - ) - - # Add the stamp to the main sources to ensure dependency tracking - set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - set(GGML_HEADERS_KOMPUTE ../include/ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_KOMPUTE) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE kompute) - list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR}) - else() - message(WARNING "Kompute not found") - endif() -endif() - -if (GGML_CPU_HBM) - find_library(memkind memkind REQUIRED) - - message(STATUS "Using memkind for CPU HBM") - - add_compile_definitions(GGML_USE_CPU_HBM) - - target_link_libraries(ggml PUBLIC memkind) -endif() - -if (GGML_CANN) - if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME}) - set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME}) - message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}") - endif() - - if (CANN_INSTALL_DIR) - # Only Support Linux. - if (GGML_CANN) - if (NOT UNIX) - set(GGML_CANN OFF) - message(WARNING "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}. Turning off GGML_CANN") - endif() - endif() - - # Supported platforms: x86-64, arm64 - if (GGML_CANN) - if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") - elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64") - else() - set(GGML_CANN OFF) - message(WARNING "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}. Turning off GGML_CANN") - endif() - endif() - - # Set header and libs - if(GGML_CANN) - set(CANN_INCLUDE_DIRS - ${CANN_INSTALL_DIR}/include - ${CANN_INSTALL_DIR}/include/aclnn - ${CANN_INSTALL_DIR}/acllib/include - ) - - add_subdirectory(ggml-cann/kernels) - list(APPEND CANN_LIBRARIES - ascendcl - nnopbase - opapi - acl_op_compiler - ascendc_kernels - ) - - set(GGML_HEADERS_CANN "../include/ggml-cann.h") - file(GLOB GGML_SOURCES_CANN "ggml-cann/*.cpp") - list(APPEND GGML_SOURCES_CANN "ggml-cann.cpp") - - message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}") - message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}") - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${CANN_LIBRARIES} ) - list(APPEND GGML_EXTRA_INCLUDES ${CANN_INCLUDE_DIRS}) - list(APPEND GGML_EXTRA_LIBDIRS ${CANN_INSTALL_DIR}/lib64) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN) - endif() - else() - set(GGML_CANN OFF) - message(WARNING "CANN: Can't find CANN_INSTALL_DIR, do you forget to source set_var.sh. Turning off GGML_CANN") - endif() - - if(NOT GGML_CANN) - message(WARNING "CANN: GGML_CANN is turned OFF, see above for details.") - endif() -endif() - function(get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -940,12 +41,6 @@ function(get_flags CCID CCVER) elseif (CCID STREQUAL "GNU") set(C_FLAGS -Wdouble-promotion) set(CXX_FLAGS -Wno-array-bounds) - - if (NOT GGML_MUSA) - if (CCVER VERSION_GREATER_EQUAL 7.1.0) - list(APPEND CXX_FLAGS -Wno-format-truncation) - endif() - endif() if (CCVER VERSION_GREATER_EQUAL 8.1.0) list(APPEND CXX_FLAGS -Wextra-semi) endif() @@ -985,54 +80,6 @@ if (GGML_ALL_WARNINGS) endif() endif() -set(CUDA_CXX_FLAGS "") - -if (GGML_CUDA) - set(CUDA_FLAGS -use_fast_math) - - if (GGML_FATAL_WARNINGS) - list(APPEND CUDA_FLAGS -Werror all-warnings) - endif() - - if (GGML_ALL_WARNINGS AND NOT MSVC) - set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) - if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") - list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) - endif() - - execute_process( - COMMAND ${NVCC_CMD} -Xcompiler --version - OUTPUT_VARIABLE CUDA_CCFULLVER - ERROR_QUIET - ) - - if (NOT CUDA_CCFULLVER MATCHES clang) - set(CUDA_CCID "GNU") - execute_process( - COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" - OUTPUT_VARIABLE CUDA_CCVER - ERROR_QUIET - ) - else() - if (CUDA_CCFULLVER MATCHES Apple) - set(CUDA_CCID "AppleClang") - else() - set(CUDA_CCID "Clang") - endif() - string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) - endif() - - message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") - - get_flags(${CUDA_CCID} ${CUDA_CCVER}) - list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later - endif() - - if (NOT MSVC) - list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) - endif() -endif() - if (GGML_LTO) include(CheckIPOSupported) check_ipo_supported(RESULT result OUTPUT output) @@ -1090,168 +137,6 @@ if (NOT MSVC) endif() endif() -set(ARCH_FLAGS "") - -if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR - CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR - (NOT CMAKE_OSX_ARCHITECTURES AND - NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) - - message(STATUS "ARM detected") - - if (MSVC) - add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead - add_compile_definitions(__ARM_NEON) - add_compile_definitions(__ARM_FEATURE_FMA) - - set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) - string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - add_compile_definitions(__ARM_FEATURE_DOTPROD) - endif () - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) - - if (GGML_COMPILER_SUPPORT_MATMUL_INT8) - add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) - endif () - - check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - endif () - - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) - else() - check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) - if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") - list(APPEND ARCH_FLAGS -mfp16-format=ieee) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") - # Raspberry Pi 1, Zero - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") - if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") - # Android armeabi-v7a - list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) - else() - # Raspberry Pi 2 - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) - endif() - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") - # Android arm64-v8a - # Raspberry Pi 3, 4, Zero 2 (32-bit) - list(APPEND ARCH_FLAGS -mno-unaligned-access) - endif() - if (GGML_SVE) - list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) - endif() - endif() -elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR - (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) - message(STATUS "x86 detected") - if (MSVC) - # instruction set detection for MSVC only - if (GGML_NATIVE) - # TODO: improve, should not reference files from the parent folder - include(../cmake/FindSIMD.cmake) - endif () - if (GGML_AVX512) - list(APPEND ARCH_FLAGS /arch:AVX512) - # MSVC has no compile-time flags enabling specific - # AVX512 extensions, neither it defines the - # macros corresponding to the extensions. - # Do it manually. - if (GGML_AVX512_VBMI) - add_compile_definitions($<$:__AVX512VBMI__>) - add_compile_definitions($<$:__AVX512VBMI__>) - endif() - if (GGML_AVX512_VNNI) - add_compile_definitions($<$:__AVX512VNNI__>) - add_compile_definitions($<$:__AVX512VNNI__>) - endif() - if (GGML_AVX512_BF16) - add_compile_definitions($<$:__AVX512BF16__>) - add_compile_definitions($<$:__AVX512BF16__>) - endif() - elseif (GGML_AVX2) - list(APPEND ARCH_FLAGS /arch:AVX2) - elseif (GGML_AVX) - list(APPEND ARCH_FLAGS /arch:AVX) - endif() - else() - if (GGML_NATIVE) - list(APPEND ARCH_FLAGS -march=native) - endif() - if (GGML_F16C) - list(APPEND ARCH_FLAGS -mf16c) - endif() - if (GGML_FMA) - list(APPEND ARCH_FLAGS -mfma) - endif() - if (GGML_AVX) - list(APPEND ARCH_FLAGS -mavx) - endif() - if (GGML_AVX2) - list(APPEND ARCH_FLAGS -mavx2) - endif() - if (GGML_AVX512) - list(APPEND ARCH_FLAGS -mavx512f) - list(APPEND ARCH_FLAGS -mavx512dq) - list(APPEND ARCH_FLAGS -mavx512bw) - endif() - if (GGML_AVX512_VBMI) - list(APPEND ARCH_FLAGS -mavx512vbmi) - endif() - if (GGML_AVX512_VNNI) - list(APPEND ARCH_FLAGS -mavx512vnni) - endif() - if (GGML_AVX512_BF16) - list(APPEND ARCH_FLAGS -mavx512bf16) - endif() - endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") - message(STATUS "PowerPC detected") - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") - list(APPEND ARCH_FLAGS -mcpu=powerpc64le) - else() - list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) - #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) - endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") - message(STATUS "loongarch64 detected") - - list(APPEND ARCH_FLAGS -march=loongarch64) - if (GGML_LASX) - list(APPEND ARCH_FLAGS -mlasx) - endif() - if (GGML_LSX) - list(APPEND ARCH_FLAGS -mlsx) - endif() -else() - message(STATUS "Unknown architecture") -endif() - -add_compile_options("$<$:${ARCH_FLAGS}>") -add_compile_options("$<$:${ARCH_FLAGS}>") - -if (GGML_CUDA) - list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS}) - list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument - - if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") - list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) - endif() - - add_compile_options("$<$:${CUDA_FLAGS}>") -endif() - if (MINGW) # Target Windows 8 for PrefetchVirtualMemory add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER}) @@ -1265,14 +150,14 @@ endif() # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # posix_memalign came in POSIX.1-2001 / SUSv3 # M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985) -add_compile_definitions(_XOPEN_SOURCE=600) # Somehow in OpenBSD whenever POSIX conformance is specified # some string functions rely on locale_t availability, # which was introduced in POSIX.1-2008, forcing us to go higher if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") - remove_definitions(-D_XOPEN_SOURCE=600) add_compile_definitions(_XOPEN_SOURCE=700) +else() + add_compile_definitions(_XOPEN_SOURCE=600) endif() # Data types, macros and functions related to controlling CPU affinity and @@ -1315,63 +200,89 @@ if (WIN32) endif() endif() -# -# libraries -# - # ggml -add_library(ggml +add_library(ggml-base ../include/ggml.h ../include/ggml-alloc.h ../include/ggml-backend.h + ../include/ggml-cpp.h + ../include/ggml-opt.h ggml.c ggml-alloc.c ggml-backend.cpp + ggml-opt.cpp + ggml-threading.cpp + ggml-threading.h ggml-quants.c ggml-quants.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} - ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} - ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} - ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} - ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} - ${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS} - ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} - ${GGML_SOURCES_CANN} ${GGML_HEADERS_CANN} - ggml-aarch64.c ggml-aarch64.h - ) + ggml-aarch64.c + ggml-aarch64.h) -if (EMSCRIPTEN) - set_target_properties(ggml PROPERTIES COMPILE_FLAGS "-msimd128") -endif() +target_include_directories(ggml-base PRIVATE .) -target_compile_definitions(ggml PUBLIC ${GGML_CDEF_PUBLIC}) -target_include_directories(ggml PUBLIC ../include) -target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES}) -target_link_directories (ggml PRIVATE ${GGML_EXTRA_LIBDIRS}) -target_compile_features (ggml PRIVATE c_std_11) # don't bump +add_library(ggml + ggml-backend-reg.cpp) -list(APPEND GGML_EXTRA_LIBS_PRIVATE Threads::Threads) +target_link_libraries(ggml PUBLIC ggml-base) + +function(ggml_add_backend backend) + string(TOUPPER "GGML_${backend}" backend_id) + if (${backend_id}) + string(TOLOWER "ggml-${backend}" backend_target) + add_subdirectory(${backend_target}) + # check again in case the backend disabled itself + # note that this should NOT be the normal behavior, in case of errors the backend should fail the build + # however, currently it is necessary for AMX, since it is enabled by default on llama.cpp + if (${backend_id}) + message(STATUS "Including ${backend} backend") + if (${BUILD_SHARED_LIBS}) + target_compile_definitions(${backend_target} PRIVATE GGML_BACKEND_BUILD) + target_compile_definitions(${backend_target} PUBLIC GGML_BACKEND_SHARED) + endif() + install(TARGETS ${backend_target} LIBRARY) + target_link_libraries(ggml PUBLIC ${backend_target}) + string(TOUPPER "GGML_USE_${backend}" backend_use) + target_compile_definitions(ggml PUBLIC ${backend_use}) + endif() + endif() +endfunction() + +ggml_add_backend(CPU) +ggml_add_backend(AMX) +ggml_add_backend(BLAS) +ggml_add_backend(CANN) +ggml_add_backend(CUDA) +ggml_add_backend(HIP) +ggml_add_backend(Kompute) +ggml_add_backend(METAL) +ggml_add_backend(RPC) +ggml_add_backend(SYCL) +ggml_add_backend(Vulkan) +ggml_add_backend(MUSA) + +foreach (target ggml-base ggml) + target_include_directories(${target} PUBLIC $ $) + target_compile_features (${target} PRIVATE c_std_11) # don't bump +endforeach() + +target_link_libraries(ggml-base PRIVATE Threads::Threads) find_library(MATH_LIBRARY m) if (MATH_LIBRARY) - if (NOT WIN32 OR NOT GGML_SYCL) - list(APPEND GGML_EXTRA_LIBS_PRIVATE m) + if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT}) + target_link_libraries(ggml-base PRIVATE m) endif() endif() if (CMAKE_SYSTEM_NAME MATCHES "Android") - list(APPEND GGML_EXTRA_LIBS_PRIVATE dl) # Must be linked explicitly + target_link_libraries(ggml-base PRIVATE dl) endif() -list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PRIVATE) -list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PUBLIC) -target_link_libraries(ggml PRIVATE ${GGML_EXTRA_LIBS_PRIVATE} PUBLIC ${GGML_EXTRA_LIBS_PUBLIC}) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(ggml PRIVATE GGML_SHARED GGML_BUILD) + foreach (target ggml-base ggml) + set_target_properties(${target} PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_compile_definitions(${target} PRIVATE GGML_BUILD) + target_compile_definitions(${target} PUBLIC GGML_SHARED) + endforeach() endif() diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c index b27f411474..0139120519 100644 --- a/ggml/src/ggml-aarch64.c +++ b/ggml/src/ggml-aarch64.c @@ -1,204 +1,49 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Limited and/or its affiliates -// SPDX-License-Identifier: MIT -// - -#define GGML_COMMON_IMPL_C +#define GGML_COMMON_DECL_C #include "ggml-common.h" -#include "ggml-quants.h" -#include "ggml-impl.h" -#include "ggml-cpu-impl.h" - -#include -#include -#include -#include -#include // for qsort -#include // for GGML_ASSERT - #include "ggml-aarch64.h" - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Woverlength-strings" -#elif defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif +#include "ggml-impl.h" +#include "ggml-quants.h" +#include #define UNUSED GGML_UNUSED -// Functions to create the interleaved data layout formats - -// interleave 4 block_q4_0s in blocks of blck_size_interleave -// returns an interleaved block_q4_0x4 -// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks -// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave -// -// - in : an array of block_q4_0 pointers -// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of -// blck_size_interleave bytes -// - xor_mask : the mask to convert the nibbles in block_q4_0 quants bytes -// from bias offset form to pure sign form (this saves subtract -// operations durin unpacking) -// -#if defined(__AVX__) -#if defined(__F16C__) -#if defined(__AVX512F__) -#define GGML_F32Cx8x2_LOAD(x, y) _mm512_cvtph_ps(_mm256_set_m128i(_mm_loadu_si128((const __m128i *)(y)), _mm_loadu_si128((const __m128i *)(x)))) -#define GGML_F32Cx16_REPEAT_LOAD(x) _mm512_cvtph_ps(_mm256_set_m128i(x, x)) -#endif -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) _mm256_cvtph_ps(_mm_shuffle_epi32(_mm_maskload_epi32((int const*)(x), loadMask), 68)) -#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) _mm256_cvtph_ps(_mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)) -#else -#if defined(__AVX512F__) -static inline __m512 __avx512_f32cx8x2_load(ggml_fp16_t *x, ggml_fp16_t *y) { - float tmp[16]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - for (int i = 0; i < 8; i++) { - tmp[i + 8] = GGML_FP16_TO_FP32(y[i]); - } - - return _mm512_loadu_ps(tmp); -} -static inline __m512 __avx512_repeat_f32cx16_load(__m128i x) { - float tmp[16]; - uint16_t tmphalf[8]; - _mm_storeu_si128((__m128i*)tmphalf, x); - - for (int i = 0; i < 4; i++) { - tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 4] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 8] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 12] = GGML_FP16_TO_FP32(tmphalf[i]); - } - - return _mm512_loadu_ps(tmp); -} -#endif -static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline __m256 __avx_repeat_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 4; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - tmp[i + 4] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline __m256 __avx_rearranged_f32cx8_load(ggml_fp16_t *x, __m128i arrangeMask) { - uint16_t tmphalf[8]; - float tmp[8]; - - _mm_storeu_si128((__m128i*)tmphalf, _mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)); - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); - } - - return _mm256_loadu_ps(tmp); -} - -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) __avx_repeat_f32cx8_load(x) -#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) __avx_rearranged_f32cx8_load(x, arrangeMask) -#if defined(__AVX512F__) -#define GGML_F32Cx8x2_LOAD(x, y) __avx512_f32cx8x2_load(x, y) -#define GGML_F32Cx16_REPEAT_LOAD(x) __avx512_repeat_f32cx16_load(x) -#endif -#endif -#endif - - -#if defined(__AVX2__) || defined(__AVX512F__) -#if defined(__AVX512F__) -// add int16_t pairwise and return as 512 bit int vector -static inline __m512i sum_i16_pairs_int_32x16(const __m512i x) { - const __m512i ones = _mm512_set1_epi16(1); - return _mm512_madd_epi16(ones, x); -} - -static inline __m512i mul_sum_us8_pairs_int32x16(const __m512i ax, const __m512i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m512i zero = _mm512_setzero_si512(); - return _mm512_dpbusd_epi32(zero, ax, sy); -#else - // Perform multiplication and create 16-bit values - const __m512i dot = _mm512_maddubs_epi16(ax, sy); - return sum_i16_pairs_int_32x16(dot); -#endif -} - -// multiply int8_t, add results pairwise twice and return as 512 bit int vector -static inline __m512i mul_sum_i8_pairs_int32x16(const __m512i x, const __m512i y) { - const __m512i zero = _mm512_setzero_si512(); - // Get absolute values of x vectors - const __m512i ax = _mm512_abs_epi8(x); - // Sign the values of the y vectors - __mmask64 blt0 = _mm512_movepi8_mask(x); - const __m512i sy = _mm512_mask_sub_epi8(y, blt0, zero, y); - return mul_sum_us8_pairs_int32x16(ax, sy); -} -#endif - -// add int16_t pairwise and return as 256 bit int vector -static inline __m256i sum_i16_pairs_int32x8(const __m256i x) { - const __m256i ones = _mm256_set1_epi16(1); - return _mm256_madd_epi16(ones, x); -} - -static inline __m256i mul_sum_us8_pairs_int32x8(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m256i zero = _mm256_setzero_si256(); - return _mm256_dpbusd_epi32(zero, ax, sy); -#else - // Perform multiplication and create 16-bit values - const __m256i dot = _mm256_maddubs_epi16(ax, sy); - return sum_i16_pairs_int32x8(dot); -#endif -} - -// Integer variant of the function defined in ggml-quants.c -// multiply int8_t, add results pairwise twice and return as 256 bit int vector -static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y) { -#if __AVXVNNIINT8__ - const __m256i zero = _mm256_setzero_si256(); - return _mm256_dpbssd_epi32(zero, x, y); -#else - // Get absolute values of x vectors - const __m256i ax = _mm256_sign_epi8(x, x); - // Sign the values of the y vectors - const __m256i sy = _mm256_sign_epi8(y, x); - return mul_sum_us8_pairs_int32x8(ax, sy); -#endif -} -#endif - -static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) { +static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { block_q4_0x4 out; for (int i = 0; i < 4; i++) { out.d[i] = in[i].d; } - for (int i = 0; i < QK4_0 * 2; i++) { - int src_offset = (i / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (i % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (i % blck_size_interleave); + const int end = QK4_0 * 2 / blck_size_interleave; - out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask; + if (blck_size_interleave == 8) { + const uint64_t xor_mask = 0x8888888888888888ULL; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + // Using memcpy to avoid unaligned memory accesses + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + const uint32_t xor_mask = 0x88888888; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint32_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); } return out; @@ -208,345 +53,30 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_in // returns an interleaved block_q4_0x8 // in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks // first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave -static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) { +static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) { block_q4_0x8 out; for (int i = 0; i < 8; i++) { out.d[i] = in[i].d; } - for (int i = 0; i < QK4_0 * 4; i++) { - int src_offset = (i / (8 * blck_size_interleave)) * blck_size_interleave; - int src_id = (i % (8 * blck_size_interleave)) / blck_size_interleave; - src_offset += (i % blck_size_interleave); + const int end = QK4_0 * 4 / blck_size_interleave; + const uint64_t xor_mask = 0x8888888888888888ULL; - out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask; + for (int i = 0; i < end; ++i) { + int src_id = i % 8; + int src_offset = (i / 8) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); } return out; } -void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; - -#if defined(__ARM_NEON) - float32x4_t srcv[4][8]; - float id[4]; - - for (int i = 0; i < nb; i++) { - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int row_iter = 0; row_iter < 4; row_iter++) { - for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); - - for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); - for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); - for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < 8; j++) { - float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]); - int32x4_t vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[1][j], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[2][j], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[3][j], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3); - } - } -#else - // scalar - const int blck_size_interleave = 4; - float srcv[4][QK8_0]; - float id[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; - amax = MAX(amax, fabsf(srcv[row_iter][j])); - } - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < QK8_0 * 4; j++) { - int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (j % blck_size_interleave); - - float x0 = srcv[src_id][src_offset] * id[src_id]; - y[i].qs[j] = roundf(x0); - } - } -#endif -} - -void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; - -#if defined(__ARM_NEON) - float32x4_t srcv[4][8]; - float id[4]; - - for (int i = 0; i < nb; i++) { - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int row_iter = 0; row_iter < 4; row_iter++) { - for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); - - for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); - for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); - for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < 4; j++) { - float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]); - int32x4_t vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[1][2 * j], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[2][2 * j], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[3][2 * j], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3); - } - } -#elif defined(__AVX2__) || defined(__AVX__) - float id[4]; - __m256 srcv[4][4]; - __m256 idvec[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x + row_iter * k + i * 32 ); - __m256 v1 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 8 ); - __m256 v2 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 16 ); - __m256 v3 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 24 ); - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Divided by 127.f to mirror results in quantize_row_q8_0 - const float d = maxScalar / 127.f; - id[row_iter] = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; //d ? 1.0f / d : 0.0f; - - // Store the scale for the individual block - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - - // Store the values in blocks of eight values - Aim is to use these later for block interleaving - srcv[row_iter][0] = v0; - srcv[row_iter][1] = v1; - srcv[row_iter][2] = v2; - srcv[row_iter][3] = v3; - idvec[row_iter] = _mm256_set1_ps(id[row_iter]); - } - - // The loop iterates four times - The aim is to get 4 corresponding chunks of eight bytes from the original weight blocks that are interleaved - for (int j = 0; j < 4; j++) { - // Apply the multiplier - __m256 v0 = _mm256_mul_ps(srcv[0][j], idvec[0]); - __m256 v1 = _mm256_mul_ps(srcv[1][j], idvec[1]); - __m256 v2 = _mm256_mul_ps(srcv[2][j], idvec[2]); - __m256 v3 = _mm256_mul_ps(srcv[3][j], idvec[3]); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); - i2 = _mm256_packs_epi32( i2, i3 ); - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); - - // Permute and store the quantized weights in the required order after the pack instruction - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)(y[i].qs + 32 * j), i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j + 16), ni4); -#endif - } - } -#else - // scalar - const int blck_size_interleave = 8; - float srcv[4][QK8_0]; - float id[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; - amax = MAX(amax, fabsf(srcv[row_iter][j])); - } - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < QK8_0 * 4; j++) { - int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (j % blck_size_interleave); - - float x0 = srcv[src_id][src_offset] * id[src_id]; - y[i].qs[j] = roundf(x0); - } - } -#endif -} - -void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) { - assert(nrow == 4); - UNUSED(nrow); - if (blck_size_interleave == 4) { - quantize_q8_0_4x4(x, vy, n_per_row); - } else if (blck_size_interleave == 8) { - quantize_q8_0_4x8(x, vy, n_per_row); - } else { - assert(false); - } -} - static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) { assert(n_per_row % QK4_0 == 0); const int nb = n_per_row / QK4_0; @@ -570,11 +100,11 @@ static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict ds } if (nrows_interleaved == 8) { - *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave, 0x88); + *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave); out_ptr = (block_q4_0x8 *) out_ptr + 1; } else if (nrows_interleaved == 4) { - *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave, 0x88); + *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave); out_ptr = (block_q4_0x4 *) out_ptr + 1; } } @@ -597,2613 +127,3 @@ size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_ UNUSED(quant_weights); return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8); } - -void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 4; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v31.16b, #0x4\n" - "movi v30.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x22, %x[a_ptr], #0x2\n" - "movi v29.16b, #0x0\n" - "mov x21, %x[nb]\n" - "2:" // Block loop - "ldr q28, [%x[b_ptr], #0x0]\n" - "ldr q27, [x22, #0x0]\n" - "movi v26.4s, #0x0\n" - "sub x20, x22, #0x2\n" - "ldr q25, [x22, #0x10]\n" - "ldr q24, [%x[b_ptr], #0x10]\n" - "sub x21, x21, #0x1\n" - "add x22, x22, #0x22\n" - "ldr q23, [%x[b_ptr], #0x20]\n" - "ldr q22, [%x[b_ptr], #0x30]\n" - "ld1r { v21.8h }, [x20]\n" - "ldr q20, [%x[b_ptr], #-0x8]\n" - "sshl v16.16b, v28.16b, v31.16b\n" - "and v28.16b, v28.16b, v30.16b\n" - "sshl v19.16b, v24.16b, v31.16b\n" - "and v24.16b, v24.16b, v30.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "sshl v18.16b, v23.16b, v31.16b\n" - "and v23.16b, v23.16b, v30.16b\n" - ".inst 0x4f9be21a // sdot v26.4s, v16.16b, v27.4b[0]\n" - "sshl v17.16b, v22.16b, v31.16b\n" - "and v22.16b, v22.16b, v30.16b\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v16.4s, v20.4h\n" - ".inst 0x4f99e39a // sdot v26.4s, v28.16b, v25.4b[0]\n" - "fmul v16.4s, v16.4s, v21.4s\n" - ".inst 0x4fbbe27a // sdot v26.4s, v19.16b, v27.4b[1]\n" - ".inst 0x4fb9e31a // sdot v26.4s, v24.16b, v25.4b[1]\n" - ".inst 0x4f9bea5a // sdot v26.4s, v18.16b, v27.4b[2]\n" - ".inst 0x4f99eafa // sdot v26.4s, v23.16b, v25.4b[2]\n" - ".inst 0x4fbbea3a // sdot v26.4s, v17.16b, v27.4b[3]\n" - ".inst 0x4fb9eada // sdot v26.4s, v22.16b, v25.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v29.4s, v26.4s, v16.4s\n" - "cbnz x21, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q29, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - float sumf[4]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } -} - -void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v2.16b, #0x4\n" - "movi v1.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x23, %x[a_ptr], #0x2\n" - "movi v0.16b, #0x0\n" - "mov x22, %x[nb]\n" - "2:" // Block loop - "ldr q31, [%x[b_ptr], #0x0]\n" - "ldr q30, [%x[b_ptr], #0x10]\n" - "mov x21, x23\n" - "movi v29.4s, #0x0\n" - "ldr q28, [%x[b_ptr], #0x20]\n" - "ldr q27, [%x[b_ptr], #0x30]\n" - "movi v26.4s, #0x0\n" - "sub x20, x23, #0x2\n" - "ld1r { v25.8h }, [x20]\n" - "ldr q24, [%x[b_ptr], #-0x8]\n" - "sub x22, x22, #0x1\n" - "add x23, x23, #0x22\n" - "ld1r { v23.2d }, [x21], #0x8\n" - "sshl v22.16b, v31.16b, v2.16b\n" - "sshl v16.16b, v30.16b, v2.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "ld1r { v21.2d }, [x21], #0x8\n" - "sshl v20.16b, v28.16b, v2.16b\n" - "sshl v19.16b, v27.16b, v2.16b\n" - "ld1r { v18.2d }, [x21], #0x8\n" - "ld1r { v17.2d }, [x21], #0x8\n" - "and v31.16b, v31.16b, v1.16b\n" - "and v30.16b, v30.16b, v1.16b\n" - ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" - ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" - "and v28.16b, v28.16b, v1.16b\n" - "and v27.16b, v27.16b, v1.16b\n" - "fcvtl v25.4s, v25.4h\n" - "fcvtl v16.4s, v24.4h\n" - ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" - ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" - "fmul v16.4s, v16.4s, v25.4s\n" - ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" - ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" - ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" - ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" - "addp v29.4s, v29.4s, v26.4s\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v0.4s, v29.4s, v16.4s\n" - "cbnz x22, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q0, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - float sumf[4]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } -} - -void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) -#if defined(__ARM_FEATURE_SVE) - if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "ptrue p0.b\n" - "add %x[b_ptr], %x[b_ptr], #0x10\n" - "1:" // Column loop - "add x22, %x[a_ptr], #0x2\n" - "mov z31.b, #0x0\n" - "mov x21, %x[nb]\n" - "2:" // Block loop - "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n" - "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n" - "mov z28.s, #0x0\n" - "mov z27.s, #0x0\n" - "ld1rd { z26.d }, p0/Z, [x22]\n" - "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n" - "sub x20, x22, #0x2\n" - "sub x21, x21, #0x1\n" - "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n" - "ld1rd { z23.d }, p0/Z, [x22, #8]\n" - "lsl z22.b, z30.b, #0x4\n" - "lsl z16.b, z29.b, #0x4\n" - "and z30.b, z30.b, #0xf0\n" - "and z29.b, z29.b, #0xf0\n" - "ld1rd { z21.d }, p0/Z, [x22, #16]\n" - "ld1rd { z20.d }, p0/Z, [x22, #24]\n" - "lsl z19.b, z25.b, #0x4\n" - "and z25.b, z25.b, #0xf0\n" - "ld1rh { z17.h }, p0/Z, [x20]\n" - "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n" - "sdot z28.s, z22.b, z26.b\n" - "sdot z27.s, z16.b, z26.b\n" - "lsl z16.b, z24.b, #0x4\n" - "add x22, x22, #0x22\n" - "and z24.b, z24.b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x90\n" - "fcvt z17.s, p0/m, z17.h\n" - "fcvt z18.s, p0/m, z18.h\n" - "sdot z28.s, z19.b, z23.b\n" - "sdot z27.s, z16.b, z23.b\n" - "fmul z18.s, z18.s, z17.s\n" - "sdot z28.s, z30.b, z21.b\n" - "sdot z27.s, z29.b, z21.b\n" - "sdot z28.s, z25.b, z20.b\n" - "sdot z27.s, z24.b, z20.b\n" - "uzp1 z17.s, z28.s, z27.s\n" - "uzp2 z16.s, z28.s, z27.s\n" - "add z17.s, z17.s, z16.s\n" - "asr z17.s, z17.s, #0x4\n" - "scvtf z17.s, p0/m, z17.s\n" - "fmla z31.s, p0/M, z17.s, z18.s\n" - "cbnz x21, 2b\n" - "sub %x[nc], %x[nc], #0x8\n" - "st1w { z31.s }, p0, [%x[res_ptr]]\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" - ); - return; - } -#endif // #if defined(__ARM_FEATURE_SVE) -#elif defined(__AVX2__) - // Lookup table to convert signed nibbles to signed bytes - __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); - signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); - __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0); - __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0); - - // Permute mask used for easier vector processing at later stages - const __m256i m4b = _mm256_set1_epi8(0x0F); - - int64_t b_nb = n / QK4_0; - - const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; - const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy; - - // Process Q8_0 blocks one by one - for (int64_t y = 0; y < nr; y++) { - - // Pointers to LHS blocks of block_q8_0 format - const block_q8_0 * a_ptr = a_ptr_start + (y * nb); - - // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < nc / 8; x++) { - - // Pointers to RHS blocks - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulator - __m256 acc_row = _mm256_setzero_ps(); - - for (int64_t b = 0; b < nb; b++) { - // Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7) - const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1); - const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2); - const __m256i rhs_raw_vec_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 3); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_vec_0123_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_0, m4b)); // B0(0-7) B1(0-7) B2(0-7) B3(0-7) - const __m256i rhs_vec_4567_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_0, m4b)); // B4(0-7) B5(0-7) B6(0-7) B7(0-7) - const __m256i rhs_vec_0123_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) - const __m256i rhs_vec_4567_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) - - const __m256i rhs_vec_0123_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_0, 4), m4b)); // B0(16-23) B1(16-23) B2(16-23) B3(16-23) - const __m256i rhs_vec_4567_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_0, 4), m4b)); // B4(16-23) B5(16-23) B6(16-23) B7(16-23) - const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31) - const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31) - - // Load the scale values for the 8 blocks interleaved in block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask); - - // Load and convert to FP32 scale from block_q8_0 - const __m256 row_scale_f32 = _mm256_set1_ps(GGML_FP16_TO_FP32(a_ptr[b].d)); - - // Load the block values in block_q8_0 in batches of 16 bytes and replicate the same across 256 bit vector - __m256i lhs_vec_0 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)a_ptr[b].qs)); - __m256i lhs_vec_1 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)(a_ptr[b].qs + 16))); - - lhs_vec_0 = _mm256_permute2f128_si256(lhs_vec_0, lhs_vec_0, 0); // A0 (0-15) A0(0-15) - lhs_vec_1 = _mm256_permute2f128_si256(lhs_vec_1, lhs_vec_1, 0); // A0 (16-31) A0(16-31)) - - __m256i iacc = _mm256_setzero_si256(); - - // Dot product done within 32 bit lanes and accumulated in the same vector - // B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3) - // B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7) - // ........................................................................... - // B0(28-31) B4(28-31) B1(28-31) B5(28-31) B2(28-31) B6(28-31) B3(28-31) B7(28-31) with A0(28-31) - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_0 ,_mm256_shuffle_epi32(rhs_vec_4567_0, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 0))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_0, 177) ,rhs_vec_4567_0, 170), _mm256_shuffle_epi32(lhs_vec_0, 85))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_1 ,_mm256_shuffle_epi32(rhs_vec_4567_1, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 170))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_1, 177) ,rhs_vec_4567_1, 170), _mm256_shuffle_epi32(lhs_vec_0, 255))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_2 ,_mm256_shuffle_epi32(rhs_vec_4567_2, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 0))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_2, 177) ,rhs_vec_4567_2, 170), _mm256_shuffle_epi32(lhs_vec_1, 85))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255))); - - // Accumulated values multipled with appropriate scales - acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row); - } - - // Accumulated output values permuted so as to be stored in appropriate order post accumulation - acc_row = _mm256_permutevar8x32_ps(acc_row, finalpermutemask); - _mm256_storeu_ps(s + (y * nr + x * 8), acc_row); - } - } - return; -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) - { - float sumf[8]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } - } -} - -void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 4; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v23.16b, #0x0\n" - "movi v16.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v0.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v21.16b, #0x0\n" - "movi v8.16b, #0x0\n" - "movi v1.16b, #0x0\n" - "3:" // Block loop - "ldr q3, [x28, #0x0]\n" - "ldr q31, [x25, #0x0]\n" - "movi v28.16b, #0x4\n" - "movi v10.4s, #0x0\n" - "ldr q22, [x28, #0x10]\n" - "ldr q6, [x25, #0x10]\n" - "movi v29.4s, #0x0\n" - "movi v9.4s, #0x0\n" - "ldr q27, [x28, #0x20]\n" - "ldr q30, [x28, #0x30]\n" - "movi v20.4s, #0x0\n" - "movi v24.16b, #0xf0\n" - "ldr d2, [x25, #-0x8]\n" - "ldr d26, [x23, #-0x8]\n" - "sshl v12.16b, v3.16b, v28.16b\n" - "sub x20, x28, #0x8\n" - "ldr d17, [x20, #0x0]\n" - "and v3.16b, v3.16b, v24.16b\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" - ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" - ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" - ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" - "sshl v31.16b, v22.16b, v28.16b\n" - "and v22.16b, v22.16b, v24.16b\n" - "fcvtl v17.4s, v17.4h\n" - "fcvtl v2.4s, v2.4h\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" - ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" - ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" - ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" - "sshl v6.16b, v27.16b, v28.16b\n" - "sshl v28.16b, v30.16b, v28.16b\n" - "and v27.16b, v27.16b, v24.16b\n" - "and v30.16b, v30.16b, v24.16b\n" - "ldr q24, [x25, #0x20]\n" - ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x30]\n" - ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" - ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" - ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" - ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x40]\n" - ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x50]\n" - ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" - ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" - ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" - ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x60]\n" - ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" - ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" - ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" - ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" - "fmul v24.4s, v17.4s, v2.s[0]\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v15.4s, v10.4s, v24.4s\n" - "ldr q24, [x23, #0x0]\n" - "fmul v10.4s, v17.4s, v2.s[1]\n" - "fmla v19.4s, v29.4s, v10.4s\n" - "ldr q10, [x23, #0x10]\n" - "fmul v29.4s, v17.4s, v2.s[2]\n" - "fmul v2.4s, v17.4s, v2.s[3]\n" - "fmla v18.4s, v9.4s, v29.4s\n" - "movi v9.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" - "fmla v14.4s, v20.4s, v2.4s\n" - "movi v20.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x20]\n" - ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" - ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" - ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" - ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x30]\n" - ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x40]\n" - ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" - ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" - ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" - ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x50]\n" - ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x60]\n" - ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" - ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" - ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" - ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x0]\n" - ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" - ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" - ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" - ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" - "fmul v10.4s, v17.4s, v26.s[0]\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v11.4s, v9.4s, v10.4s\n" - "ldr q9, [x22, #0x10]\n" - "fmul v10.4s, v17.4s, v26.s[1]\n" - "fmla v13.4s, v29.4s, v10.4s\n" - "ldr d29, [x22, #-0x8]\n" - "fmul v10.4s, v17.4s, v26.s[2]\n" - "fmul v26.4s, v17.4s, v26.s[3]\n" - "fcvtl v29.4s, v29.4h\n" - "fmla v23.4s, v20.4s, v10.4s\n" - "movi v20.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v16.4s, v2.4s, v26.4s\n" - "movi v26.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x20]\n" - ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x30]\n" - ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x40]\n" - ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x50]\n" - ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x60]\n" - ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x21, #0x0]\n" - ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" - ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" - ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" - "fmul v9.4s, v17.4s, v29.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v25.4s, v20.4s, v9.4s\n" - "ldr q9, [x21, #0x10]\n" - "fmul v20.4s, v17.4s, v29.s[1]\n" - "fmla v7.4s, v10.4s, v20.4s\n" - "ldr d20, [x21, #-0x8]\n" - "fmul v10.4s, v17.4s, v29.s[2]\n" - "fmul v29.4s, v17.4s, v29.s[3]\n" - "fcvtl v20.4s, v20.4h\n" - "fmla v0.4s, v26.4s, v10.4s\n" - "movi v26.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v4.4s, v2.4s, v29.4s\n" - "movi v2.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" - "ldr q12, [x21, #0x20]\n" - "fmul v24.4s, v17.4s, v20.s[0]\n" - ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x30]\n" - "fmul v31.4s, v17.4s, v20.s[1]\n" - ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" - ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" - ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" - ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x40]\n" - "fmul v6.4s, v17.4s, v20.s[2]\n" - "fmul v20.4s, v17.4s, v20.s[3]\n" - ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x50]\n" - ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" - ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" - ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" - ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x60]\n" - ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" - "ldr q17, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" - ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" - ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" - ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" - ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" - ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" - ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" - ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "fmla v5.4s, v26.4s, v24.4s\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v21.4s, v10.4s, v31.4s\n" - "fmla v8.4s, v2.4s, v6.4s\n" - "fmla v1.4s, v29.4s, v20.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q16, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q0, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q21, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q8, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q1, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q7, [x24, #0x0]\n" - "ldr q5, [x25, #0x0]\n" - "movi v9.16b, #0x4\n" - "movi v4.4s, #0x0\n" - "ldr q3, [x24, #0x10]\n" - "ldr q2, [x25, #0x10]\n" - "movi v1.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q13, [x24, #0x20]\n" - "ldr q31, [x25, #0x20]\n" - "movi v30.4s, #0x0\n" - "movi v29.16b, #0xf0\n" - "ldr q28, [x24, #0x30]\n" - "ldr q27, [x25, #0x30]\n" - "sshl v20.16b, v7.16b, v9.16b\n" - "sub x20, x24, #0x8\n" - "ldr q26, [x25, #0x40]\n" - "ldr q25, [x25, #0x50]\n" - "sshl v17.16b, v3.16b, v9.16b\n" - "and v7.16b, v7.16b, v29.16b\n" - "ldr q24, [x25, #0x60]\n" - "ldr q16, [x25, #0x70]\n" - "sshl v22.16b, v13.16b, v9.16b\n" - "and v3.16b, v3.16b, v29.16b\n" - "ldr d21, [x20, #0x0]\n" - "ldr d12, [x25, #-0x8]\n" - ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" - ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" - ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" - ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" - "sshl v9.16b, v28.16b, v9.16b\n" - "subs x21, x21, #0x1\n" - "and v13.16b, v13.16b, v29.16b\n" - "and v28.16b, v28.16b, v29.16b\n" - "add x25, x25, #0x88\n" - "add x24, x24, #0x48\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v12.4s, v12.4h\n" - ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" - ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" - ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" - ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" - "fmul v11.4s, v21.4s, v12.s[0]\n" - "fmul v23.4s, v21.4s, v12.s[1]\n" - "fmul v17.4s, v21.4s, v12.s[2]\n" - ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" - "fmul v6.4s, v21.4s, v12.s[3]\n" - ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" - ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" - ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" - ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" - ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" - ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" - ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" - ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" - ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" - ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" - ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" - ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" - ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" - ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" - ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" - ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" - ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" - ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" - ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" - ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" - ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" - ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" - ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" - "scvtf v4.4s, v4.4s, #0x4\n" - "scvtf v1.4s, v1.4s, #0x4\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "fmla v15.4s, v4.4s, v11.4s\n" - "scvtf v30.4s, v30.4s, #0x4\n" - "fmla v19.4s, v1.4s, v23.4s\n" - "fmla v18.4s, v0.4s, v17.4s\n" - "fmla v14.4s, v30.4s, v6.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q14, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - { - float sumf[4][4]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } - } -} - -void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v22.16b, #0x0\n" - "movi v23.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v6.16b, #0x0\n" - "movi v30.16b, #0x0\n" - "movi v24.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "3:" // Block loop - "ldr q21, [x28, #0x0]\n" - "ldr q16, [x28, #0x10]\n" - "movi v1.16b, #0x4\n" - "movi v19.4s, #0x0\n" - "ldr q27, [x25, #0x0]\n" - "ldr q15, [x25, #0x10]\n" - "movi v26.4s, #0x0\n" - "movi v18.4s, #0x0\n" - "ldr q29, [x28, #0x20]\n" - "ldr q3, [x28, #0x30]\n" - "movi v17.4s, #0x0\n" - "movi v0.16b, #0xf0\n" - "ldr d20, [x25, #-0x8]\n" - "ldr d9, [x23, #-0x8]\n" - "sshl v8.16b, v21.16b, v1.16b\n" - "sshl v31.16b, v16.16b, v1.16b\n" - "and v21.16b, v21.16b, v0.16b\n" - "and v16.16b, v16.16b, v0.16b\n" - "sub x20, x28, #0x8\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" - ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" - "ldr q27, [x25, #0x20]\n" - ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" - ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" - "sshl v15.16b, v29.16b, v1.16b\n" - "sshl v1.16b, v3.16b, v1.16b\n" - "and v29.16b, v29.16b, v0.16b\n" - "and v3.16b, v3.16b, v0.16b\n" - "ldr q0, [x25, #0x30]\n" - "fcvtl v20.4s, v20.4h\n" - ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" - "fcvtl v9.4s, v9.4h\n" - ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" - "ldr q27, [x25, #0x40]\n" - ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" - ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" - "ldr q0, [x25, #0x50]\n" - ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" - ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" - "ldr q27, [x25, #0x60]\n" - ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" - ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" - "ldr q0, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" - ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" - "ldr d27, [x20, #0x0]\n" - ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" - ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" - "fcvtl v27.4s, v27.4h\n" - "uzp1 v0.2d, v19.2d, v26.2d\n" - "uzp2 v26.2d, v19.2d, v26.2d\n" - "fmul v19.4s, v27.4s, v20.s[0]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v2.4s, v0.4s, v19.4s\n" - "ldr q19, [x23, #0x0]\n" - "uzp1 v0.2d, v18.2d, v17.2d\n" - "uzp2 v18.2d, v18.2d, v17.2d\n" - "fmul v17.4s, v27.4s, v20.s[1]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v10.4s, v26.4s, v17.4s\n" - "ldr q17, [x23, #0x10]\n" - "fmul v26.4s, v27.4s, v20.s[2]\n" - "fmul v20.4s, v27.4s, v20.s[3]\n" - "fmla v12.4s, v0.4s, v26.4s\n" - "ldr d0, [x22, #-0x8]\n" - "ldr d26, [x21, #-0x8]\n" - "fcvtl v0.4s, v0.4h\n" - "fmla v28.4s, v18.4s, v20.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x23, #0x20]\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x23, #0x40]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q19, [x23, #0x60]\n" - ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" - ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" - "uzp1 v19.2d, v20.2d, v18.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp2 v20.2d, v20.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v9.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v11.4s, v19.4s, v18.4s\n" - "ldr q18, [x22, #0x0]\n" - "fmul v19.4s, v27.4s, v9.s[1]\n" - "fmla v13.4s, v20.4s, v19.4s\n" - "movi v19.4s, #0x0\n" - "movi v20.4s, #0x0\n" - ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" - ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" - "ldr q17, [x23, #0x30]\n" - ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" - "ldr q17, [x23, #0x50]\n" - ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" - "ldr q17, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v9.s[2]\n" - "fmul v9.4s, v27.4s, v9.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v22.4s, v17.4s, v19.4s\n" - "ldr q17, [x22, #0x10]\n" - "movi v19.4s, #0x0\n" - ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" - "fmla v23.4s, v20.4s, v9.4s\n" - "movi v20.4s, #0x0\n" - "movi v9.4s, #0x0\n" - ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" - "ldr q18, [x22, #0x20]\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" - ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" - "ldr q18, [x22, #0x40]\n" - ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" - ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" - "ldr q18, [x22, #0x60]\n" - ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" - ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" - "ldr q17, [x22, #0x30]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" - "ldr q17, [x22, #0x50]\n" - ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" - "ldr q17, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v0.s[0]\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v25.4s, v17.4s, v19.4s\n" - "ldr q19, [x21, #0x0]\n" - "fmul v17.4s, v27.4s, v0.s[1]\n" - "fmla v5.4s, v20.4s, v17.4s\n" - "ldr q17, [x21, #0x10]\n" - "uzp1 v20.2d, v9.2d, v18.2d\n" - "uzp2 v9.2d, v9.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v0.s[2]\n" - "fmul v0.4s, v27.4s, v0.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "fmla v7.4s, v20.4s, v18.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x21, #0x20]\n" - "fmla v4.4s, v9.4s, v0.4s\n" - "movi v9.4s, #0x0\n" - "movi v0.4s, #0x0\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - "fmul v8.4s, v27.4s, v26.s[0]\n" - ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" - "ldr q17, [x21, #0x30]\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - "fmul v31.4s, v27.4s, v26.s[1]\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x21, #0x40]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - "fmul v15.4s, v27.4s, v26.s[2]\n" - "fmul v27.4s, v27.4s, v26.s[3]\n" - ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" - "ldr q1, [x21, #0x50]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q26, [x21, #0x60]\n" - ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" - ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" - "ldr q21, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" - ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" - ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" - ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" - "uzp1 v29.2d, v20.2d, v18.2d\n" - "uzp2 v21.2d, v20.2d, v18.2d\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "uzp1 v18.2d, v9.2d, v0.2d\n" - "uzp2 v16.2d, v9.2d, v0.2d\n" - "scvtf v21.4s, v21.4s, #0x4\n" - "fmla v6.4s, v29.4s, v8.4s\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v30.4s, v21.4s, v31.4s\n" - "fmla v24.4s, v18.4s, v15.4s\n" - "fmla v14.4s, v16.4s, v27.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q28, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q22, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q6, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q30, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q24, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q6, [x24, #0x0]\n" - "ldr q5, [x24, #0x10]\n" - "movi v17.16b, #0x4\n" - "movi v8.4s, #0x0\n" - "ldr q4, [x25, #0x0]\n" - "ldr q13, [x25, #0x10]\n" - "movi v27.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q31, [x24, #0x20]\n" - "ldr q14, [x24, #0x30]\n" - "movi v29.4s, #0x0\n" - "movi v22.16b, #0xf0\n" - "ldr q11, [x25, #0x20]\n" - "ldr q23, [x25, #0x30]\n" - "sshl v21.16b, v6.16b, v17.16b\n" - "sshl v16.16b, v5.16b, v17.16b\n" - "ldr q20, [x25, #0x40]\n" - "ldr q26, [x25, #0x50]\n" - "and v6.16b, v6.16b, v22.16b\n" - "and v5.16b, v5.16b, v22.16b\n" - "ldr q25, [x25, #0x60]\n" - "ldr q3, [x25, #0x70]\n" - "sshl v19.16b, v31.16b, v17.16b\n" - "sshl v18.16b, v14.16b, v17.16b\n" - "ldr d17, [x25, #-0x8]\n" - ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" - ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" - "and v31.16b, v31.16b, v22.16b\n" - ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" - ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" - "and v14.16b, v14.16b, v22.16b\n" - "sub x20, x24, #0x8\n" - "ldr d16, [x20, #0x0]\n" - "subs x21, x21, #0x1\n" - "add x25, x25, #0x88\n" - "fcvtl v17.4s, v17.4h\n" - "add x24, x24, #0x48\n" - ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" - ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" - ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" - ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" - "fcvtl v16.4s, v16.4h\n" - ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" - ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" - "fmul v23.4s, v16.4s, v17.s[0]\n" - "fmul v21.4s, v16.4s, v17.s[1]\n" - "fmul v1.4s, v16.4s, v17.s[2]\n" - "fmul v20.4s, v16.4s, v17.s[3]\n" - ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" - ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" - ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" - ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" - ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" - ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" - "uzp1 v19.2d, v8.2d, v27.2d\n" - "uzp2 v18.2d, v8.2d, v27.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp1 v17.2d, v0.2d, v29.2d\n" - "uzp2 v16.2d, v0.2d, v29.2d\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v2.4s, v19.4s, v23.4s\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v10.4s, v18.4s, v21.4s\n" - "fmla v12.4s, v17.4s, v1.4s\n" - "fmla v28.4s, v16.4s, v20.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q28, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - float sumf[4][4]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } -} - -void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) -#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x20, #0x4\n" - "mov x13, %x[nr]\n" - "mov z28.s, #-0x4\n" - "mov x12, #0x88\n" - "ptrue p1.b\n" - "whilelt p0.s, XZR, x20\n" - "cmp x13, #0x10\n" - "mul x12, %x[nb], x12\n" - "blt 4f\n" - "1:" // Row loop - "add x11, %x[b_ptr], #0x10\n" - "mov x10, %x[nc]\n" - "add x9, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x28, %x[a_ptr], #0x8\n" - "mov z24.b, #0x0\n" - "mov z15.b, #0x0\n" - "mov x27, %x[nb]\n" - "add x26, x28, x12\n" - "mov z12.b, #0x0\n" - "mov z0.b, #0x0\n" - "add x25, x26, x12\n" - "mov z13.b, #0x0\n" - "mov z1.b, #0x0\n" - "add x24, x25, x12\n" - "mov z20.b, #0x0\n" - "mov z25.b, #0x0\n" - "mov z11.b, #0x0\n" - "mov z16.b, #0x0\n" - "mov z19.b, #0x0\n" - "mov z26.b, #0x0\n" - "mov z8.b, #0x0\n" - "mov z29.b, #0x0\n" - "mov z27.b, #0x0\n" - "mov z10.b, #0x0\n" - "3:" // Block loop - "ld1b { z30.b }, p1/Z, [x11]\n" - "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n" - "mov z18.s, #0x0\n" - "mov z7.s, #0x0\n" - "ld1rqb { z3.b }, p1/Z, [x28]\n" - "ld1rqb { z5.b }, p1/Z, [x28, #16]\n" - "mov z9.s, #0x0\n" - "mov z22.s, #0x0\n" - "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n" - "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n" - "sub x20, x11, #0x10\n" - "sub x23, x28, #0x8\n" - "lsl z31.b, z30.b, #0x4\n" - "lsl z6.b, z21.b, #0x4\n" - "ld1h { z23.s }, p1/Z, [x20]\n" - "sub x22, x26, #0x8\n" - "and z30.b, z30.b, #0xf0\n" - "and z21.b, z21.b, #0xf0\n" - "sub x21, x25, #0x8\n" - "sub x20, x24, #0x8\n" - "lsl z14.b, z4.b, #0x4\n" - "lsl z2.b, z17.b, #0x4\n" - "subs x27, x27, #0x1\n" - "add x11, x11, #0x90\n" - ".inst 0x451f9872 // smmla z18.s, z3.b, z31.b\n" - ".inst 0x45069867 // smmla z7.s, z3.b, z6.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #32]\n" - "and z4.b, z4.b, #0xf0\n" - ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" - ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #48]\n" - "and z17.b, z17.b, #0xf0\n" - "fcvt z23.s, p1/m, z23.h\n" - ".inst 0x450e9872 // smmla z18.s, z3.b, z14.b\n" - ".inst 0x45029867 // smmla z7.s, z3.b, z2.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #64]\n" - ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" - ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #80]\n" - "fscale z23.s, p1/m, z23.s, z28.s\n" - ".inst 0x451e9872 // smmla z18.s, z3.b, z30.b\n" - ".inst 0x45159867 // smmla z7.s, z3.b, z21.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #96]\n" - ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" - ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #112]\n" - "add x28, x28, #0x88\n" - ".inst 0x45049872 // smmla z18.s, z3.b, z4.b\n" - ".inst 0x45119867 // smmla z7.s, z3.b, z17.b\n" - "ld1h { z3.s }, p0/Z, [x23]\n" - ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" - ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" - "fcvt z3.s, p1/m, z3.h\n" - "uzp1 z5.d, z18.d, z7.d\n" - "uzp2 z18.d, z18.d, z7.d\n" - "mov z3.q, z3.q[0]\n" - "uzp1 z7.d, z9.d, z22.d\n" - "uzp2 z22.d, z9.d, z22.d\n" - "fmul z9.s, z23.s, z3.s[0]\n" - "scvtf z5.s, p1/m, z5.s\n" - "scvtf z18.s, p1/m, z18.s\n" - "scvtf z7.s, p1/m, z7.s\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z24.s, p1/M, z5.s, z9.s\n" - "ld1rqb { z5.b }, p1/Z, [x26]\n" - "fmul z9.s, z23.s, z3.s[1]\n" - "fmla z15.s, p1/M, z18.s, z9.s\n" - "ld1rqb { z18.b }, p1/Z, [x26, #16]\n" - "fmul z9.s, z23.s, z3.s[2]\n" - "fmul z3.s, z23.s, z3.s[3]\n" - "fmla z12.s, p1/M, z7.s, z9.s\n" - "mov z9.s, #0x0\n" - "ld1h { z7.s }, p0/Z, [x22]\n" - ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" - "fmla z0.s, p1/M, z22.s, z3.s\n" - "mov z22.s, #0x0\n" - "ld1h { z3.s }, p0/Z, [x21]\n" - ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #32]\n" - "fcvt z7.s, p1/m, z7.h\n" - "fcvt z3.s, p1/m, z3.h\n" - ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" - ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #64]\n" - "mov z7.q, z7.q[0]\n" - "mov z3.q, z3.q[0]\n" - ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" - ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #96]\n" - ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" - ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" - "uzp1 z5.d, z9.d, z22.d\n" - "scvtf z5.s, p1/m, z5.s\n" - "uzp2 z22.d, z9.d, z22.d\n" - "fmul z9.s, z23.s, z7.s[0]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z13.s, p1/M, z5.s, z9.s\n" - "ld1rqb { z9.b }, p1/Z, [x25]\n" - "fmul z5.s, z23.s, z7.s[1]\n" - "fmla z1.s, p1/M, z22.s, z5.s\n" - "mov z5.s, #0x0\n" - "mov z22.s, #0x0\n" - ".inst 0x451f9a45 // smmla z5.s, z18.b, z31.b\n" - ".inst 0x45069a56 // smmla z22.s, z18.b, z6.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #48]\n" - ".inst 0x450e9a45 // smmla z5.s, z18.b, z14.b\n" - ".inst 0x45029a56 // smmla z22.s, z18.b, z2.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #80]\n" - ".inst 0x451e9a45 // smmla z5.s, z18.b, z30.b\n" - ".inst 0x45159a56 // smmla z22.s, z18.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #112]\n" - "add x26, x26, #0x88\n" - ".inst 0x45049a45 // smmla z5.s, z18.b, z4.b\n" - ".inst 0x45119a56 // smmla z22.s, z18.b, z17.b\n" - "uzp1 z18.d, z5.d, z22.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp2 z22.d, z5.d, z22.d\n" - "fmul z5.s, z23.s, z7.s[2]\n" - "fmul z7.s, z23.s, z7.s[3]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z20.s, p1/M, z18.s, z5.s\n" - "ld1rqb { z18.b }, p1/Z, [x25, #16]\n" - "ld1h { z5.s }, p0/Z, [x20]\n" - "fcvt z5.s, p1/m, z5.h\n" - "fmla z25.s, p1/M, z22.s, z7.s\n" - "mov z22.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9936 // smmla z22.s, z9.b, z31.b\n" - ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #32]\n" - "mov z5.q, z5.q[0]\n" - ".inst 0x450e9936 // smmla z22.s, z9.b, z14.b\n" - ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #64]\n" - ".inst 0x451e9936 // smmla z22.s, z9.b, z30.b\n" - ".inst 0x45159927 // smmla z7.s, z9.b, z21.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #96]\n" - ".inst 0x45049936 // smmla z22.s, z9.b, z4.b\n" - ".inst 0x45119927 // smmla z7.s, z9.b, z17.b\n" - "uzp1 z9.d, z22.d, z7.d\n" - "scvtf z9.s, p1/m, z9.s\n" - "uzp2 z22.d, z22.d, z7.d\n" - "fmul z7.s, z23.s, z3.s[0]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z11.s, p1/M, z9.s, z7.s\n" - "ld1rqb { z9.b }, p1/Z, [x24]\n" - "fmul z7.s, z23.s, z3.s[1]\n" - "fmla z16.s, p1/M, z22.s, z7.s\n" - "mov z22.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9a56 // smmla z22.s, z18.b, z31.b\n" - ".inst 0x45069a47 // smmla z7.s, z18.b, z6.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #48]\n" - ".inst 0x450e9a56 // smmla z22.s, z18.b, z14.b\n" - ".inst 0x45029a47 // smmla z7.s, z18.b, z2.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #80]\n" - ".inst 0x451e9a56 // smmla z22.s, z18.b, z30.b\n" - ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #112]\n" - "add x25, x25, #0x88\n" - ".inst 0x45049a56 // smmla z22.s, z18.b, z4.b\n" - ".inst 0x45119a47 // smmla z7.s, z18.b, z17.b\n" - "uzp1 z18.d, z22.d, z7.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp2 z7.d, z22.d, z7.d\n" - "fmul z22.s, z23.s, z3.s[2]\n" - "fmul z3.s, z23.s, z3.s[3]\n" - "scvtf z7.s, p1/m, z7.s\n" - "fmla z19.s, p1/M, z18.s, z22.s\n" - "ld1rqb { z18.b }, p1/Z, [x24, #16]\n" - "fmul z22.s, z23.s, z5.s[0]\n" - "fmla z26.s, p1/M, z7.s, z3.s\n" - "mov z3.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9923 // smmla z3.s, z9.b, z31.b\n" - ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" - "ld1rqb { z9.b }, p1/Z, [x24, #32]\n" - ".inst 0x450e9923 // smmla z3.s, z9.b, z14.b\n" - ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" - "mov z9.s, #0x0\n" - ".inst 0x451f9a49 // smmla z9.s, z18.b, z31.b\n" - "mov z31.s, #0x0\n" - ".inst 0x45069a5f // smmla z31.s, z18.b, z6.b\n" - "ld1rqb { z6.b }, p1/Z, [x24, #48]\n" - "ld1rqb { z18.b }, p1/Z, [x24, #64]\n" - ".inst 0x450e98c9 // smmla z9.s, z6.b, z14.b\n" - "fmul z14.s, z23.s, z5.s[1]\n" - ".inst 0x450298df // smmla z31.s, z6.b, z2.b\n" - "ld1rqb { z6.b }, p1/Z, [x24, #80]\n" - "fmul z2.s, z23.s, z5.s[2]\n" - "fmul z23.s, z23.s, z5.s[3]\n" - ".inst 0x451e9a43 // smmla z3.s, z18.b, z30.b\n" - ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x24, #96]\n" - ".inst 0x451e98c9 // smmla z9.s, z6.b, z30.b\n" - ".inst 0x451598df // smmla z31.s, z6.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x24, #112]\n" - "add x24, x24, #0x88\n" - ".inst 0x450498a3 // smmla z3.s, z5.b, z4.b\n" - ".inst 0x451198a7 // smmla z7.s, z5.b, z17.b\n" - ".inst 0x45049a49 // smmla z9.s, z18.b, z4.b\n" - ".inst 0x45119a5f // smmla z31.s, z18.b, z17.b\n" - "uzp1 z18.d, z3.d, z7.d\n" - "uzp2 z5.d, z3.d, z7.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp1 z6.d, z9.d, z31.d\n" - "uzp2 z9.d, z9.d, z31.d\n" - "scvtf z5.s, p1/m, z5.s\n" - "fmla z8.s, p1/M, z18.s, z22.s\n" - "scvtf z6.s, p1/m, z6.s\n" - "scvtf z9.s, p1/m, z9.s\n" - "fmla z29.s, p1/M, z5.s, z14.s\n" - "fmla z27.s, p1/M, z6.s, z2.s\n" - "fmla z10.s, p1/M, z9.s, z23.s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x10, x10, #0x8\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "st1w { z24.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z15.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z12.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z0.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z13.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z1.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z20.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z25.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z11.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z16.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z19.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z26.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z8.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z29.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z27.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z10.s }, p1, [x20]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x13, x13, #0x10\n" - "cmp x13, #0x10\n" - "mov %x[res_ptr], x9\n" - "madd %x[a_ptr], x20, x12, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x13, 9f\n" - "5:" // Row tail: Row loop - "add x25, %x[b_ptr], #0x10\n" - "mov x24, %x[nc]\n" - "add x23, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "mov z24.b, #0x0\n" - "mov z15.b, #0x0\n" - "add x28, %x[a_ptr], #0x8\n" - "mov x22, %x[nb]\n" - "mov z12.b, #0x0\n" - "mov z0.b, #0x0\n" - "7:" // Row tail: Block loop - "ld1b { z3.b }, p1/Z, [x25]\n" - "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n" - "mov z2.s, #0x0\n" - "mov z25.s, #0x0\n" - "ld1rqb { z26.b }, p1/Z, [x28]\n" - "ld1rqb { z21.b }, p1/Z, [x28, #16]\n" - "mov z27.s, #0x0\n" - "mov z19.s, #0x0\n" - "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n" - "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n" - "sub x21, x25, #0x10\n" - "sub x20, x28, #0x8\n" - "lsl z20.b, z3.b, #0x4\n" - "lsl z4.b, z6.b, #0x4\n" - "ld1rqb { z10.b }, p1/Z, [x28, #32]\n" - "ld1rqb { z23.b }, p1/Z, [x28, #48]\n" - "and z3.b, z3.b, #0xf0\n" - "and z6.b, z6.b, #0xf0\n" - "ld1rqb { z11.b }, p1/Z, [x28, #64]\n" - "ld1rqb { z7.b }, p1/Z, [x28, #80]\n" - "lsl z8.b, z29.b, #0x4\n" - "lsl z14.b, z16.b, #0x4\n" - "ld1rqb { z18.b }, p1/Z, [x28, #96]\n" - "ld1rqb { z30.b }, p1/Z, [x28, #112]\n" - ".inst 0x45149b42 // smmla z2.s, z26.b, z20.b\n" - ".inst 0x45049b59 // smmla z25.s, z26.b, z4.b\n" - "and z29.b, z29.b, #0xf0\n" - "ld1h { z17.s }, p1/Z, [x21]\n" - ".inst 0x45149abb // smmla z27.s, z21.b, z20.b\n" - ".inst 0x45049ab3 // smmla z19.s, z21.b, z4.b\n" - "and z16.b, z16.b, #0xf0\n" - "ld1h { z4.s }, p0/Z, [x20]\n" - "subs x22, x22, #0x1\n" - "add x28, x28, #0x88\n" - "fcvt z17.s, p1/m, z17.h\n" - "add x25, x25, #0x90\n" - ".inst 0x45089942 // smmla z2.s, z10.b, z8.b\n" - ".inst 0x450e9959 // smmla z25.s, z10.b, z14.b\n" - "fcvt z4.s, p1/m, z4.h\n" - ".inst 0x45089afb // smmla z27.s, z23.b, z8.b\n" - ".inst 0x450e9af3 // smmla z19.s, z23.b, z14.b\n" - "fscale z17.s, p1/m, z17.s, z28.s\n" - "mov z4.q, z4.q[0]\n" - ".inst 0x45039962 // smmla z2.s, z11.b, z3.b\n" - ".inst 0x45069979 // smmla z25.s, z11.b, z6.b\n" - "fmul z23.s, z17.s, z4.s[0]\n" - "fmul z9.s, z17.s, z4.s[1]\n" - "fmul z21.s, z17.s, z4.s[2]\n" - "fmul z4.s, z17.s, z4.s[3]\n" - ".inst 0x450398fb // smmla z27.s, z7.b, z3.b\n" - ".inst 0x450698f3 // smmla z19.s, z7.b, z6.b\n" - ".inst 0x451d9a42 // smmla z2.s, z18.b, z29.b\n" - ".inst 0x45109a59 // smmla z25.s, z18.b, z16.b\n" - ".inst 0x451d9bdb // smmla z27.s, z30.b, z29.b\n" - ".inst 0x45109bd3 // smmla z19.s, z30.b, z16.b\n" - "uzp1 z31.d, z2.d, z25.d\n" - "uzp2 z13.d, z2.d, z25.d\n" - "scvtf z31.s, p1/m, z31.s\n" - "uzp1 z17.d, z27.d, z19.d\n" - "uzp2 z18.d, z27.d, z19.d\n" - "scvtf z13.s, p1/m, z13.s\n" - "fmla z24.s, p1/M, z31.s, z23.s\n" - "scvtf z17.s, p1/m, z17.s\n" - "scvtf z18.s, p1/m, z18.s\n" - "fmla z15.s, p1/M, z13.s, z9.s\n" - "fmla z12.s, p1/M, z17.s, z21.s\n" - "fmla z0.s, p1/M, z18.s, z4.s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x13, #0x1\n" - "st1w { z24.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x13, #0x2\n" - "st1w { z15.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x13, #0x3\n" - "st1w { z12.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "st1w { z0.s }, p1, [x20]\n" - "8:" // Row tail: Accumulator store skip - "subs x24, x24, #0x8\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "bne 6b\n" - "subs x13, x13, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x12\n" - "mov %x[res_ptr], x23\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" - ); - return; - } -#endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) -#elif defined(__AVX2__) || defined(__AVX512F__) - { - const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; - const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; - int64_t b_nb = n / QK4_0; - int64_t y = 0; - // Mask to mask out nibbles from packed bytes - const __m256i m4b = _mm256_set1_epi8(0x0F); - const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); - // Lookup table to convert signed nibbles to signed bytes - __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); - signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); - // Permute mask used for easier vector processing at later stages - __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); - int64_t xstart = 0; - int anr = nr - nr%16; // Used to align nr with boundary of 16 - #ifdef __AVX512F__ - int anc = nc - nc%16; // Used to align nc with boundary of 16 - // Mask to mask out nibbles from packed bytes expanded to 512 bit length - const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); - // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length - __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); - - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < anr / 4; y += 4) { - - const block_q8_0x4 * a_ptrs[4]; - - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } - - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { - - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); - - // Master FP accumulators - __m512 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm512_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) - - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - - // Shuffle pattern two - right side input - - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) - - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) - - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); - - // Process LHS in pairs of rows - for (int rp = 0; rp < 4; rp++) { - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); - __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); - __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); - __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); - __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); - __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); - __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); - __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); - __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); - - __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); - __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); - __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); - __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); - __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); - __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); - __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); - __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); - - // Shuffle pattern one - left side input - - const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m512i iacc_mat_00_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_01_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_10_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_11_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_00_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_01_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); - __m512i iacc_mat_10_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_11_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); - __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); - __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); - __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); - const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - - // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); - } - } - - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { - - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); - - // Master FP accumulators - __m512 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm512_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) - - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - - // Shuffle pattern two - right side input - - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) - - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) - - - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); - __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); - __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); - __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); - __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); - - __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); - __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); - __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); - __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); - __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); - __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); - __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); - __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); - - // Shuffle pattern one - left side input - - const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m512i iacc_mat_00_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_01_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_10_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_11_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_00_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_01_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); - __m512i iacc_mat_10_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_11_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); - __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); - __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); - __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); - const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } - - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - if (anc != nc) { - xstart = anc/8; - y = 0; - } - #endif // __AVX512F__ - - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - - for (; y < anr / 4; y += 4) { - const block_q8_0x4 * a_ptrs[4]; - - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } - - // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = xstart; x < nc / 8; x++) { - - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulators - __m256 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - - // Shuffle pattern two - right side input - - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - - // Process LHS in groups of four - for (int rp = 0; rp < 4; rp++) { - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); - __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); - __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); - __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); - __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); - __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); - __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); - __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); - __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); - - // Shuffle pattern one - left side input - const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m256i iacc_mat_00_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_01_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_10_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_11_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_00_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_01_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); - __m256i iacc_mat_10_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_11_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - // Straighten out to make 4 row vectors - __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); - __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); - __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); - __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); - - // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); - } - } - - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - for (int64_t x = xstart; x < nc / 8; x++) { - - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulators - __m256 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - - // Shuffle pattern two - right side input - - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); - __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); - __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); - __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); - __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); - - // Shuffle pattern one - left side input - - const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m256i iacc_mat_00_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_01_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_10_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_11_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_00_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_01_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); - __m256i iacc_mat_10_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_11_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); - __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); - __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); - __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } - - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) - float sumf[4][8]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } -} diff --git a/ggml/src/ggml-aarch64.h b/ggml/src/ggml-aarch64.h index 517babaf16..a578685911 100644 --- a/ggml/src/ggml-aarch64.h +++ b/ggml/src/ggml-aarch64.h @@ -1,9 +1,5 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd. #pragma once -#define GGML_COMMON_DECL_C -#include "ggml-common.h" - #include "ggml.h" // GGML internal header @@ -12,27 +8,11 @@ extern "C" { #endif -// Quantization -void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave); - // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -// GEMV -void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); - -// GEMM -void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); - #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index 28548fbbb6..2b2240be85 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -348,7 +348,6 @@ struct tensor_alloc { }; struct leaf_alloc { - int buffer_id; struct tensor_alloc leaf; }; @@ -467,18 +466,12 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { return ggml_gallocr_hash_get(galloc, t)->allocated; } -static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) { - struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); - hn->buffer_id = buffer_id; - hn->offset = offset; - hn->allocated = true; -} - static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated; } static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { + GGML_ASSERT(buffer_id >= 0); struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) { @@ -740,7 +733,6 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); - galloc->leaf_allocs[i].buffer_id = hn->buffer_id; if (leaf->view_src || leaf->data) { galloc->leaf_allocs[i].leaf.buffer_id = -1; galloc->leaf_allocs[i].leaf.offset = SIZE_MAX; @@ -818,7 +810,11 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * } static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) { - size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); + size_t node_size = 0; + if (!node->data && !node->view_src) { + GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API + node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); + } return talloc->size_max >= node_size; } diff --git a/ggml/src/ggml-amx/CMakeLists.txt b/ggml/src/ggml-amx/CMakeLists.txt new file mode 100644 index 0000000000..d6676f3f67 --- /dev/null +++ b/ggml/src/ggml-amx/CMakeLists.txt @@ -0,0 +1,107 @@ +if (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$") AND + CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0) + message(STATUS "Using AMX") + + file(GLOB GGML_HEADERS_AMX "*.h") + list(APPEND GGML_HEADERS_AMX "../../include/ggml-amx.h") + + file(GLOB GGML_SOURCES_AMX "*.cpp") + + add_library(ggml-amx + ${GGML_HEADERS_AMX} + ${GGML_SOURCES_AMX}) + + target_link_libraries(ggml-amx PRIVATE ggml-base) + target_include_directories(ggml-amx PRIVATE . ..) + + # this is duplicated from the CPU backend, since the AMX backend also depends on the architecture flags + # TODO: integrate AMX backend into the CPU backend + if (MSVC) + # instruction set detection for MSVC only + if (GGML_NATIVE) + # TODO: improve, should not reference files from the parent folder + include(../ggml-cpu/cmake/FindSIMD.cmake) + endif () + if (GGML_AVX512) + list(APPEND ARCH_FLAGS /arch:AVX512) + # MSVC has no compile-time flags enabling specific + # AVX512 extensions, neither it defines the + # macros corresponding to the extensions. + # Do it manually. + if (GGML_AVX512_VBMI) + add_compile_definitions($<$:__AVX512VBMI__>) + add_compile_definitions($<$:__AVX512VBMI__>) + endif() + if (GGML_AVX512_VNNI) + add_compile_definitions($<$:__AVX512VNNI__>) + add_compile_definitions($<$:__AVX512VNNI__>) + endif() + if (GGML_AVX512_BF16) + add_compile_definitions($<$:__AVX512BF16__>) + add_compile_definitions($<$:__AVX512BF16__>) + endif() + if (GGML_AMX_TILE) + add_compile_definitions($<$:__AMX_TILE__>) + add_compile_definitions($<$:__AMX_TILE__>) + endif() + if (GGML_AMX_INT8) + add_compile_definitions($<$:__AMX_INT8__>) + add_compile_definitions($<$:__AMX_INT8__>) + endif() + if (GGML_AMX_BF16) + add_compile_definitions($<$:__AMX_BF16__>) + add_compile_definitions($<$:__AMX_BF16__>) + endif() + elseif (GGML_AVX2) + list(APPEND ARCH_FLAGS /arch:AVX2) + elseif (GGML_AVX) + list(APPEND ARCH_FLAGS /arch:AVX) + endif() + else() + if (GGML_NATIVE) + list(APPEND ARCH_FLAGS -march=native) + endif() + if (GGML_F16C) + list(APPEND ARCH_FLAGS -mf16c) + endif() + if (GGML_FMA) + list(APPEND ARCH_FLAGS -mfma) + endif() + if (GGML_AVX) + list(APPEND ARCH_FLAGS -mavx) + endif() + if (GGML_AVX2) + list(APPEND ARCH_FLAGS -mavx2) + endif() + if (GGML_AVX512) + list(APPEND ARCH_FLAGS -mavx512f) + list(APPEND ARCH_FLAGS -mavx512dq) + list(APPEND ARCH_FLAGS -mavx512bw) + endif() + if (GGML_AVX512_VBMI) + list(APPEND ARCH_FLAGS -mavx512vbmi) + endif() + if (GGML_AVX512_VNNI) + list(APPEND ARCH_FLAGS -mavx512vnni) + endif() + if (GGML_AVX512_BF16) + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() + if (GGML_AMX_TILE) + list(APPEND ARCH_FLAGS -mamx-tile) + endif() + if (GGML_AMX_INT8) + list(APPEND ARCH_FLAGS -mamx-int8) + endif() + if (GGML_AMX_BF16) + list(APPEND ARCH_FLAGS -mamx-bf16) + endif() + endif() + + target_compile_options(ggml-amx PRIVATE ${ARCH_FLAGS}) +else() + set(GGML_AMX OFF PARENT_SCOPE) + message(WARNING "AMX requires x86 and gcc version > 11.0. Turning off GGML_AMX.") +endif() diff --git a/ggml/src/ggml-amx/common.h b/ggml/src/ggml-amx/common.h new file mode 100644 index 0000000000..5db8ce30d9 --- /dev/null +++ b/ggml/src/ggml-amx/common.h @@ -0,0 +1,94 @@ +#pragma once + +#include "ggml.h" +// hack until AMX is moved into the CPU backend +#include "../ggml-cpu/ggml-cpu-impl.h" // + +#include +#include +#include + +#if defined(_OPENMP) +#include +#endif + +#define TILE_M 16 +#define TILE_N 16 +#define TILE_K 32 +#define VNNI_BLK 4 + +#define AMX_BLK_SIZE 32 + +#define TMM0 0 +#define TMM1 1 +#define TMM2 2 +#define TMM3 3 +#define TMM4 4 +#define TMM5 5 +#define TMM6 6 +#define TMM7 7 + +// parallel routines +template ::value, int>::type = 0> +inline T div_up(T x, T y) { return (x + y - 1) / y; } + +template +inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) { +#if 0 + // onednn partition pattern + T& n_my = n_end; + if (nth <= 1 || n == 0) { + n_start = 0; + n_my = n; + } else { + T n1 = div_up(n, nth); + T n2 = n1 - 1; + T T1 = n - n2 * nth; + n_my = ith < T1 ? n1 : n2; + n_start = ith <= T1 ? ith*n1 : T1 * n1 + (ith - T1) * n2; + } + n_end += n_start; +#else + // pytorch aten partition pattern + T n_my = div_up(n, nth); + n_start = ith * n_my; + n_end = std::min(n_start + n_my, n); +#endif +} + +template +inline void parallel_for(int nth, int n, const func_t& f) { +#if defined(_OPENMP) +#pragma omp parallel num_threads(nth) +{ + //int nth = omp_get_num_threads(); + int ith = omp_get_thread_num(); + int tbegin, tend; + balance211(n, nth, ith, tbegin, tend); + f(tbegin, tend); +} +#else + f(0, n); + + GGML_UNUSED(nth); +#endif +} + +// quantized types that have AMX support +inline bool qtype_has_amx_kernels(const enum ggml_type type) { + // TODO: fix padding for vnni format + return (type == GGML_TYPE_Q4_0) || + (type == GGML_TYPE_Q4_1); + //(type == GGML_TYPE_Q8_0) || + //(type == GGML_TYPE_Q4_K) || + //(type == GGML_TYPE_Q5_K) || + //(type == GGML_TYPE_Q6_K) || + //(type == GGML_TYPE_IQ4_XS); +} + +// ggml backend context +struct ggml_backend_amx_context { + int n_threads = GGML_DEFAULT_N_THREADS; + std::unique_ptr work_data; + size_t work_size = 0; +}; diff --git a/ggml/src/ggml-amx/ggml-amx.cpp b/ggml/src/ggml-amx/ggml-amx.cpp new file mode 100644 index 0000000000..8568e7965f --- /dev/null +++ b/ggml/src/ggml-amx/ggml-amx.cpp @@ -0,0 +1,446 @@ +#include "ggml-amx.h" +#include "ggml-amx/common.h" +#include "ggml-amx/mmq.h" +#include "ggml-backend-impl.h" +#include "ggml-impl.h" + +#if defined(__gnu_linux__) +#include +#include +#endif + +#include +#include +#include + +#if defined(__AMX_INT8__) + +// AMX buffer interface +static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { + free(buffer->context); +} + +static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { + return (void *)(buffer->context); +} + +static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + if (qtype_has_amx_kernels(tensor->type)) { + ggml_backend_amx_convert_weight(tensor, data, offset, size); + } else { + memcpy((char *)tensor->data + offset, data, size); + } + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + if (qtype_has_amx_kernels(src->type)) { + ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst)); + } else { + memcpy(dst->data, src->data, ggml_nbytes(src)); + } + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { + /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, + /* .get_base = */ ggml_backend_amx_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, + /* .clear = */ ggml_backend_amx_buffer_clear, + /* .reset = */ NULL, +}; + +static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "AMX"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = aligned_alloc(TENSOR_ALIGNMENT, size); + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); +} + +static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { + return ggml_backend_amx_get_alloc_size(tensor); + + GGML_UNUSED(buft); +} + +static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { + /* .iface = */ { + /* .get_name = */ ggml_backend_amx_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_amx_buffer_type_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_buffer_type_amx; +} + +// backend interface + +static const char * ggml_backend_amx_name(ggml_backend_t backend) { + return "AMX"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_amx_free(ggml_backend_t backend) { + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; + delete ctx; + delete backend; +} + +static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_backend_amx_mul_mat(ctx, node); + break; + + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + break; + + default: + fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); + GGML_ASSERT(false); + } + } + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(backend); +} + +static struct ggml_backend_i ggml_backend_amx_i = { + /* .get_name = */ ggml_backend_amx_name, + /* .free = */ ggml_backend_amx_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_amx_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; + +static ggml_guid_t ggml_backend_amx_guid() { + static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e }; + return &guid; +} + +#define ARCH_GET_XCOMP_PERM 0x1022 +#define ARCH_REQ_XCOMP_PERM 0x1023 +#define XFEATURE_XTILECFG 17 +#define XFEATURE_XTILEDATA 18 + +static bool ggml_amx_init() { +#if defined(__gnu_linux__) + if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { + fprintf(stderr, "AMX is not ready to be used!\n"); + return false; + } + return true; +#elif defined(_WIN32) + return true; +#endif +} + +ggml_backend_t ggml_backend_amx_init() { + + // invoke a Linux system call to request access to AMX features + ggml_amx_init(); + + // backend context + ggml_backend_amx_context * ctx = new ggml_backend_amx_context; + + // ggml amx backend + ggml_backend_t backend = new ggml_backend { + /* .guid = */ ggml_backend_amx_guid(), + /* .interface = */ ggml_backend_amx_i, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), + /* .context = */ ctx, + }; + + return backend; +} + +bool ggml_backend_is_amx(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid()); +} + +void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { + GGML_ASSERT(ggml_backend_is_amx(backend_amx)); + + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context; + ctx->n_threads = n_threads; +} + +// device interface + +static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) { + return "AMX"; + + GGML_UNUSED(dev); +} + +static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) { + return "Intel Advanced Matrix Extensions"; + + GGML_UNUSED(dev); +} + +static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + // TODO + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_ACCEL; + + GGML_UNUSED(dev); +} + +static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_amx_device_get_name(dev); + props->description = ggml_backend_amx_device_get_description(dev); + props->type = ggml_backend_amx_device_get_type(dev); + ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total); + + // `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) { + return ggml_backend_amx_init(); + + GGML_UNUSED(dev); + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_amx_buffer_type(); + + GGML_UNUSED(dev); +} + +static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + + // handle only 2d gemm for now + auto is_contiguous_2d = [](const struct ggml_tensor * t) { + return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; + }; + + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + return true; + + case GGML_OP_MUL_MAT: { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + const enum ggml_type type = src0->type; + const int64_t ne0 = op->ne[0]; + + // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 + // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 + bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); + + bool can_use_amx = + is_contiguous_2d(src0) && // src0 must be contiguous + is_contiguous_2d(src1) && // src1 must be contiguous + src1->type == GGML_TYPE_F32 && // src1 must be float32 + has_amx_kernels && // with amx kernel impls + ne0 % (TILE_N * 2) == 0; // out_features is 32x + + return can_use_amx; + } + default: + return false; + } + + GGML_UNUSED(dev); +} + +static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_amx_device_i = { + /* .get_name = */ ggml_backend_amx_device_get_name, + /* .get_description = */ ggml_backend_amx_device_get_description, + /* .get_memory = */ ggml_backend_amx_device_get_memory, + /* .get_type = */ ggml_backend_amx_device_get_type, + /* .get_props = */ ggml_backend_amx_device_get_props, + /* .init_backend = */ ggml_backend_amx_device_init, + /* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_amx_device_supports_op, + /* .supports_buft = */ ggml_backend_amx_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// backend reg interface + +static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) { + return "AMX"; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) { + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + static ggml_backend_device ggml_backend_amx_device = { + /* .iface = */ ggml_backend_amx_device_i, + /* .reg = */ reg, + /* .context = */ nullptr, + }; + + return &ggml_backend_amx_device; + + GGML_UNUSED(reg); + GGML_UNUSED(index); +} + +static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { + return (void *)ggml_backend_amx_set_n_threads; + } + return NULL; + + GGML_UNUSED(reg); + GGML_UNUSED(name); +} + +static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = { + /* .get_name = */ ggml_backend_amx_reg_get_name, + /* .get_device_count = */ ggml_backend_amx_reg_get_device_count, + /* .get_device = */ ggml_backend_amx_reg_get_device, + /* .get_proc_address = */ ggml_backend_amx_get_proc_address, +}; + +ggml_backend_reg_t ggml_backend_amx_reg(void) { + static struct ggml_backend_reg ggml_backend_amx_reg = { + /* .iface = */ ggml_backend_amx_reg_i, + /* .context = */ NULL, + }; + + return &ggml_backend_amx_reg; +} + +#else // if defined(__AMX_INT8__) + +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void) { + return nullptr; +} + +bool ggml_backend_is_amx(ggml_backend_t backend) { + GGML_UNUSED(backend); + return false; +} + +ggml_backend_t ggml_backend_amx_init(void) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + return nullptr; +} + +void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + + GGML_UNUSED(backend_amx); + GGML_UNUSED(n_threads); +} + +ggml_backend_reg_t ggml_backend_amx_reg(void) { + return nullptr; +} + +#endif diff --git a/ggml/src/ggml-amx/mmq.cpp b/ggml/src/ggml-amx/mmq.cpp new file mode 100644 index 0000000000..529bee25b5 --- /dev/null +++ b/ggml/src/ggml-amx/mmq.cpp @@ -0,0 +1,2510 @@ + +#if defined(__GNUC__) +#pragma GCC diagnostic ignored "-Wpedantic" +#pragma GCC diagnostic ignored "-Wunused-local-typedefs" +#endif + +#include "mmq.h" +#include "ggml-impl.h" +#include "ggml-quants.h" +#include +#include + +#if defined(__gnu_linux__) +#include +#include +#endif + +#if defined(_OPENMP) +#include +#endif + +#if (defined(_WIN32) || defined(_WIN64)) +#define RESTRICT __restrict +#else +#define RESTRICT __restrict__ +#endif + +#if (defined(_WIN32) || defined(_WIN64)) +#define ALWAYS_INLINE __forceinline +#elif __has_attribute(always_inline) || defined(__GNUC__) +#define ALWAYS_INLINE __attribute__((__always_inline__)) inline +#else +#define ALWAYS_INLINE inline +#endif + +#if defined(__AMX_INT8__) + +namespace { + +// Forced unrolling +template +struct Unroll { + template + ALWAYS_INLINE void operator()(const Func& f, Args... args) const { + Unroll{}(f, args...); + f(std::integral_constant{}, args...); + } +}; + +template <> +struct Unroll<1> { + template + ALWAYS_INLINE void operator()(const Func& f, Args... args) const { + f(std::integral_constant{}, args...); + } +}; + +// type traits +template struct PackedTypes {}; +template <> struct PackedTypes { using type = int8_t; }; +template <> struct PackedTypes { using type = uint8_t; }; +template <> struct PackedTypes { using type = int8_t; }; +template using packed_B_type = typename PackedTypes::type; + +template +struct do_compensate : std::integral_constant::value> {}; + +template +struct do_unpack : std::integral_constant::value || + std::is_same::value> {}; + +template +struct is_type_qkk : std::integral_constant::value || + std::is_same::value || + std::is_same::value || + std::is_same::value> {}; + +#define GGML_DISPATCH_FLOATING_TYPES(TYPE, ...) \ + [&] { \ + switch (TYPE) { \ + case GGML_TYPE_F16: { \ + using type = ggml_fp16_t; \ + constexpr int blck_size = 16; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_BF16: { \ + using type = ggml_bf16_t; \ + constexpr int blck_size = 32; \ + return __VA_ARGS__(); \ + } \ + default: \ + fprintf(stderr, "Unsupported floating data type\n"); \ + } \ + }() + +#define GGML_DISPATCH_QTYPES(QT, ...) \ + [&] { \ + switch (QT) { \ + case GGML_TYPE_Q4_0: { \ + using type = block_q4_0; \ + using vec_dot_type = block_q8_0; \ + constexpr int blck_size = QK4_0; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q4_1: { \ + using type = block_q4_1; \ + using vec_dot_type = block_q8_1; \ + constexpr int blck_size = QK4_1; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q8_0: { \ + using type = block_q8_0; \ + using vec_dot_type = block_q8_0; \ + constexpr int blck_size = QK8_0; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q4_K: { \ + using type = block_q4_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q5_K: { \ + using type = block_q5_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q6_K: { \ + using type = block_q6_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_IQ4_XS: { \ + using type = block_iq4_xs; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + default: \ + fprintf(stderr, "Unsupported quantized data type: %d\n", int(TYPE)); \ + } \ + }() + +#define GGML_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...) \ + [&] { \ + if (BOOL_V) { \ + constexpr bool BOOL_NAME = true; \ + return __VA_ARGS__(); \ + } else { \ + constexpr bool BOOL_NAME = false; \ + return __VA_ARGS__(); \ + } \ + }() + +// define amx tile config data structure +struct tile_config_t{ + uint8_t palette_id = 0; + uint8_t start_row = 0; + uint8_t reserved_0[14] = {0}; + uint16_t colsb[16] = {0}; + uint8_t rows[16] = {0}; +}; + +// Notes: amx tile config +// +// Typically, TMUL calculates A and B of size 16 x 64 containing INT8 values, +// and accumulate the result to a 16 x 16 matrix C containing INT32 values, +// +// As many GGUF quantized types as `block_size` of 32, so a 16-16-32 config is used +// instead of the normally used 16-16-64 config. +// +// Block A: {16, 32}, dtype = int8_t +// Block B: {16, 32}, dtype = uint8_t/int8_t +// Block C: {16, 16}, dtype = int32_t +// +// Block B needs to be prepacked to vnni format before feeding into TMUL: +// packed_B: from {n, k} to {k/vnni_blk, n, vnni_blck}, viewed in 2d, we get {8, 64} +// +// Therefore, we get tileconfig: +// A B C +// rows 16 8 16 +// colsb 32 64 16 +// +// For tile distribution, follow a 2-2-4 pattern, e.g. A used TMM2-TMM3, B used TMM0-TMM1, +// C used TMM4-TMM7: +// B TMM0 B TMM1 +// A TMM2 C TMM4 C TMM6 +// A TMM3 C TMM5 C TMM7 +// +// Each `amx` kernel handles 4 blocks at a time: 2MB * 2NB, when m < 2 * BLOCK_M, unpack A +// will be needed. +// +// Here another commonly used pattern 1-3-3 is skipped, as it is mostly used when m <=16; +// and the sinlge batch gemm (m=1) has a special fast path with `avx512-vnni`. +// +// ref: https://www.intel.com/content/www/us/en/developer/articles/code-sample/ +// advanced-matrix-extensions-intrinsics-functions.html +// + +#define TC_CONFIG_TILE(i, r, cb) tc.rows[i] = r; tc.colsb[i] = cb +void ggml_tile_config_init(void) { + static thread_local bool is_first_time = true; + + if (!is_first_time) { + return; + } + + static thread_local tile_config_t tc; + tile_config_t current_tc; + _tile_storeconfig(¤t_tc); + + // load only when config changes + if (tc.palette_id == 0 || (memcmp(¤t_tc.colsb, &tc.colsb, sizeof(uint16_t) * 8) != 0 && + memcmp(¤t_tc.rows, &tc.rows, sizeof(uint8_t) * 8) != 0)) { + tc.palette_id = 1; + tc.start_row = 0; + TC_CONFIG_TILE(TMM0, 8, 64); + TC_CONFIG_TILE(TMM1, 8, 64); + TC_CONFIG_TILE(TMM2, 16, 32); + TC_CONFIG_TILE(TMM3, 16, 32); + TC_CONFIG_TILE(TMM4, 16, 64); + TC_CONFIG_TILE(TMM5, 16, 64); + TC_CONFIG_TILE(TMM6, 16, 64); + TC_CONFIG_TILE(TMM7, 16, 64); + _tile_loadconfig(&tc); + } + + is_first_time = false; +} + +// we need an extra 16 * 4B (TILE_N * int32_t) for each NB/KB block for compensation. +// See the notes `s8s8 igemm compensation in avx512-vnni` for detail. +template +int get_tile_size() { + int tile_size = TILE_N * sizeof(TB); + if (do_compensate::value) { + tile_size += TILE_N * sizeof(int32_t); + } + if (std::is_same::value || + std::is_same::value) { + tile_size += TILE_N * 4; + } + if (std::is_same::value) { + tile_size += TILE_N * 2; + } + return tile_size; +} + +template +int get_row_size(int K) { + int KB = K / BLOCK_K; + int row_size = KB * sizeof(TB); + if (do_compensate::value) { + row_size += KB * sizeof(int32_t); + } + if (std::is_same::value || + std::is_same::value) { + row_size += KB * 4; + } + if (std::is_same::value) { + row_size += KB * 2; + } + return row_size; +} + +// vectorized dtype conversion +inline float FP16_TO_FP32(ggml_half val) { + __m256i v = _mm256_setr_epi16( + val, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0); + __m512 o = _mm512_cvtph_ps(v); + return _mm512_cvtss_f32(o); +} + +inline __m512 FP16_TO_FP32_VEC(ggml_half val) { + __m256i v = _mm256_set1_epi16(val); + return _mm512_cvtph_ps(v); +} + +// horizontal reduce +inline float _mm512_reduce_max_ps(const __m512 x) { + __m512 v = x; + __m512 v1 = _mm512_shuffle_f32x4(v, v, 0x4E); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_f32x4(v, v, 0xB1); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_ps(v, v, 0x4E); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_ps(v, v, 0xB1); + v = _mm512_max_ps(v, v1); + return _mm512_cvtss_f32(v); +} + +// transpose utils +#define SHUFFLE_EPI32(a, b, mask) \ + _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b), mask)) +inline void transpose_8x8_32bit(__m256i * v, __m256i * v1) { + // unpacking and 32-bit elements + v1[0] = _mm256_unpacklo_epi32(v[0], v[1]); + v1[1] = _mm256_unpackhi_epi32(v[0], v[1]); + v1[2] = _mm256_unpacklo_epi32(v[2], v[3]); + v1[3] = _mm256_unpackhi_epi32(v[2], v[3]); + v1[4] = _mm256_unpacklo_epi32(v[4], v[5]); + v1[5] = _mm256_unpackhi_epi32(v[4], v[5]); + v1[6] = _mm256_unpacklo_epi32(v[6], v[7]); + v1[7] = _mm256_unpackhi_epi32(v[6], v[7]); + + // shuffling the 32-bit elements + v[0] = SHUFFLE_EPI32(v1[0], v1[2], 0x44); + v[1] = SHUFFLE_EPI32(v1[0], v1[2], 0xee); + v[2] = SHUFFLE_EPI32(v1[4], v1[6], 0x44); + v[3] = SHUFFLE_EPI32(v1[4], v1[6], 0xee); + v[4] = SHUFFLE_EPI32(v1[1], v1[3], 0x44); + v[5] = SHUFFLE_EPI32(v1[1], v1[3], 0xee); + v[6] = SHUFFLE_EPI32(v1[5], v1[7], 0x44); + v[7] = SHUFFLE_EPI32(v1[5], v1[7], 0xee); + + // shuffling 128-bit elements + v1[0] = _mm256_permute2f128_si256(v[2], v[0], 0x02); + v1[1] = _mm256_permute2f128_si256(v[3], v[1], 0x02); + v1[2] = _mm256_permute2f128_si256(v[6], v[4], 0x02); + v1[3] = _mm256_permute2f128_si256(v[7], v[5], 0x02); + v1[4] = _mm256_permute2f128_si256(v[2], v[0], 0x13); + v1[5] = _mm256_permute2f128_si256(v[3], v[1], 0x13); + v1[6] = _mm256_permute2f128_si256(v[6], v[4], 0x13); + v1[7] = _mm256_permute2f128_si256(v[7], v[5], 0x13); +} + +inline void transpose_16x4_32bit(__m512i * r, __m512i * d) { + + static const __m512i index1 = _mm512_set_epi32( + 0x0f, 0x0b, 0x07, 0x03, + 0x0e, 0x0a, 0x06, 0x02, + 0x0d, 0x09, 0x05, 0x01, + 0x0c, 0x08, 0x04, 0x00); + + d[0] = _mm512_permutexvar_epi32(index1, r[0]); + d[1] = _mm512_permutexvar_epi32(index1, r[1]); + d[2] = _mm512_permutexvar_epi32(index1, r[2]); + d[3] = _mm512_permutexvar_epi32(index1, r[3]); + + r[0] = _mm512_shuffle_i32x4(d[0], d[1], 0x44); + r[1] = _mm512_shuffle_i32x4(d[0], d[1], 0xee); + r[2] = _mm512_shuffle_i32x4(d[2], d[3], 0x44); + r[3] = _mm512_shuffle_i32x4(d[2], d[3], 0xee); + + d[0] = _mm512_shuffle_i32x4(r[0], r[2], 0x88); + d[1] = _mm512_shuffle_i32x4(r[0], r[2], 0xdd); + d[2] = _mm512_shuffle_i32x4(r[1], r[3], 0x88); + d[3] = _mm512_shuffle_i32x4(r[1], r[3], 0xdd); +} + +inline void transpose_16x16_32bit(__m512i * v) { + __m512i v1[16]; + v1[0] = _mm512_unpacklo_epi32(v[0], v[1]); + v1[1] = _mm512_unpackhi_epi32(v[0], v[1]); + v1[2] = _mm512_unpacklo_epi32(v[2], v[3]); + v1[3] = _mm512_unpackhi_epi32(v[2], v[3]); + v1[4] = _mm512_unpacklo_epi32(v[4], v[5]); + v1[5] = _mm512_unpackhi_epi32(v[4], v[5]); + v1[6] = _mm512_unpacklo_epi32(v[6], v[7]); + v1[7] = _mm512_unpackhi_epi32(v[6], v[7]); + v1[8] = _mm512_unpacklo_epi32(v[8], v[9]); + v1[9] = _mm512_unpackhi_epi32(v[8], v[9]); + v1[10] = _mm512_unpacklo_epi32(v[10], v[11]); + v1[11] = _mm512_unpackhi_epi32(v[10], v[11]); + v1[12] = _mm512_unpacklo_epi32(v[12], v[13]); + v1[13] = _mm512_unpackhi_epi32(v[12], v[13]); + v1[14] = _mm512_unpacklo_epi32(v[14], v[15]); + v1[15] = _mm512_unpackhi_epi32(v[14], v[15]); + + v[0] = _mm512_unpacklo_epi64(v1[0], v1[2]); + v[1] = _mm512_unpackhi_epi64(v1[0], v1[2]); + v[2] = _mm512_unpacklo_epi64(v1[1], v1[3]); + v[3] = _mm512_unpackhi_epi64(v1[1], v1[3]); + v[4] = _mm512_unpacklo_epi64(v1[4], v1[6]); + v[5] = _mm512_unpackhi_epi64(v1[4], v1[6]); + v[6] = _mm512_unpacklo_epi64(v1[5], v1[7]); + v[7] = _mm512_unpackhi_epi64(v1[5], v1[7]); + v[8] = _mm512_unpacklo_epi64(v1[8], v1[10]); + v[9] = _mm512_unpackhi_epi64(v1[8], v1[10]); + v[10] = _mm512_unpacklo_epi64(v1[9], v1[11]); + v[11] = _mm512_unpackhi_epi64(v1[9], v1[11]); + v[12] = _mm512_unpacklo_epi64(v1[12], v1[14]); + v[13] = _mm512_unpackhi_epi64(v1[12], v1[14]); + v[14] = _mm512_unpacklo_epi64(v1[13], v1[15]); + v[15] = _mm512_unpackhi_epi64(v1[13], v1[15]); + + v1[0] = _mm512_shuffle_i32x4(v[0], v[4], 0x88); + v1[1] = _mm512_shuffle_i32x4(v[1], v[5], 0x88); + v1[2] = _mm512_shuffle_i32x4(v[2], v[6], 0x88); + v1[3] = _mm512_shuffle_i32x4(v[3], v[7], 0x88); + v1[4] = _mm512_shuffle_i32x4(v[0], v[4], 0xdd); + v1[5] = _mm512_shuffle_i32x4(v[1], v[5], 0xdd); + v1[6] = _mm512_shuffle_i32x4(v[2], v[6], 0xdd); + v1[7] = _mm512_shuffle_i32x4(v[3], v[7], 0xdd); + v1[8] = _mm512_shuffle_i32x4(v[8], v[12], 0x88); + v1[9] = _mm512_shuffle_i32x4(v[9], v[13], 0x88); + v1[10] = _mm512_shuffle_i32x4(v[10], v[14], 0x88); + v1[11] = _mm512_shuffle_i32x4(v[11], v[15], 0x88); + v1[12] = _mm512_shuffle_i32x4(v[8], v[12], 0xdd); + v1[13] = _mm512_shuffle_i32x4(v[9], v[13], 0xdd); + v1[14] = _mm512_shuffle_i32x4(v[10], v[14], 0xdd); + v1[15] = _mm512_shuffle_i32x4(v[11], v[15], 0xdd); + + v[0] = _mm512_shuffle_i32x4(v1[0], v1[8], 0x88); + v[1] = _mm512_shuffle_i32x4(v1[1], v1[9], 0x88); + v[2] = _mm512_shuffle_i32x4(v1[2], v1[10], 0x88); + v[3] = _mm512_shuffle_i32x4(v1[3], v1[11], 0x88); + v[4] = _mm512_shuffle_i32x4(v1[4], v1[12], 0x88); + v[5] = _mm512_shuffle_i32x4(v1[5], v1[13], 0x88); + v[6] = _mm512_shuffle_i32x4(v1[6], v1[14], 0x88); + v[7] = _mm512_shuffle_i32x4(v1[7], v1[15], 0x88); + v[8] = _mm512_shuffle_i32x4(v1[0], v1[8], 0xdd); + v[9] = _mm512_shuffle_i32x4(v1[1], v1[9], 0xdd); + v[10] = _mm512_shuffle_i32x4(v1[2], v1[10], 0xdd); + v[11] = _mm512_shuffle_i32x4(v1[3], v1[11], 0xdd); + v[12] = _mm512_shuffle_i32x4(v1[4], v1[12], 0xdd); + v[13] = _mm512_shuffle_i32x4(v1[5], v1[13], 0xdd); + v[14] = _mm512_shuffle_i32x4(v1[6], v1[14], 0xdd); + v[15] = _mm512_shuffle_i32x4(v1[7], v1[15], 0xdd); +} + +void quantize_row_q8_K_vnni(const float * RESTRICT x, void * RESTRICT vy, int64_t k) { + assert(k % QK_K == 0); + const int KB = k / QK_K; + constexpr int kVecs = QK_K / 16; + + block_q8_K * y = reinterpret_cast(vy); + + // hold 16 float vecs from x + __m512 v[kVecs]; + + // hold the quants vecs + __m512i vq[kVecs / 4]; + + // hold the packed quants vecs + __m512i vq_packed[kVecs / 4]; + + const __m512 signBit = _mm512_set1_ps(-0.f); + + for (int i = 0; i < KB; ++i) { + // Compute max(abs(e)) for the block + __m512 vamax = _mm512_set1_ps(0.f); + for (int j = 0; j < kVecs; ++j) { + v[j] = _mm512_loadu_ps(x); x += 16; + vamax = _mm512_max_ps(vamax, _mm512_andnot_ps(signBit, v[j])); + } + const float amax = _mm512_reduce_max_ps(vamax); + + // Quantize these floats + const float iscale = 127.f / amax; + y[i].d = GGML_FP32_TO_FP16(1 / iscale); + const float id = ( amax != 0.0f ) ? iscale : 0.f; + const __m512 vscale = _mm512_set1_ps(id); + + // Apply multiplier and round to nearest integer + for (int j = 0; j < kVecs; ++j) { + v[j] = _mm512_mul_ps(v[j], vscale); + v[j] = _mm512_roundscale_ps(v[j], (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC)); + } + + // Pack to epi8 vecs + for (int j = 0; j < kVecs / 4; ++j) { + __m128i q8_0 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 0])); + __m128i q8_1 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 1])); + __m128i q8_2 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 2])); + __m128i q8_3 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 3])); + + __m256i q8_01 = _mm256_insertf128_si256(_mm256_castsi128_si256(q8_0), (q8_1), 1); + __m256i q8_23 = _mm256_insertf128_si256(_mm256_castsi128_si256(q8_2), (q8_3), 1); + + vq[j] = _mm512_inserti32x8(_mm512_castsi256_si512(q8_01), q8_23, 1); + _mm512_storeu_si512((__m512i *)(y[i].qs + j * 64), vq[j]); + } + + // Compute the bsums with vnni + transpose_16x4_32bit(vq, vq_packed); + + const __m512i one = _mm512_set1_epi8(1); + __m512i sum = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + sum = _mm512_dpbusd_epi32(sum, one, vq_packed[k]); + } + _mm256_storeu_si256((__m256i *)(y[i].bsums), _mm512_cvtepi32_epi16(sum)); + } +} + +// quantize A from float to `vec_dot_type` +template +inline void from_float(const float * x, char * vy, int64_t k); + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { + // FIXME: using unoptimized reference impl until moved to CPU backend + quantize_row_q8_0_ref(x, (block_q8_0 *)vy, k); +} + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { + quantize_row_q8_1_ref(x, (block_q8_1 *)vy, k); +} + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { +#if 1 + // TODO: this is reference impl! + quantize_row_q8_K_ref(x, (block_q8_K *)vy, k); +#else + quantize_row_q8_K_vnni(x, vy, k); +#endif +} + +// load A from memory to array when nrows can not fill in whole tile +void unpack_A(int8_t * RESTRICT tile, const block_q8_0 * RESTRICT A, int lda, int nr) { + assert(nr != TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +void unpack_A(int8_t * RESTRICT tile, const block_q8_1 * RESTRICT A, int lda, int nr) { + assert(nr != TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +template +void unpack_A(int8_t * RESTRICT tile, const block_q8_K * RESTRICT A, int lda, int k, int nr) { + assert(nr <= TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs + k * 32)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +template <> +void unpack_A(int8_t * RESTRICT tile, const block_q8_K * RESTRICT A, int lda, int k, int nr) { + assert(nr <= TILE_M); + // zero padding k from 16 to 32, so that we don't have to re-config amx + const __m128i zero = _mm_setzero_si128(); + for (int m = 0; m < nr; ++m) { + const __m128i v = _mm_loadu_si128((const __m128i *)(A[m * lda].qs + k * 16)); + const __m256i r = _mm256_insertf128_si256(_mm256_castsi128_si256(v), zero, 1); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), r); + } +} + +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) +inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8(0xF); + return _mm256_and_si256(lowMask, bytes); +} + +// used for block_q4_K +inline __m512i bytes_from_nibbles_64(const uint8_t * rsi) { + const __m256i tmp = _mm256_loadu_si256((const __m256i *)rsi); + const __m256i lowMask = _mm256_set1_epi8(0xF); + const __m256i q4l = _mm256_and_si256(tmp, lowMask); + const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(tmp, 4), lowMask); + return _mm512_inserti32x8(_mm512_castsi256_si512(q4l), q4h, 1); +} + +// used for block_q5_K +inline __m512i bytes_from_nibbles_64(const uint8_t * qs, const uint8_t * qh, int k) { + const __m256i lowMask = _mm256_set1_epi8(0xF); + __m256i hmask = _mm256_set1_epi8(1); + hmask = _mm256_slli_epi16(hmask, k); + + const __m256i q5bits = _mm256_loadu_si256((const __m256i *)qs); + const __m256i hbits = _mm256_loadu_si256((const __m256i *)qh); + + const __m256i q5l_0 = _mm256_and_si256(q5bits, lowMask); + const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), k + 0), 4); + const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), lowMask); + const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), k + 1), 4); + const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); + + return _mm512_inserti32x8(_mm512_castsi256_si512(q5_0), q5_1, 1); +} + +// used for block_q6_K +inline void bytes_from_nibbles_128(__m512i& r0, __m512i& r1, const uint8_t * qs, const uint8_t * qh) { + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i m2 = _mm256_set1_epi8(0x3); + + const __m256i q6bits1 = _mm256_loadu_si256((const __m256i *)qs); + const __m256i q6bits2 = _mm256_loadu_si256((const __m256i *)(qs + 32)); + const __m256i q6bitsH = _mm256_loadu_si256((const __m256i *)qh); + + const __m256i q6h_0 = _mm256_slli_epi16(_mm256_and_si256( q6bitsH, m2), 4); + const __m256i q6h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 2), m2), 4); + const __m256i q6h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 4), m2), 4); + const __m256i q6h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 6), m2), 4); + + const __m256i q6_0 = _mm256_or_si256(_mm256_and_si256(q6bits1, m4), q6h_0); + const __m256i q6_1 = _mm256_or_si256(_mm256_and_si256(q6bits2, m4), q6h_1); + const __m256i q6_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q6bits1, 4), m4), q6h_2); + const __m256i q6_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q6bits2, 4), m4), q6h_3); + + r0 = _mm512_inserti32x8(_mm512_castsi256_si512(q6_0), q6_1, 1); + r1 = _mm512_inserti32x8(_mm512_castsi256_si512(q6_2), q6_3, 1); +} + +inline __m512i packNibbles(__m512i r0, __m512i r1) { + return _mm512_or_si512(r0, _mm512_slli_epi16(r1, 4)); +} + +template +inline void pack_qs(void * RESTRICT packed_B, const TB * RESTRICT B, int KB) { + int8_t tmp[8 * 64]; + __m256i v[8], v2[8]; + for (int n = 0; n < 8; ++n) { + v[n] = bytes_from_nibbles_32(B[n * KB].qs); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)(tmp + n * 64), v2[n]); + } + for (int n = 0; n < 8; ++n) { + v[n] = bytes_from_nibbles_32(B[(n + 8) * KB].qs); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)(tmp + n * 64 + 32), v2[n]); + } + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < 8; n += 2) { + __m512i r0 = _mm512_loadu_si512((const __m512i *)(tmp + n * 64)); + __m512i r1 = _mm512_loadu_si512((const __m512i *)(tmp + n * 64 + 64)); + __m512i r1r0 = packNibbles(r0, r1); + _mm512_storeu_si512((__m512i *)((char *)packed_B + n * 32), r1r0); + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q8_0 * RESTRICT B, int KB) { + __m256i v[8], v2[8]; + for (int n = 0; n < 8; ++n) { + v[n] = _mm256_loadu_si256((const __m256i *)(B[n * KB].qs)); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)((char *)packed_B + n * 64), v2[n]); + } + for (int n = 0; n < 8; ++n) { + v[n] = _mm256_loadu_si256((const __m256i *)(B[(n + 8) * KB].qs)); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)((char *)packed_B + n * 64 + 32), v2[n]); + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q4_K * RESTRICT B, int KB) { + __m512i v[16]; + // QK_K 256 with 8 groups, handle 2 groups at a time + char * pb = (char *)packed_B; + for (int k = 0; k < QK_K / 64; ++k) { + // pack 2 groups { n, g, k} to {g, k/4, 4n} + // e.g. {16, 2, 32} to {2, 8, 64} + for (int n = 0; n < TILE_N; ++n) { + v[n] = bytes_from_nibbles_64(B[n * KB].qs + k * 32); + } + + transpose_16x16_32bit(v); + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < TILE_N; n += 2) { + _mm512_storeu_si512((__m512i *)pb, packNibbles(v[n], v[n + 1])); + pb += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q5_K * RESTRICT B, int KB) { + __m512i v[16]; + const __m512i lowMask = _mm512_set1_epi8(0xF); + // QK_K 256 with 8 groups, handle 2 groups at a time + char * pb = (char *)packed_B; + char * ph = (char *)packed_B + (QK_K / 2) * TILE_N; + for (int k = 0; k < QK_K / 64; ++k) { + // pack 2 groups { n, g, k} to {g, k/4, 4n} + // e.g. {16, 2, 32} to {2, 8, 64} + for (int n = 0; n < TILE_N; ++n) { + v[n] = bytes_from_nibbles_64(B[n * KB].qs + k * 32, B[n * KB].qh, /* group */2 * k); + } + + transpose_16x16_32bit(v); + + // 1. pack lower 4bits with 2 groups + for (int n = 0; n < TILE_N; n += 2) { + // get lower 4 bits + const __m512i r0 = _mm512_and_si512(v[n], lowMask); + const __m512i r1 = _mm512_and_si512(v[n + 1], lowMask); + _mm512_storeu_si512((__m512i *)pb, packNibbles(r0, r1)); pb += 64; + } + + // 2. pack higher 1bit with 2 groups + const __m512i hmask = _mm512_set1_epi8(0x10); + for (int g = 0; g < 2; ++g) { + __m512i hbits = _mm512_setzero_si512(); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 0], hmask), 4)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 1], hmask), 3)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 2], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 3], hmask), 1)); + hbits = _mm512_add_epi8(hbits, _mm512_and_si512(v[g * 8 + 4], hmask) ); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 5], hmask), 1)); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 6], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 7], hmask), 3)); + _mm512_storeu_si512((__m512i *)ph, hbits); ph += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q6_K * RESTRICT B, int KB) { + __m512i v[32]; + const __m512i lowMask = _mm512_set1_epi8(0xF); + // QK_K 256 with 8 groups, handle 4 groups at a time + char * pb = (char *)packed_B; + char * ph = (char *)packed_B + (QK_K / 2) * TILE_N; + for (int k = 0; k < QK_K / 128; ++k) { + for (int n = 0; n < TILE_N; ++n) { + bytes_from_nibbles_128(v[n], v[n + 16], B[n * KB].ql + k * 64, B[n * KB].qh + k * 32); + } + + // top half: group 0,1 or 4,5; bottom half: group 2,3 or 6,7 + transpose_16x16_32bit(v); + transpose_16x16_32bit(v + 16); + + // 1. pack lower 4bits with 4 groups + for (int n = 0; n < 32; n += 2) { + const __m512i r0 = _mm512_and_si512(v[n], lowMask); + const __m512i r1 = _mm512_and_si512(v[n + 1], lowMask); + _mm512_storeu_si512((__m512i *)pb, packNibbles(r0, r1)); pb += 64; + } + + // 2. pack higher 2bit with 4 groups + const __m512i hmask = _mm512_set1_epi8(0x30); + for (int g = 0; g < 8; ++g) { + __m512i hbits = _mm512_setzero_si512(); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 4 + 0], hmask), 4)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 4 + 1], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_and_si512(v[g * 4 + 2], hmask) ); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 4 + 3], hmask), 2)); + _mm512_storeu_si512((__m512i *)ph, hbits); ph += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_iq4_xs * RESTRICT B, int KB) { + __m512i v[16]; + char * pb = (char *)packed_B; + for (int k = 0; k < QK_K / 64; ++k) { + for (int n = 0; n < TILE_N; ++n) { + __m256i r0 = bytes_from_nibbles_32(B[n * KB].qs + k * 32 + 0); + __m256i r1 = bytes_from_nibbles_32(B[n * KB].qs + k * 32 + 16); + v[n] = _mm512_inserti32x8(_mm512_castsi256_si512(r0), r1, 1); + } + + transpose_16x16_32bit(v); + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < TILE_N; n += 2) { + _mm512_storeu_si512((__m512i *)pb, packNibbles(v[n], v[n + 1])); + pb += 64; + } + } +} + +// pack B to vnni formats in 4bits or 8 bits +void pack_B(void * RESTRICT packed_B, const block_q4_0 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K / 2); + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + } +} + +void pack_B(void * RESTRICT packed_B, const block_q4_1 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K / 2); + ggml_half * m0 = d0 + TILE_N; + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + m0[n] = B[n * KB].m; + } +} + +inline void s8s8_compensation(void * RESTRICT packed_B) { + // packed_B layout: + // quants {TILE_N, TILEK} int8_t + // d0 {TILE_N} ggml_half + // comp {TILE_N} int32_t + const int offset = TILE_N * TILE_K + TILE_N * sizeof(ggml_half); + __m512i vcomp = _mm512_setzero_si512(); + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + for (int k = 0; k < 8; ++k) { + __m512i vb = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + k * 64)); + vcomp = _mm512_dpbusd_epi32(vcomp, off, vb); + } + _mm512_storeu_si512((__m512i *)((char *)(packed_B) + offset), vcomp); +} + +void pack_B(void * RESTRICT packed_B, const block_q8_0 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K); + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + } + s8s8_compensation(packed_B); +} + +// convert 8 * {min, scale} from int6 to int8 +inline void unpack_mins_and_scales(const uint8_t * scales, uint32_t * utmp) { + const uint32_t kmask1 = 0x3f3f3f3f; + const uint32_t kmask2 = 0x0f0f0f0f; + const uint32_t kmask3 = 0x03030303; + + memcpy(utmp, scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// scales {8, TILE_N} uint8 +// mins {8, TILE_N} uint8 +// d {TILE_N} ggml_half +// dmin {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q4_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N); + uint8_t * mins = scales + 8 * TILE_N; + ggml_half * d = reinterpret_cast(mins + 8 * TILE_N); + ggml_half * dmin = d + TILE_N; + + union { + uint32_t u32[4]; + uint8_t u8[16]; + } s; + + for (int n = 0; n < TILE_N; ++n) { + unpack_mins_and_scales(B[n * KB].scales, s.u32); + for (int k = 0; k < 8; ++k) { + scales[k * TILE_N + n] = s.u8[k]; + mins[(k >> 1) * TILE_N * 2 + n * 2 + (k & 0x1)] = s.u8[k + 8]; + } + d[n] = B[n * KB].d; + dmin[n] = B[n * KB].dmin; + } +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// qh {8, TILE_N, 4} uint8 +// scales {8, TILE_N} uint8 +// mins {8, TILE_N} uint8 +// d {TILE_N} ggml_half +// dmin {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q5_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N); + uint8_t * mins = scales + 8 * TILE_N; + ggml_half * d = reinterpret_cast(mins + 8 * TILE_N); + ggml_half * dmin = d + TILE_N; + + union { + uint32_t u32[4]; + uint8_t u8[16]; + } s; + + for (int n = 0; n < TILE_N; ++n) { + unpack_mins_and_scales(B[n * KB].scales, s.u32); + for (int k = 0; k < 8; ++k) { + scales[k * TILE_N + n] = s.u8[k]; + mins[(k >> 1) * TILE_N * 2 + n * 2 + (k & 0x1)] = s.u8[k + 8]; + } + d[n] = B[n * KB].d; + dmin[n] = B[n * KB].dmin; + } +} + +// packed_B layout: +// quants {16, TILE_N, 8} uint8 +// qh {16, TILE_N, 4} uint8 +// scales {16, TILE_N} uint8 +// d {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q6_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N); + ggml_half * d = reinterpret_cast(scales + 16 * TILE_N); + for (int n = 0; n < TILE_N; ++n) { + const int8_t * ps = B[n * KB].scales; + for (int k = 0; k < 16; ++k) { + scales[k * TILE_N + n] = ps[k]; + } + d[n] = B[n * KB].d; + } +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// scales {8, TILE_N} int8 +// d {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_iq4_xs * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + int8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N); + ggml_half * d = reinterpret_cast(scales + 8 * TILE_N); + + // pack the scales + for (int n = 0; n < TILE_N; ++n) { + uint16_t sh = B[n * KB].scales_h; + for (int k = 0; k < 8; k += 2) { + const int16_t ls1 = ((B[n * KB].scales_l[k / 2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((B[n * KB].scales_l[k / 2] >> 4) | ((sh << 2) & 0x30)) - 32; + scales[(k + 0) * TILE_N + n] = ls1; + scales[(k + 1) * TILE_N + n] = ls2; + sh >>= 4; + } + d[n] = B[n * KB].d; + } +} + +template> +void unpack_B(packed_B_t * RESTRICT tile, const void * RESTRICT packed_B) { + GGML_UNUSED(tile); + GGML_UNUSED(packed_B); +}; + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B) { + const __m512i off = _mm512_set1_epi8(8); + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + n * 32)); + const __m512i r0 = _mm512_sub_epi8(_mm512_and_si512(bytes, lowMask), off); + const __m512i r1 = _mm512_sub_epi8(_mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask), off); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(uint8_t * RESTRICT tile, const void * RESTRICT packed_B) { + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + n * 32)); + const __m512i r0 = _mm512_and_si512(bytes, lowMask); + const __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +// packed_B_t for QKK is int8_t +template +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + const int packed_B_group_size = QK_K / 2 * TILE_N / 8; + const char * packed_B_group = (const char *)packed_B + k * packed_B_group_size; + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(packed_B_group + n * 32); + const __m512i r0 = _mm512_and_si512(bytes, lowMask); + const __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + // lower 4bits, stride 256 bytes + const int packed_l4_group_size = QK_K / 2 * TILE_N / 8; + const char * pb = (const char *)packed_B + k * packed_l4_group_size; + + // higher 1bit, stride 64 bytes + const int packed_h1_group_size = QK_K / 8 * TILE_N / 8; + const char * ph = (const char *)packed_B + (QK_K / 2) * TILE_N + k * packed_h1_group_size; + const __m512i hbits = _mm512_loadu_si512(ph); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + __m512i hmask0 = _mm512_set1_epi8(0x1); + __m512i hmask1 = _mm512_set1_epi8(0x2); + + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(pb + n * 32); + __m512i r0 = _mm512_and_si512(bytes, lowMask); + __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i h0 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask0), n), 4); + __m512i h1 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), n + 1), 4); + + hmask0 = _mm512_slli_epi16(hmask0, 2); + hmask1 = _mm512_slli_epi16(hmask1, 2); + r0 = _mm512_add_epi8(r0, h0); + r1 = _mm512_add_epi8(r1, h1); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + // lower 4bits, stride 128 bytes + const int packed_l4_group_size = QK_K / 2 * TILE_N / 16; + const char * pb = (const char *)packed_B + k * packed_l4_group_size; + + // higher 2bits, stride 64 bytes + const int packed_h2_group_size = QK_K / 4 * TILE_N / 16; + const char * ph = (const char *)packed_B + (QK_K / 2) * TILE_N + k * packed_h2_group_size; + const __m512i hbits = _mm512_loadu_si512(ph); + + const __m512i off = _mm512_set1_epi8(32); + const __m512i lowMask = _mm512_set1_epi8(0xF); + __m512i hmask0 = _mm512_set1_epi8(0x3); // 0011 + __m512i hmask1 = _mm512_set1_epi8(0xC); // 1100 + + // notes: skip zero padding from row4 to row7 as we have done so in `unpack_A` + __m512i bytes = _mm512_loadu_si512(pb); + __m512i r0 = _mm512_and_si512(bytes, lowMask); + __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i h0 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask0), 4); + __m512i h1 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask1), 2); + _mm512_storeu_si512((__m512i *)(tile + 0), _mm512_sub_epi8(_mm512_add_epi8(r0, h0), off)); + _mm512_storeu_si512((__m512i *)(tile + 64), _mm512_sub_epi8(_mm512_add_epi8(r1, h1), off)); + + hmask0 = _mm512_slli_epi16(hmask0, 4); + hmask1 = _mm512_slli_epi16(hmask1, 4); + + bytes = _mm512_loadu_si512(pb + 64); + r0 = _mm512_and_si512(bytes, lowMask); + r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + h0 = _mm512_and_si512(hbits, hmask0); + h1 = _mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), 2); + _mm512_storeu_si512((__m512i *)(tile + 128), _mm512_sub_epi8(_mm512_add_epi8(r0, h0), off)); + _mm512_storeu_si512((__m512i *)(tile + 192), _mm512_sub_epi8(_mm512_add_epi8(r1, h1), off)); +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + static const __m512i values128 = _mm512_set_epi8( + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127 + ); + + const int packed_B_group_size = QK_K / 2 * TILE_N / 8; + const char * pb = (const char *)packed_B + k * packed_B_group_size; + const __m512i lowMask = _mm512_set1_epi8(0xF); + + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(pb + n * 32); + const __m512i r0 = _mm512_shuffle_epi8(values128, _mm512_and_si512(bytes, lowMask)); + const __m512i r1 = _mm512_shuffle_epi8(values128, _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask)); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template +struct acc_C {}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_0 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_1 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + const __m512 vm0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset + TILE_N * sizeof(ggml_half)))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vs1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].s)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + vsum = _mm512_fmadd_ps(vm0, vs1, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_0 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N); + const uint8_t * mins = scales + 8 * TILE_N; + const ggml_half * d0 = reinterpret_cast(mins + 8 * TILE_N); + const ggml_half * dmin = d0 + TILE_N; + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)dmin)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vdm = _mm512_mul_ps(_mm512_set1_ps(-d1), vdmin); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[m * lda].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, _mm512_castsi128_si512(q8s)); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + vsum = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc_m), vdm, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N); + const uint8_t * mins = scales + 8 * TILE_N; + const ggml_half * d0 = reinterpret_cast(mins + 8 * TILE_N); + const ggml_half * dmin = d0 + TILE_N; + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)dmin)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vdm = _mm512_mul_ps(_mm512_set1_ps(-d1), vdmin); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[m * lda].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, _mm512_castsi128_si512(q8s)); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + vsum = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc_m), vdm, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N); + const ggml_half * d0 = reinterpret_cast(scales + 16 * TILE_N); + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const int8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N); + const ggml_half * d0 = reinterpret_cast(scales + 8 * TILE_N); + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template constexpr int get_quants_size(); +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N; } + +// used for QKK format +template ::value, int>::type = 0> +inline void scale_C(const int32_t * RESTRICT tile, int32_t * RESTRICT sumi, const void * packed_B, int k, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + get_quants_size()); + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(scales + k * TILE_N))); + + for (int m = 0; m < nr; ++m) { + __m512i vsumi; + if (is_acc) { + vsumi = _mm512_loadu_si512(sumi + m * TILE_N); + } else { + vsumi = _mm512_setzero_si512(); + } + __m512i vtile = _mm512_loadu_si512(tile + m * TILE_N); + vsumi = _mm512_add_epi32(vsumi, _mm512_mullo_epi32(vtile, vscale)); + _mm512_storeu_si512((__m512i *)(sumi + m * TILE_N), vsumi); + } +} + +template +struct tinygemm_kernel_avx { + static void apply(int K, const TA * RESTRICT A, const TB * RESTRICT B, TC * RESTRICT C, int ldc) { + GGML_UNUSED(K); + GGML_UNUSED(A); + GGML_UNUSED(B); + GGML_UNUSED(C); + GGML_UNUSED(ldc); + } +}; + +template +struct tinygemm_kernel_avx { + static void apply(int K, const float * RESTRICT A, const ggml_fp16_t * RESTRICT B, float * RESTRICT C, int ldc) { + constexpr int ROWS = BLOCK_M; + constexpr int COLS = BLOCK_N; + assert(BLOCK_K == 16); + + __m512 va; + __m512 vb[COLS]; + __m512 vc[ROWS * COLS]; + + auto loadc = [&](int idx) { + vc[idx] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int idx, int k) { + // TODO: use `constexpr` here to get rid of interger div + // when upgraded to C++17 + const int row = idx / COLS; + const int col = idx % COLS; + + if (col == 0) { + va = _mm512_loadu_ps(A + row * K + k); + } + if (row == 0) { + vb[col] = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(B + col * K + k))); + } + vc[idx] = _mm512_fmadd_ps(va, vb[col], vc[idx]); + }; + + for (int k = 0; k < K; k += 16) { + Unroll{}(compute, k); + } + + auto storec = [&](int idx) { + const int row = idx / COLS; + const int col = idx % COLS; + C[row * ldc + col] = _mm512_reduce_add_ps(vc[idx]); + }; + Unroll{}(storec); + } +}; + +#define LAUNCH_TINYGEMM_KERNEL_AVX(MB_SIZE, NB_SIZE) \ + tinygemm_kernel_avx::apply( \ + K, (const float *)src1->data + mb_start * K, \ + (const type *)src0->data + nb_start * K, \ + (float *)dst->data + mb_start * ldc + nb_start, ldc); + + +// re-organize in the format {NB, KB, TILE_SIZE}: +#define PACKED_INDEX(n, k, KB, tile_size) (n * KB + k) * tile_size + +template +void convert_B_packed_format(void * RESTRICT packed_B, const TB * RESTRICT B, int N, int K, int n_threads) { + const int NB = N / TILE_N; + const int KB = K / BLOCK_K; + const int TILE_SIZE = get_tile_size(); + + // parallel on NB should be enough + parallel_for(n_threads, NB, [&](int begin, int end) { + for (int n = begin; n < end; ++n) { + for (int k = 0; k < KB; ++k) { + int n0 = n * TILE_N; + pack_B((char *)packed_B + PACKED_INDEX(n, k, KB, TILE_SIZE), &B[n0 * KB + k], KB); + } + } + }); +} + +template +struct tinygemm_kernel_vnni {}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_0); + + const block_q8_0 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512 vc[COLS]; + __m512 vd1; + + // sum of offsets, shared across COLS + // + // avx512-vnni does not have `_mm512_dpbssd_epi32`, + // need to transfrom ss to us: + // a * (b - 8) is equavilent to b * a - 8 * a + // s u u u s u s + // + __m512i vcomp; + + const __m512i off = _mm512_set1_epi8(8); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a and compute compensation + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + vcomp = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + vcomp = _mm512_dpbusd_epi32(vcomp, off, va[k]); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + } + + // load b + __m512i vsum = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; k += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 32)); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va[k + 0]); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va[k + 1]); + } + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + vsum = _mm512_sub_epi32(vsum, vcomp); + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_1); + + const block_q8_1 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512i vb[8]; + __m512 vc[COLS]; + __m512 vd1, vs1; + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + vs1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].s)); + } + + // load b + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; k += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 32)); + vb[k + 0] = _mm512_and_si512(bytes, lowMask); + vb[k + 1] = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + } + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + const __m512 vm0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset + TILE_N * sizeof(ggml_half)))); + + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + vsum = _mm512_dpbusd_epi32(vsum, vb[k], va[k]); + } + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + vc[col] = _mm512_fmadd_ps(vm0, vs1, vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q8_0) + TILE_N * sizeof(int32_t); + + const block_q8_0 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512i vb[8]; + __m512 vc[COLS]; + __m512 vd1; + + // Notes: s8s8 igemm compensation in avx512-vnni + // change s8s8 to u8s8 with compensate + // a * b = (a + 128) * b - 128 * b + // s s u s u s + // + // (128 * b is pre-computed when packing B to vnni formats) + // + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a and add offset 128 + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + va[k] = _mm512_add_epi8(va[k], off); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + } + + // load b + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; ++k) { + vb[k] = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 64)); + } + const int offset = TILE_N * TILE_K; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + const int offset2 = TILE_N * TILE_K + TILE_N * sizeof(ggml_half); + const __m512i vcomp = _mm512_loadu_si512((const __m512i *)(b_ptr + offset2)); + + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + vsum = _mm512_dpbusd_epi32(vsum, va[k], vb[k]); + } + vsum = _mm512_sub_epi32(vsum, vcomp); + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_K) + TILE_N * 4; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // a.qs: 8 groups, 32 bytes each group (m256i) + __m512i va[8]; + // a.bsum: 8 groups, 2 bytes each group (m128i) + __m512i va_bsum; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_scales = (QK_K / 2) * TILE_N; + const int offset_mins = (QK_K / 2) * TILE_N + 8 * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + 16 * TILE_N; + const int offset_dmin = (QK_K / 2) * TILE_N + 16 * TILE_N + TILE_N * sizeof(ggml_half); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + // Notes: vnni formats in QK_K + // a) quants vnni format + // int8 {k/4, n, 4}, viewed as 2d {k/4, 4n}, k = 32 + // from {16, 32} to {8, 64} + // + // b) min vnni format + // int16 {k/2, n, 2}, viewed as 2d {k/2, 2n}, k = 8 + // from {16, 8} to {4, 32} + // + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + va[k_group] = _mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)(A[0 * KB + i].qs + k_group * 32))); + } + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + va_bsum = _mm512_castsi128_si512(q8s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // step 1: accumultate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 0), va[k_group]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 1), va[k_group]); + + __m512i bytes = _mm512_loadu_si512((const __m512i *)b_qs); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + + b_qs += 64; + } + // vacc += scale * (q8 @ q4) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + + // step 2: accumulate the mins + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, va_bsum); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_dmin))); + vc[col] = _mm512_fnmadd_ps(_mm512_cvtepi32_ps(acc_m), _mm512_mul_ps(vdmin, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q5_K) + TILE_N * 4; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // a.qs: 8 groups, 32 bytes each group (m256i) + __m512i va[8]; + // a.bsum: 8 groups, 2 bytes each group (m128i) + __m512i va_bsum; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_qh = (QK_K / 2) * TILE_N; + const int offset_scales = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N; + const int offset_mins = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 8 * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 16 * TILE_N; + const int offset_dmin = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 16 * TILE_N + TILE_N * sizeof(ggml_half); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + // Q5_K and Q4_K shares the same vnni formats, refer to notes above. + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + va[k_group] = _mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)(A[0 * KB + i].qs + k_group * 32))); + } + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + va_bsum = _mm512_castsi128_si512(q8s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // step 1: accumultate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + const char * b_qh = b_ptr + offset_qh; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + __m512i vsum = _mm512_setzero_si512(); + __m512i hmask0 = _mm512_set1_epi8(0x1); + __m512i hmask1 = _mm512_set1_epi8(0x2); + __m512i hbits = _mm512_loadu_si512((const __m512i *)(b_qh + k_group * 64)); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 0), va[k_group]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 1), va[k_group]); + + __m512i bytes = _mm512_loadu_si512((const __m512i *)b_qs); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + + __m512i vh0 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask0), k), 4); + __m512i vh1 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), k + 1), 4); + + hmask0 = _mm512_slli_epi16(hmask0, 2); + hmask1 = _mm512_slli_epi16(hmask1, 2); + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + + b_qs += 64; + } + // vacc += scale * (q8 @ q5) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + + // step 2: accumulate the mins + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, va_bsum); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_dmin))); + vc[col] = _mm512_fnmadd_ps(_mm512_cvtepi32_ps(acc_m), _mm512_mul_ps(vdmin, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q6_K); + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // load the 256 bytes from A to 4 avx512 vectors + __m512i va[4]; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_qh = (QK_K / 2) * TILE_N; + const int offset_scales = (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N + 16 * TILE_N; + + // compensation + __m512i vcomp; + + const __m512i m32s = _mm512_set1_epi32(32); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + if (col == 0) { + // load a + va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); + va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); + va[2] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 128)); + va[3] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 192)); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + vcomp = _mm512_mullo_epi32(_mm512_cvtepi16_epi32(q8sums), m32s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // accmulate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + const char * b_qh = b_ptr + offset_qh; + int mask = 0; + for (int k_group = 0; k_group < QK_K / 16; ++k_group) { + int r = k_group >> 2; + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + __m512i vsum = _mm512_setzero_si512(); + __m512i hmask = _mm512_set1_epi8(0x3); + + __m512i bytes = _mm512_loadu_si512(b_qs); + __m512i hbits = _mm512_loadu_si512(b_qh); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i vh0 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask), 4); + __m512i vh1 = _mm512_slli_epi16(_mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 2)), 2); + + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + + va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + bytes = _mm512_loadu_si512(b_qs); + vb0 = _mm512_and_si512(bytes, lowMask); + vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vh0 = _mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 4)); + vh1 = _mm512_srli_epi16(_mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 6)), 2); + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + b_qh += 64; + + // B * A - 32 * A + __m512i vmask = _mm512_set1_epi32(k_group); + vsum = _mm512_sub_epi32(vsum, _mm512_permutexvar_epi32(vmask, vcomp)); + + // vacc += scale * (q8 @ q6) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_iq4_xs) + TILE_N * 2; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // load the 256 bytes from A to 4 avx512 vectors + __m512i va[4]; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_scales = (QK_K / 2) * TILE_N ; + const int offset_d0 = (QK_K / 2) * TILE_N + 8 * TILE_N; + + // compensation + __m512i vcomp; + + const __m256i m128s = _mm256_set1_epi16(128); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + const __m512i values128 = _mm512_set_epi8( + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127 + ); + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + const __m512i values256 = _mm512_add_epi8(values128, off); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + if (col == 0) { + // load a + va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); + va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); + va[2] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 128)); + va[3] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 192)); + + // compensation: 128 * A + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + vcomp = _mm512_castsi256_si512(_mm256_madd_epi16(q8sums, m128s)); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // accmulate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + int mask = 0; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + int r = k_group >> 1; + __m512i vmask = _mm512_set1_epi32(k_group); + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + __m512i bytes = _mm512_loadu_si512(b_qs); + __m512i vb0 = _mm512_shuffle_epi8(values256, _mm512_and_si512(bytes, lowMask)); + __m512i vb1 = _mm512_shuffle_epi8(values256, _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask)); + + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + } + // (B + 128) * A - 128 * A + vsum = _mm512_sub_epi32(vsum, _mm512_permutexvar_epi32(vmask, vcomp)); + + // vacc += scale * (q8 @ q4) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +#define LAUNCH_TINYGEMM_KERNEL_VNNI(NB_SIZE) \ + tinygemm_kernel_vnni::apply( \ + KB, (const char *)wdata + 0 * row_size_A, \ + (const char *)src0->data + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE), \ + (float *) dst->data + 0 * N + nb_start, ldc) + +template ::value, int>::type = 0> +void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const void * RESTRICT _B, TC * RESTRICT C, int ldc) { + using packed_B_t = packed_B_type; + const int TILE_SIZE = get_tile_size(); + const bool need_unpack = do_unpack::value; + + GGML_ASSERT(M <= 2 * TILE_M && N == 2 * TILE_N); + const TA * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + const int m0 = std::min(M, TILE_M); + const int m1 = std::max(M - TILE_M, 0); + const int lda = KB * sizeof(TA); + //const int ldb = KB * sizeof(TB); + + static thread_local packed_B_t Tile0[TILE_N * TILE_K]; + static thread_local packed_B_t Tile1[TILE_N * TILE_K]; + static thread_local int8_t Tile23[TILE_M * TILE_K]; + + static thread_local int32_t TileC0[TILE_M * TILE_N * 4]; + static thread_local int32_t TileC1[TILE_M * TILE_N * 4]; + + // double buffering C to interleave avx512 and amx + int32_t * C_cur = TileC0; + int32_t * C_pre = TileC1; + + auto Tile4 = [&](int32_t * base) { return base; }; + auto Tile5 = [&](int32_t * base) { return base + TILE_M * TILE_N; }; + auto Tile6 = [&](int32_t * base) { return base + 2 * TILE_M * TILE_N; }; + auto Tile7 = [&](int32_t * base) { return base + 3 * TILE_M * TILE_N; }; + + if (M == 2 * TILE_M) { + // i = 0 + const char * B_blk0 = B + PACKED_INDEX(0, 0, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, 0, KB, TILE_SIZE); + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + + _tile_zero(TMM4); + _tile_loadd(TMM2, A[0].qs, lda); + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_stored(TMM4, Tile4(C_pre), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM5); + _tile_loadd(TMM3, A[TILE_M * KB + 0].qs, lda); + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_stored(TMM5, Tile5(C_pre), TILE_N * sizeof(int32_t)); + + if (need_unpack) { + unpack_B(Tile1, B_blk0); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + + _tile_zero(TMM6); + _tile_dpbssd(TMM6, TMM2, TMM1); + _tile_stored(TMM6, Tile6(C_pre), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM7); + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM7, Tile7(C_pre), TILE_N * sizeof(int32_t)); + + for (int i = 1; i < KB; ++i) { + // index of previous iter + const int ii = i - 1; + const char * B_blk0 = B + PACKED_INDEX(0, i, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, i, KB, TILE_SIZE); + GGML_DISPATCH_BOOL(ii > 0, is_acc, [&] { + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + _tile_zero(TMM4); + _tile_loadd(TMM2, A[i].qs, lda); + acc_C::apply(C, ldc, Tile4(C_pre), &A[ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_stored(TMM4, Tile4(C_cur), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM5); + _tile_loadd(TMM3, A[TILE_M * KB + i].qs, lda); + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_stored(TMM5, Tile5(C_cur), TILE_N * sizeof(int32_t)); + + if (need_unpack) { + unpack_B(Tile1, B_blk1); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + _tile_zero(TMM6); + acc_C::apply(C + TILE_N, ldc, Tile6(C_pre), &A[ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM6, TMM2, TMM1); + _tile_stored(TMM6, Tile6(C_cur), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM7); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM7, Tile7(C_cur), TILE_N * sizeof(int32_t)); + + std::swap(C_cur, C_pre); + }); + } + // final accumulation + { + int ii = KB - 1; + acc_C::apply(C, ldc, Tile4(C_pre), &A[ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_N, ldc, Tile6(C_pre), &A[ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + } + } else { + for (int i = 0; i < KB; ++i) { + _tile_zero(TMM4); + _tile_zero(TMM6); + if (m1 != 0) { + _tile_zero(TMM5); + _tile_zero(TMM7); + } + + const char * B_blk0 = B + PACKED_INDEX(0, i, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, i, KB, TILE_SIZE); + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + + if (need_unpack) { + unpack_B(Tile1, B_blk1); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + + if (m0 == TILE_M) { + _tile_loadd(TMM2, A[i].qs, lda); + } else { + unpack_A(Tile23, &A[i], KB, m0); + _tile_loadd(TMM2, Tile23, TILE_K); + } + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_dpbssd(TMM6, TMM2, TMM1); + + _tile_stored(TMM4, Tile4(C_cur), TILE_N * sizeof(int32_t)); + _tile_stored(TMM6, Tile6(C_cur), TILE_N * sizeof(int32_t)); + + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C, ldc, Tile4(C_cur), &A[i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m0); + acc_C::apply(C + TILE_N, ldc, Tile6(C_cur), &A[i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m0); + }); + + if (m1 != 0) { + unpack_A(Tile23, &A[TILE_M * KB + i], KB, m1); + _tile_loadd(TMM3, Tile23, TILE_K); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM5, Tile5(C_cur), TILE_N * sizeof(int32_t)); + _tile_stored(TMM7, Tile7(C_cur), TILE_N * sizeof(int32_t)); + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_cur), &A[TILE_M * KB + i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m1); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_cur), &A[TILE_M * KB + i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m1); + }); + } + } + } + return; +} + +template ::value, int>::type = 0> +void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + static_assert(std::is_same::value); + const int TILE_SIZE = get_tile_size(); + + GGML_ASSERT(M <= 2 * TILE_M && N == 2 * TILE_N); + const TA * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + const int m0 = std::min(M, TILE_M); + const int m1 = std::max(M - TILE_M, 0); + //const int lda = KB * sizeof(TA); + + static thread_local int8_t Tile0[TILE_N * TILE_K]; + static thread_local int8_t Tile1[TILE_N * TILE_K]; + static thread_local int8_t Tile23[TILE_M * TILE_K]; + + // mat mul result for each group + static thread_local int32_t Tile4[TILE_M * TILE_N]; + static thread_local int32_t Tile5[TILE_M * TILE_N]; + static thread_local int32_t Tile6[TILE_M * TILE_N]; + static thread_local int32_t Tile7[TILE_M * TILE_N]; + + // sum of each QK_K block, contains 8 groups, int32 + static thread_local int32_t Sumi4[TILE_M * TILE_N]; + static thread_local int32_t Sumi5[TILE_M * TILE_N]; + static thread_local int32_t Sumi6[TILE_M * TILE_N]; + static thread_local int32_t Sumi7[TILE_M * TILE_N]; + + const int k_group_size = std::is_same::value ? 16 : 32; + for (int i = 0; i < KB; ++i) { + // step 1: accumulate the quants across 8 groups, each group with 32 + for (int k = 0; k < QK_K / k_group_size; ++k) { + GGML_DISPATCH_BOOL(k > 0, is_acc, [&] { + _tile_zero(TMM4); + _tile_zero(TMM6); + + unpack_B(Tile0, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + + unpack_B(Tile1, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + + unpack_A(Tile23, &A[i], KB, k, m0); + _tile_loadd(TMM2, Tile23, TILE_K); + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_dpbssd(TMM6, TMM2, TMM1); + + _tile_stored(TMM4, Tile4, TILE_N * sizeof(int32_t)); + _tile_stored(TMM6, Tile6, TILE_N * sizeof(int32_t)); + + scale_C(Tile4, Sumi4, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k, m0); + scale_C(Tile6, Sumi6, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k, m0); + + if (m1 != 0) { + _tile_zero(TMM5); + _tile_zero(TMM7); + + unpack_A(Tile23, &A[TILE_M * KB + i], KB, k, m1); + _tile_loadd(TMM3, Tile23, TILE_K); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_dpbssd(TMM7, TMM3, TMM1); + + _tile_stored(TMM5, Tile5, TILE_N * sizeof(int32_t)); + _tile_stored(TMM7, Tile7, TILE_N * sizeof(int32_t)); + + scale_C(Tile5, Sumi5, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k, m1); + scale_C(Tile7, Sumi7, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k, m1); + } + }); + } + + // step 2: accmulate the mins + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C, ldc, Sumi4, &A[i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m0); + acc_C::apply(C + TILE_N, ldc, Sumi6, &A[i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m0); + if (m1 != 0) { + acc_C::apply(C + TILE_M * ldc, ldc, Sumi5, &A[TILE_M * KB + i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m1); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Sumi7, &A[TILE_M * KB + i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m1); + } + }); + } + return; +} + +} // anonymous namespace + +// get the packed tensor size for quantized weights +size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor) { + const enum ggml_type TYPE = tensor->type; + + const int K = tensor->ne[0]; // ne0: in_features + const int N = tensor->ne[1]; // ne1: out_features + + auto get_tensor_size = [&] { + size_t row_size_B{0}; + GGML_DISPATCH_QTYPES(TYPE, [&] { + row_size_B = get_row_size(K); + }); + return N * row_size_B; + }; + + if (qtype_has_amx_kernels(TYPE)) { + return get_tensor_size(); + } else { + // for f16, bf16 we don't do packing + return ggml_nbytes(tensor); + } +} + +// pack weight to vnni format +void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + + size_t alloc_size = ggml_backend_amx_get_alloc_size(tensor); + GGML_ASSERT(alloc_size == size); + + const enum ggml_type TYPE = tensor->type; + + const int K = tensor->ne[0]; // ne0: in_features + const int N = tensor->ne[1]; // ne1: out_features + +#if defined(_OPENMP) + // the buffer ctx is not initialized when .set_tensor is called + int n_threads = omp_get_num_threads(); +#else + int n_threads = 1; +#endif + + GGML_DISPATCH_QTYPES(TYPE, [&] { + convert_B_packed_format((void *)((char *)tensor->data + offset), (const type *)data, N, K, n_threads); + }); +} + +// NB: mixed dtype gemm with Advanced Matrix Extensions (Intel AMX) +// +// src0: weight in shape of {N, K}, quantized +// src1: input in shape of {M, K}, float32 +// dst: output in shape of {M, N}, float32 +// +// the function performs: dst = src1 @ src0.T +// +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { + struct ggml_tensor * src0 = dst->src[0]; + struct ggml_tensor * src1 = dst->src[1]; + + const enum ggml_type TYPE = src0->type; + + const int n_threads = ctx->n_threads; + + // f16 only has avx512 kernels for now, + // amx kernels will be added once 6th gen xeon is released. + const bool is_floating_type = TYPE == GGML_TYPE_F16; + + const int M = dst->ne[1]; + const int N = dst->ne[0]; + const int K = src0->ne[0]; + const int ldc = dst->nb[1] / dst->nb[0]; + + if (is_floating_type) { + constexpr int BLOCK_M = 4; + constexpr int BLOCK_N = 6; + const int MB = div_up(M, BLOCK_M); + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, MB * NB, [&](int begin, int end) { + GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] { + for (int i = begin; i < end; ++i) { + int mb = i / NB; + int nb = i % NB; + + int mb_start = mb * BLOCK_M; + int mb_size = std::min(BLOCK_M, M - mb_start); + int nb_start = nb * BLOCK_N; + int nb_size = std::min(BLOCK_N, N - nb_start); + + switch (mb_size << 4 | nb_size) { + case 0x12: LAUNCH_TINYGEMM_KERNEL_AVX(1, 2); break; + case 0x14: LAUNCH_TINYGEMM_KERNEL_AVX(1, 4); break; + case 0x16: LAUNCH_TINYGEMM_KERNEL_AVX(1, 6); break; + case 0x22: LAUNCH_TINYGEMM_KERNEL_AVX(2, 2); break; + case 0x24: LAUNCH_TINYGEMM_KERNEL_AVX(2, 4); break; + case 0x26: LAUNCH_TINYGEMM_KERNEL_AVX(2, 6); break; + case 0x32: LAUNCH_TINYGEMM_KERNEL_AVX(3, 2); break; + case 0x34: LAUNCH_TINYGEMM_KERNEL_AVX(3, 4); break; + case 0x36: LAUNCH_TINYGEMM_KERNEL_AVX(3, 6); break; + case 0x42: LAUNCH_TINYGEMM_KERNEL_AVX(4, 2); break; + case 0x44: LAUNCH_TINYGEMM_KERNEL_AVX(4, 4); break; + case 0x46: LAUNCH_TINYGEMM_KERNEL_AVX(4, 6); break; + default: fprintf(stderr, "Unexpected block size!\n"); + } + } + }); + }); + return; + } + + // pointer to work space, used convert A from float to quantized type + void * wdata = nullptr; + + //TODO: performance improvement: merge quant A + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + const size_t desired_wsize = M * row_size_A; + if (ctx->work_size < desired_wsize) { + ctx->work_data.reset(new char[desired_wsize]); + ctx->work_size = desired_wsize; + } + wdata = ctx->work_data.get(); + + // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size + // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size + GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); + + const float * A_data = static_cast(src1->data); + for (int m = 0; m < M; ++m) { + from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); + } + }); + + if (M == 1) { + // MB = 1 and handle 8 tiles in each block + constexpr int kTilesN = 4; + constexpr int BLOCK_N = TILE_N * kTilesN; + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, NB, [&](int begin, int end) { + GGML_DISPATCH_QTYPES(TYPE, [&] { + const int KB = K / blck_size; + const int TILE_SIZE = get_tile_size(); + const int row_size_A = KB * sizeof(vec_dot_type); + for (int i = begin; i < end; ++i) { + int nb = i; + int nb_start = nb * BLOCK_N; + int nb_size = std::min(BLOCK_N, N - nb_start); // 32, 64, 96 + + switch (nb_size) { + //case 160: LAUNCH_TINYGEMM_KERNEL_VNNI(160); break; + case 128: LAUNCH_TINYGEMM_KERNEL_VNNI(128); break; + case 96: LAUNCH_TINYGEMM_KERNEL_VNNI(96); break; + case 64: LAUNCH_TINYGEMM_KERNEL_VNNI(64); break; + case 32: LAUNCH_TINYGEMM_KERNEL_VNNI(32); break; + default: fprintf(stderr, "Unexpected n block size!\n"); + } + } + }); + }); + return; + } + + // handle 4 tiles at a tile + constexpr int BLOCK_M = TILE_M * 2; + constexpr int BLOCK_N = TILE_N * 2; + const int MB = div_up(M, BLOCK_M); + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, MB * NB, [&](int begin, int end) { + // init tile config for each thread + ggml_tile_config_init(); + + GGML_DISPATCH_QTYPES(TYPE, [&] { + const int KB = K / blck_size; + const int TILE_SIZE = get_tile_size(); + const int row_size_A = KB * sizeof(vec_dot_type); + + for (int i = begin; i < end; ++i) { + int mb = i / NB; + int nb = i % NB; + + int mb_start = mb * BLOCK_M; + int mb_size = std::min(BLOCK_M, M - mb_start); + int nb_start = nb * BLOCK_N; + int nb_size = BLOCK_N; + + tinygemm_kernel_amx( + mb_size, nb_size, KB, + (const char *)wdata + mb_start * row_size_A, + (const char *)src0->data + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE), + (float *) dst->data + mb_start * N + nb_start, ldc); + } + }); + }); +} + +#else // if defined(__AMX_INT8__) + +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + + GGML_UNUSED(ctx); + GGML_UNUSED(dst); +} + +#endif // if defined(__AMX_INT8__) diff --git a/ggml/src/ggml-amx/mmq.h b/ggml/src/ggml-amx/mmq.h new file mode 100644 index 0000000000..cf09206206 --- /dev/null +++ b/ggml/src/ggml-amx/mmq.h @@ -0,0 +1,17 @@ +#pragma once +#include "common.h" +#include + +#ifdef __cplusplus +extern "C" { +#endif + +size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor); + +void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h index fd3deae009..fa8d5b7fb6 100644 --- a/ggml/src/ggml-backend-impl.h +++ b/ggml/src/ggml-backend-impl.h @@ -22,7 +22,7 @@ extern "C" { size_t (*get_max_size) (ggml_backend_buffer_type_t buft); // (optional) data size needed to allocate the tensor, including padding (defaults to ggml_nbytes) size_t (*get_alloc_size)(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); - // (optional) check if tensor data is in host memory (defaults to false) + // (optional) check if tensor data is in host memory and uses standard ggml tensor layout (defaults to false) bool (*is_host) (ggml_backend_buffer_type_t buft); }; @@ -37,7 +37,6 @@ extern "C" { // struct ggml_backend_buffer_i { - const char * (*get_name) (ggml_backend_buffer_t buffer); // (optional) free the buffer void (*free_buffer) (ggml_backend_buffer_t buffer); // base address of the buffer @@ -88,19 +87,16 @@ extern "C" { void (*free)(ggml_backend_t backend); - // Will be moved to the device interface - // buffer allocation - ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); - // (optional) asynchronous tensor data access void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); bool (*cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst); - // (optional) complete all pending operations + // (optional) complete all pending operations (required if the backend supports async operations) void (*synchronize)(ggml_backend_t backend); - // (optional) compute graph with a plan (not used currently) + // (optional) graph plans (not used currently) + // compute graph with a plan ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); // update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology @@ -111,13 +107,6 @@ extern "C" { // compute graph (always async if supported by the backend) enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); - // IMPORTANT: these functions have been moved to the device interface and will be removed from the backend interface - // new backends should implement the device interface instead - // These functions are being moved to the device interface - bool (*supports_op) (ggml_backend_t backend, const struct ggml_tensor * op); - bool (*supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft); - bool (*offload_op) (ggml_backend_t backend, const struct ggml_tensor * op); - // (optional) event synchronization // record an event on this stream void (*event_record)(ggml_backend_t backend, ggml_backend_event_t event); diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp new file mode 100644 index 0000000000..63e9d82017 --- /dev/null +++ b/ggml/src/ggml-backend-reg.cpp @@ -0,0 +1,195 @@ +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-cpu.h" +#include "ggml-impl.h" +#include +#include + +// Backend registry + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + +#ifdef GGML_USE_VULKAN +#include "ggml-vulkan.h" +#endif + +#ifdef GGML_USE_BLAS +#include "ggml-blas.h" +#endif + +#ifdef GGML_USE_RPC +#include "ggml-rpc.h" +#endif + +#ifdef GGML_USE_AMX +# include "ggml-amx.h" +#endif + +#ifdef GGML_USE_CANN +#include "ggml-cann.h" +#endif + +#ifdef GGML_USE_KOMPUTE +#include "ggml-kompute.h" +#endif + +struct ggml_backend_registry { + std::vector backends; + std::vector devices; + + ggml_backend_registry() { +#ifdef GGML_USE_CUDA + register_backend(ggml_backend_cuda_reg()); +#endif +#ifdef GGML_USE_METAL + register_backend(ggml_backend_metal_reg()); +#endif +#ifdef GGML_USE_SYCL + register_backend(ggml_backend_sycl_reg()); +#endif +#ifdef GGML_USE_VULKAN + register_backend(ggml_backend_vk_reg()); +#endif +#ifdef GGML_USE_CANN + register_backend(ggml_backend_cann_reg()); +#endif +#ifdef GGML_USE_BLAS + register_backend(ggml_backend_blas_reg()); +#endif +#ifdef GGML_USE_RPC + register_backend(ggml_backend_rpc_reg()); +#endif +#ifdef GGML_USE_AMX + register_backend(ggml_backend_amx_reg()); +#endif +#ifdef GGML_USE_KOMPUTE + register_backend(ggml_backend_kompute_reg()); +#endif + + register_backend(ggml_backend_cpu_reg()); + } + + void register_backend(ggml_backend_reg_t reg) { + if (!reg) { + return; + } + +#ifndef NDEBUG + GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n", + __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg)); +#endif + backends.push_back(reg); + for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) { + register_device(ggml_backend_reg_dev_get(reg, i)); + } + } + + void register_device(ggml_backend_dev_t device) { +#ifndef NDEBUG + GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device)); +#endif + devices.push_back(device); + } +}; + +static ggml_backend_registry & get_reg() { + static ggml_backend_registry reg; + return reg; +} + +// Internal API +void ggml_backend_register(ggml_backend_reg_t reg) { + get_reg().register_backend(reg); +} + +void ggml_backend_device_register(ggml_backend_dev_t device) { + get_reg().register_device(device); +} + +// Backend (reg) enumeration +size_t ggml_backend_reg_count() { + return get_reg().backends.size(); +} + +ggml_backend_reg_t ggml_backend_reg_get(size_t index) { + GGML_ASSERT(index < ggml_backend_reg_count()); + return get_reg().backends[index]; +} + +ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) { + for (size_t i = 0; i < ggml_backend_reg_count(); i++) { + ggml_backend_reg_t reg = ggml_backend_reg_get(i); + if (std::strcmp(ggml_backend_reg_name(reg), name) == 0) { + return reg; + } + } + return NULL; +} + +// Device enumeration +size_t ggml_backend_dev_count() { + return get_reg().devices.size(); +} + +ggml_backend_dev_t ggml_backend_dev_get(size_t index) { + GGML_ASSERT(index < ggml_backend_dev_count()); + return get_reg().devices[index]; +} + +ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) { + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (strcmp(ggml_backend_dev_name(dev), name) == 0) { + return dev; + } + } + return NULL; +} + +ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) { + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == type) { + return dev; + } + } + return NULL; +} + +// Convenience functions +ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) { + ggml_backend_dev_t dev = ggml_backend_dev_by_name(name); + if (!dev) { + return NULL; + } + return ggml_backend_dev_init(dev, params); +} + +ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) { + ggml_backend_dev_t dev = ggml_backend_dev_by_type(type); + if (!dev) { + return NULL; + } + return ggml_backend_dev_init(dev, params); +} + +ggml_backend_t ggml_backend_init_best(void) { + ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU); + if (!dev) { + dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + } + if (!dev) { + return NULL; + } + return ggml_backend_dev_init(dev, NULL); +} diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 15d650150a..45da0c27da 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -8,6 +8,7 @@ #include #endif +#include "ggml-backend.h" #include "ggml-backend-impl.h" #include "ggml-alloc.h" #include "ggml-impl.h" @@ -34,6 +35,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { } ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + if (size == 0) { + // return a dummy buffer for zero-sized allocations + return ggml_backend_buffer_init(buft, {}, NULL, 0); + } + return buft->iface.alloc_buffer(buft, size); } @@ -89,7 +95,7 @@ ggml_backend_buffer_t ggml_backend_buffer_init( } const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name(buffer); + return ggml_backend_buft_name(ggml_backend_buffer_get_type(buffer)); } void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { @@ -108,6 +114,11 @@ size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) { } void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { + // get_base is optional if the buffer is zero-sized + if (buffer->size == 0) { + return NULL; + } + void * base = buffer->iface.get_base(buffer); GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); @@ -122,6 +133,15 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t } } +void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + // clear is optional if the buffer is zero-sized + if (buffer->size == 0) { + return; + } + + buffer->iface.clear(buffer, value); +} + size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) { return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); } @@ -134,10 +154,6 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } -void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - buffer->iface.clear(buffer, value); -} - bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer)); } @@ -198,7 +214,7 @@ void ggml_backend_free(ggml_backend_t backend) { } ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) { - return backend->iface.get_default_buffer_type(backend); + return ggml_backend_dev_buffer_type(backend->device); } ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) { @@ -236,45 +252,46 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor); ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + if (size == 0) { + return; + } + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - if (!size) { - return; - } - buf->iface.set_tensor(buf, tensor, data, offset, size); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor); ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + if (size == 0) { + return; + } + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - if (!size) { - return; - } - buf->iface.get_tensor(buf, tensor, data, offset, size); } -GGML_API void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { +void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + if (size == 0) { + return; + } + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - - if (!size) { - return; - } - - GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer"); + GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not implemented by backend buffer"); buf->iface.memset_tensor(buf, tensor, value, offset, size); } @@ -316,33 +333,15 @@ enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct } bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_supports_op(backend->device, op); - } - - return backend->iface.supports_op(backend, op); + return ggml_backend_dev_supports_op(backend->device, op); } bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_supports_buft(backend->device, buft); - } - - return backend->iface.supports_buft(backend, buft); + return ggml_backend_dev_supports_buft(backend->device, buft); } bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_offload_op(backend->device, op); - } - - if (backend->iface.offload_op != NULL) { - return backend->iface.offload_op(backend, op); - } - return false; + return ggml_backend_dev_offload_op(backend->device, op); } ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) { @@ -528,752 +527,6 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na return reg->iface.get_proc_address(reg, name); } -// Backend registry - -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_BLAS -#include "ggml-blas.h" -#endif - -#ifdef GGML_USE_RPC -#include "ggml-rpc.h" -#endif - -struct ggml_backend_registry { - std::vector backends; - std::vector devices; - - ggml_backend_registry() { -#ifdef GGML_USE_CUDA - register_backend(ggml_backend_cuda_reg()); -#endif -#ifdef GGML_USE_METAL - register_backend(ggml_backend_metal_reg()); -#endif -#ifdef GGML_USE_BLAS - register_backend(ggml_backend_blas_reg()); -#endif -#ifdef GGML_USE_RPC - register_backend(ggml_backend_rpc_reg()); -#endif - - // TODO: sycl, vulkan, kompute, cann - - register_backend(ggml_backend_cpu_reg()); - } - - void register_backend(ggml_backend_reg_t reg) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n", - __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg)); -#endif - backends.push_back(reg); - for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) { - register_device(ggml_backend_reg_dev_get(reg, i)); - } - } - - void register_device(ggml_backend_dev_t device) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device)); -#endif - devices.push_back(device); - } -}; - -static ggml_backend_registry & get_reg() { - static ggml_backend_registry reg; - return reg; -} - -// Internal API -void ggml_backend_register(ggml_backend_reg_t reg) { - get_reg().register_backend(reg); -} - -void ggml_backend_device_register(ggml_backend_dev_t device) { - get_reg().register_device(device); -} - -// Backend (reg) enumeration -size_t ggml_backend_reg_count() { - return get_reg().backends.size(); -} - -ggml_backend_reg_t ggml_backend_reg_get(size_t index) { - GGML_ASSERT(index < ggml_backend_reg_count()); - return get_reg().backends[index]; -} - -ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_reg_count(); i++) { - ggml_backend_reg_t reg = ggml_backend_reg_get(i); - if (strcmp(ggml_backend_reg_name(reg), name) == 0) { - return reg; - } - } - return NULL; -} - -// Device enumeration -size_t ggml_backend_dev_count() { - return get_reg().devices.size(); -} - -ggml_backend_dev_t ggml_backend_dev_get(size_t index) { - GGML_ASSERT(index < ggml_backend_dev_count()); - return get_reg().devices[index]; -} - -ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (strcmp(ggml_backend_dev_name(dev), name) == 0) { - return dev; - } - } - return NULL; -} - -ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) == type) { - return dev; - } - } - return NULL; -} - -// Convenience functions -ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_name(name); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(type); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_best(void) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL); - if (!dev) { - dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU_FULL); - } - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, NULL); -} - -// backend CPU - -static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment - -static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) { - return "CPU"; - - GGML_UNUSED(buffer); -} - -static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { - uintptr_t data = (uintptr_t)buffer->context; - - // align the buffer - if (data % TENSOR_ALIGNMENT != 0) { - data = GGML_PAD(data, TENSOR_ALIGNMENT); - } - - return (void *)data; -} - -static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); -} - -static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - memset((char *)tensor->data + offset, value, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - memcpy((char *)tensor->data + offset, data, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - memcpy(data, (const char *)tensor->data + offset, size); - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - memcpy(dst->data, src->data, ggml_nbytes(src)); - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - memset(buffer->context, value, buffer->size); -} - -static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { - /* .get_name = */ ggml_backend_cpu_buffer_get_name, - /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cpu_buffer_clear, - /* .reset = */ NULL, -}; - -static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { - /* .get_name = */ ggml_backend_cpu_buffer_get_name, - /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cpu_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned - void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h) - if (data == NULL) { - GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); - return NULL; - } - - return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); -} - -static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - - GGML_UNUSED(buft); -} - -static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return true; - - GGML_UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type; -} - -#ifdef GGML_USE_CPU_HBM - -// buffer type HBM - -#include - -static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU_HBM"; - - GGML_UNUSED(buft); -} - -static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { - return "CPU_HBM"; - - GGML_UNUSED(buf); -} - -static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { - hbw_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - //void * ptr = hbw_malloc(size); - void * ptr; - int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); - if (result != 0) { - GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size); - return NULL; - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name; - buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type_hbm; -} -#endif - -struct ggml_backend_cpu_context { - int n_threads; - ggml_threadpool_t threadpool; - - uint8_t * work_data; - size_t work_size; - - ggml_abort_callback abort_callback; - void * abort_callback_data; -}; - -static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { - return "CPU"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_cpu_free(ggml_backend_t backend) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - delete[] cpu_ctx->work_data; - delete cpu_ctx; - delete backend; -} - -static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(backend); -} - -struct ggml_backend_plan_cpu { - struct ggml_cplan cplan; - struct ggml_cgraph cgraph; -}; - -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_backend_plan_cpu * cpu_plan = new ggml_backend_plan_cpu; - - cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - cpu_plan->cgraph = *cgraph; // FIXME: deep copy - - if (cpu_plan->cplan.work_size > 0) { - cpu_plan->cplan.work_data = new uint8_t[cpu_plan->cplan.work_size]; - if (cpu_plan->cplan.work_data == NULL) { - delete cpu_plan; - return NULL; - } - } - - cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; - cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return cpu_plan; -} - -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - delete[] cpu_plan->cplan.work_data; - delete cpu_plan; - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - - if (cpu_ctx->work_size < cplan.work_size) { - delete[] cpu_ctx->work_data; - cpu_ctx->work_data = new uint8_t[cplan.work_size]; - if (cpu_ctx->work_data == NULL) { - cpu_ctx->work_size = 0; - return GGML_STATUS_ALLOC_FAILED; - } - cpu_ctx->work_size = cplan.work_size; - } - cplan.work_data = (uint8_t *)cpu_ctx->work_data; - - cplan.abort_callback = cpu_ctx->abort_callback; - cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return ggml_graph_compute(cgraph, &cplan); -} - -static const struct ggml_backend_i ggml_backend_cpu_i = { - /* .get_name = */ ggml_backend_cpu_get_name, - /* .free = */ ggml_backend_cpu_free, - /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, - /* .graph_compute = */ ggml_backend_cpu_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_cpu_guid(void) { - static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; - return &guid; -} - -ggml_backend_t ggml_backend_cpu_init(void) { - struct ggml_backend_cpu_context * ctx = new ggml_backend_cpu_context; - if (ctx == NULL) { - return NULL; - } - - ctx->n_threads = GGML_DEFAULT_N_THREADS; - ctx->threadpool = NULL; - ctx->work_data = NULL; - ctx->work_size = 0; - ctx->abort_callback = NULL; - ctx->abort_callback_data = NULL; - - ggml_backend_t cpu_backend = new ggml_backend { - /* .guid = */ ggml_backend_cpu_guid(), - /* .interface = */ ggml_backend_cpu_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ ctx, - }; - - if (cpu_backend == NULL) { - delete ctx; - return NULL; - } - - return cpu_backend; -} - -bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); -} - -void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->n_threads = n_threads; -} - -void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - - if (ctx->threadpool && ctx->threadpool != threadpool) { - // already had a different threadpool, pause/suspend it before switching - ggml_threadpool_pause(ctx->threadpool); - } - ctx->threadpool = threadpool; -} - -void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->abort_callback = abort_callback; - ctx->abort_callback_data = abort_callback_data; -} - -ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { - GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); - return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); -} - -//////////////////////// - -struct ggml_backend_cpu_device_context { - std::string description = "CPU"; - - ggml_backend_cpu_device_context() { -#ifdef __APPLE__ - size_t len = 0; - if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) { - description.resize(len); - sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT - } -#elif defined(__linux__) - FILE * f = fopen("/proc/cpuinfo", "r"); - if (f) { - char buf[1024]; - while (fgets(buf, sizeof(buf), f)) { - if (strncmp(buf, "model name", 10) == 0) { - char * p = strchr(buf, ':'); - if (p) { - p++; - while (std::isspace(*p)) { - p++; - } - while (std::isspace(p[strlen(p) - 1])) { - p[strlen(p) - 1] = '\0'; - } - description = p; - break; - } - } - } - fclose(f); - } -#elif defined(_WIN32) - HKEY hKey; - if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, - TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), - 0, - KEY_READ, - &hKey) == ERROR_SUCCESS) { - DWORD cpu_brand_size = 0; - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - NULL, - &cpu_brand_size) == ERROR_SUCCESS) { - description.resize(cpu_brand_size); - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - (LPBYTE)&description[0], // NOLINT - &cpu_brand_size) == ERROR_SUCCESS) { - if (description.find('\0') != std::string::npos) { - description.resize(description.find('\0')); - } - } - } - RegCloseKey(hKey); - } -#endif - } -}; - -static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) { - return "CPU"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) { - struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context; - - return ctx->description.c_str(); -} - -static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU_FULL; - - GGML_UNUSED(dev); -} - -static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_cpu_device_get_name(dev); - props->description = ggml_backend_cpu_device_get_description(dev); - props->type = ggml_backend_cpu_device_get_type(dev); - ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_cpu_device_init(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_cpu_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(dev); -} - -static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - GGML_UNUSED(dev); - GGML_UNUSED(max_tensor_size); -} - -static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - switch (op->op) { - case GGML_OP_CPY: - return - op->type != GGML_TYPE_IQ2_XXS && - op->type != GGML_TYPE_IQ2_XS && - op->type != GGML_TYPE_IQ1_S && - op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float - case GGML_OP_MUL_MAT: - return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_get_type_traits(op->src[0]->type)->vec_dot_type; - case GGML_OP_ROPE_BACK: - return op->src[2] == NULL && (op->op_params[2] & 4) == 0; - case GGML_OP_IM2COL_BACK: - return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32; - case GGML_OP_OUT_PROD: - return (op->src[0]->type == GGML_TYPE_F32 || ggml_is_quantized(op->src[0]->type)) && op->src[1]->type == GGML_TYPE_F32; - default: - return true; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return ggml_backend_buft_is_host(buft); - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { - /* .get_name = */ ggml_backend_cpu_device_get_name, - /* .get_description = */ ggml_backend_cpu_device_get_description, - /* .get_memory = */ ggml_backend_cpu_device_get_memory, - /* .get_type = */ ggml_backend_cpu_device_get_type, - /* .get_props = */ ggml_backend_cpu_device_get_props, - /* .init_backend = */ ggml_backend_cpu_device_init, - /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_ptr, - /* .supports_op = */ ggml_backend_cpu_device_supports_op, - /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -//////////////////////// - -static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { - return "CPU"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_cpu_device_context ctx; - static ggml_backend_device ggml_backend_cpu_device = { - /* .iface = */ ggml_backend_cpu_device_i, - /* .reg = */ reg, - /* .context = */ &ctx, - }; - - return &ggml_backend_cpu_device; -} - -static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_cpu_set_n_threads; - } - return NULL; - - GGML_UNUSED(reg); -} - -static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = { - /* .get_name = */ ggml_backend_cpu_reg_get_name, - /* .get_device_count = */ ggml_backend_cpu_reg_get_device_count, - /* .get_device = */ ggml_backend_cpu_reg_get_device, - /* .get_proc_address = */ ggml_backend_cpu_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_cpu_reg(void) { - static struct ggml_backend_reg ggml_backend_cpu_reg = { - /* .iface = */ ggml_backend_cpu_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_reg; -} - // multi-buffer buffer struct ggml_backend_multi_buffer_context { @@ -1281,12 +534,6 @@ struct ggml_backend_multi_buffer_context { size_t n_buffers; }; -static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context; - - return ctx->buffers[0]->iface.get_name(ctx->buffers[0]); -} - static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context; for (size_t i = 0; i < ctx->n_buffers; i++) { @@ -1305,7 +552,6 @@ static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = { - /* .get_name = */ ggml_backend_multi_buffer_get_name, /* .free_buffer = */ ggml_backend_multi_buffer_free_buffer, /* .get_base = */ NULL, /* .init_tensor = */ NULL, @@ -1334,7 +580,7 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer } bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_multi_buffer_get_name; + return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer; } void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { @@ -1426,7 +672,7 @@ struct ggml_backend_sched { char * context_buffer; size_t context_buffer_size; - bool debug; + int debug; }; #define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor) @@ -1445,7 +691,7 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen } static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) { - ggml_backend_buffer_t buffer = tensor->buffer; + ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; if (buffer == NULL) { return -1; } @@ -1478,8 +724,6 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML // returns the backend that should be used for the node based on the current locations static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) { - // TODO: use supports_op to check if the backend supports the op - // assign pre-allocated nodes to their backend int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor); if (cur_backend_id != -1) { @@ -1498,7 +742,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) { // since the tensor is pre-allocated, it cannot be moved to another backend - GGML_ABORT("pre-allocated tensor in a backend that cannot run the operation"); + GGML_ABORT("pre-allocated tensor (%s) in a backend that cannot run the operation", tensor->name); } // graph input @@ -1514,7 +758,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (src == NULL) { continue; } - if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + // skip ROPE since the rope freqs tensor is too small to choose a backend based on it + // not an ideal solution + if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); // check if a backend with higher prio wants to offload the op if (src_backend_id == sched->n_backends - 1) { @@ -1561,19 +807,21 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str if (ggml_is_view_op(node->op)) { continue; } - ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); - GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, - fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - continue; + if (sched->debug > 1) { + ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); + GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, + fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); + GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name, + fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } - ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); - GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name, - fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); + GGML_LOG_DEBUG("\n"); } - GGML_LOG_DEBUG("\n"); } } @@ -1865,11 +1113,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (src == NULL) { continue; } - // check if a weight is on a different backend + // check if a weight is on a different and incompatible backend // by starting a new split, the memory of the previously offloaded weights can be reused if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend_id = tensor_backend_id(src); - if (src_backend_id != cur_backend_id) { + if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) { need_new_split = true; break; } @@ -1881,7 +1129,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg int src_backend_id = sched->hv_tensor_backend_ids[id]; bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id); if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) { - //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name); need_new_split = true; break; } @@ -2202,11 +1449,12 @@ ggml_backend_sched_t ggml_backend_sched_new( bool parallel) { GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); - GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU + GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU); struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched)); - sched->debug = getenv("GGML_SCHED_DEBUG") != NULL; + const char * GGML_SCHED_DEBUG = getenv("GGML_SCHED_DEBUG"); + sched->debug = GGML_SCHED_DEBUG ? atoi(GGML_SCHED_DEBUG) : 0; sched->n_backends = n_backends; sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; @@ -2234,6 +1482,7 @@ ggml_backend_sched_t ggml_backend_sched_new( sched->backends[b] = backends[b]; sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b])); + if (sched->n_copies > 1) { for (int c = 0; c < sched->n_copies; c++) { sched->events[b][c] = ggml_backend_event_new(backends[b]->device); @@ -2289,12 +1538,13 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * ggml_backend_sched_split_graph(sched, measure_graph); + ggml_backend_sched_synchronize(sched); + if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) { return false; } ggml_backend_sched_reset(sched); - ggml_backend_sched_synchronize(sched); return true; } @@ -2595,3 +1845,154 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t return true; } + +// CPU backend - buffer + +static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { + uintptr_t data = (uintptr_t)buffer->context; + + // align the buffer + if (data % TENSOR_ALIGNMENT != 0) { + data = GGML_PAD(data, TENSOR_ALIGNMENT); + } + + return (void *)data; +} + +static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_aligned_free(buffer->context, buffer->size); +} + +static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + memcpy((char *)tensor->data + offset, data, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { + /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, +}; + +static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { + /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, +}; + +// CPU backend buffer type + +// this buffer type is defined here to make it available to all backends + +static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = ggml_aligned_malloc(size); + + if (data == NULL) { + GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); +} + +static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return true; + + GGML_UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type; +} + +static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_Mapped"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type; +} + +ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { + GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); + return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); +} diff --git a/ggml/src/ggml-blas/CMakeLists.txt b/ggml/src/ggml-blas/CMakeLists.txt new file mode 100644 index 0000000000..e2cbabf0da --- /dev/null +++ b/ggml/src/ggml-blas/CMakeLists.txt @@ -0,0 +1,90 @@ +if (GGML_STATIC) + set(BLA_STATIC ON) +endif() +#if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) +# set(BLA_SIZEOF_INTEGER 8) +#endif() + +set(BLA_VENDOR ${GGML_BLAS_VENDOR}) +find_package(BLAS) + +if (BLAS_FOUND) + message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") + + add_library(ggml-blas + ggml-blas.cpp + ) + + target_link_libraries(ggml-blas PRIVATE ggml-base) + target_include_directories(ggml-blas PRIVATE . ..) + + if (${GGML_BLAS_VENDOR} MATCHES "Apple") + add_compile_definitions(ACCELERATE_NEW_LAPACK) + add_compile_definitions(ACCELERATE_LAPACK_ILP64) + add_compile_definitions(GGML_BLAS_USE_ACCELERATE) + elseif ("${BLAS_INCLUDE_DIRS}" STREQUAL "") + # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake. + # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268 + find_package(PkgConfig REQUIRED) + if (${GGML_BLAS_VENDOR} MATCHES "Generic") + pkg_check_modules(DepBLAS blas) + elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS") + # As of openblas v0.3.22, the 64-bit is named openblas64.pc + pkg_check_modules(DepBLAS openblas64) + if (NOT DepBLAS_FOUND) + pkg_check_modules(DepBLAS openblas) + endif() + elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME") + add_compile_definitions(GGML_BLAS_USE_BLIS) + pkg_check_modules(DepBLAS blis) + elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS") + pkg_check_modules(DepBLAS blas-atlas) + elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS") + pkg_check_modules(DepBLAS flexiblas_api) + elseif (${GGML_BLAS_VENDOR} MATCHES "Intel") + add_compile_definitions(GGML_BLAS_USE_MKL) + # all Intel* libraries share the same include path + pkg_check_modules(DepBLAS mkl-sdl) + elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC") + # this doesn't provide pkg-config + # suggest to assign BLAS_INCLUDE_DIRS on your own + if ("${NVHPC_VERSION}" STREQUAL "") + message(WARNING "Better to set NVHPC_VERSION") + else() + set(DepBLAS_FOUND ON) + set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include") + endif() + endif() + if (DepBLAS_FOUND) + set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS}) + else() + message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically" + " detected by pkgconfig, trying to find cblas.h from possible paths...") + find_path(BLAS_INCLUDE_DIRS + NAMES cblas.h + HINTS + /usr/include + /usr/local/include + /usr/include/openblas + /opt/homebrew/opt/openblas/include + /usr/local/opt/openblas/include + /usr/include/x86_64-linux-gnu/openblas/include + ) + endif() + endif() + + message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") + + target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS}) + + if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel")) + add_compile_definitions(GGML_BLAS_USE_MKL) + endif() + + target_link_libraries (ggml-blas PRIVATE ${BLAS_LIBRARIES}) + target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS}) +else() + message(ERROR "BLAS not found, please refer to " + "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" + " to set correct GGML_BLAS_VENDOR") +endif() diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp similarity index 95% rename from ggml/src/ggml-blas.cpp rename to ggml/src/ggml-blas/ggml-blas.cpp index 7875ec86d0..648c9d875e 100644 --- a/ggml/src/ggml-blas.cpp +++ b/ggml/src/ggml-blas/ggml-blas.cpp @@ -6,7 +6,7 @@ #include #include -#if defined(GGML_USE_ACCELERATE) +#if defined(GGML_BLAS_USE_ACCELERATE) # include #elif defined(GGML_BLAS_USE_MKL) # include @@ -224,12 +224,6 @@ static void ggml_backend_blas_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(backend); -} - static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; @@ -265,7 +259,6 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, static struct ggml_backend_i blas_backend_i = { /* .get_name = */ ggml_backend_blas_get_name, /* .free = */ ggml_backend_blas_free, - /* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -275,9 +268,6 @@ static struct ggml_backend_i blas_backend_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_blas_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -330,7 +320,7 @@ static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) { } static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) { - #if defined(GGML_USE_ACCELERATE) + #if defined(GGML_BLAS_USE_ACCELERATE) return "Accelerate"; #elif defined(GGML_BLAS_USE_MKL) return "MKL"; @@ -356,7 +346,7 @@ static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * } static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU; + return GGML_BACKEND_DEVICE_TYPE_ACCEL; GGML_UNUSED(dev); } @@ -374,7 +364,7 @@ static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct gg }; } -static ggml_backend_t ggml_backend_blas_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) { return ggml_backend_blas_init(); GGML_UNUSED(dev); @@ -387,7 +377,7 @@ static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_ GGML_UNUSED(dev); } -static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { +static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { return ggml_backend_cpu_buffer_from_ptr(ptr, size); GGML_UNUSED(dev); @@ -456,10 +446,10 @@ static const struct ggml_backend_device_i ggml_backend_blas_device_i = { /* .get_memory = */ ggml_backend_blas_device_get_memory, /* .get_type = */ ggml_backend_blas_device_get_type, /* .get_props = */ ggml_backend_blas_device_get_props, - /* .init_backend = */ ggml_backend_blas_device_init, + /* .init_backend = */ ggml_backend_blas_device_init_backend, /* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_ptr, + /* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_host_ptr, /* .supports_op = */ ggml_backend_blas_device_supports_op, /* .supports_buft = */ ggml_backend_blas_device_supports_buft, /* .offload_op = */ NULL, diff --git a/ggml/src/ggml-cann/CMakeLists.txt b/ggml/src/ggml-cann/CMakeLists.txt new file mode 100644 index 0000000000..756200b893 --- /dev/null +++ b/ggml/src/ggml-cann/CMakeLists.txt @@ -0,0 +1,75 @@ +if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME}) + set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME}) + message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}") +endif() + +# Auto-detech Soc type and Soc version, if detect failed, will abort build +set(SOC_VERSION "") +function(detect_ascend_soc_type SOC_VERSION) + execute_process( + COMMAND bash -c "npu-smi info|awk -F' ' 'NF > 0 && NR==7 {print $3}'" + OUTPUT_VARIABLE npu_info + RESULT_VARIABLE npu_result + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + if("${npu_info}" STREQUAL "" OR ${npu_result}) + message(FATAL_ERROR "Auto-detech ascend soc type failed, please specify manually or check ascend device working normally.") + endif() + set(${SOC_VERSION} "Ascend${npu_info}" PARENT_SCOPE) +endfunction() + +if(NOT SOC_TYPE) + detect_ascend_soc_type(SOC_VERSION) + set(SOC_TYPE "${SOC_VERSION}") + message(STATUS "CANN: SOC_VERSION auto-detected is:${SOC_VERSION}") +else() + string(TOLOWER ${SOC_TYPE} SOC_VERSION) +endif() + +# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND310P. +string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}") +set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}") + +if (CANN_INSTALL_DIR) + # Only Support Linux. + if (NOT UNIX) + message(FATAL_ERROR "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}") + endif() + + # Supported platforms: x86-64, arm64 + if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") + elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64") + else() + message(FATAL_ERROR "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}") + endif() + + # Set header and libs + set(CANN_INCLUDE_DIRS + ${CANN_INSTALL_DIR}/include + ${CANN_INSTALL_DIR}/include/aclnn + ${CANN_INSTALL_DIR}/acllib/include + ) + + add_subdirectory(kernels) + list(APPEND CANN_LIBRARIES + ascendcl + nnopbase + opapi + acl_op_compiler + ascendc_kernels + ) + + file(GLOB GGML_SOURCES_CANN "*.cpp") + + add_library(ggml-cann ${GGML_SOURCES_CANN}) + target_link_libraries(ggml-cann PRIVATE ggml-base ${CANN_LIBRARIES}) + target_include_directories(ggml-cann PRIVATE . .. ${CANN_INCLUDE_DIRS}) + target_link_directories(ggml-cann PRIVATE ${CANN_INSTALL_DIR}/lib64) + + target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}") + + message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}") + message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}") +else() + message(FATAL_ERROR "CANN: Can't find CANN_INSTALL_DIR, did you forget to source set_var.sh?") +endif() diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index a4ec8418e2..1f4ee986ce 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2312,6 +2312,14 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { switch (src0->type) { case GGML_TYPE_F32: + { +#ifdef ASCEND_310P + // Special operation for get_row_f32 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes + if ((src0->ne[0] % 8) != 0) { + size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32); + ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); + } +#endif aclrtlaunch_ascendc_get_row_f32( 24, ctx.stream(), src0->data, src1->data, dst->data, ((ggml_tensor*)src0->extra)->ne, @@ -2320,7 +2328,16 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne, ((ggml_tensor*)dst->extra)->nb); break; + } case GGML_TYPE_F16: + { +#ifdef ASCEND_310P + // Special operation for get_row_f16 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes + if ((src0->ne[0] % 16) != 0) { + size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32); // out is also f32, even input is f16 + ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); + } +#endif aclrtlaunch_ascendc_get_row_f16( 24, ctx.stream(), src0->data, src1->data, dst->data, ((ggml_tensor*)src0->extra)->ne, @@ -2329,6 +2346,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne, ((ggml_tensor*)dst->extra)->nb); break; + } case GGML_TYPE_Q4_0: aclrtlaunch_ascendc_get_row_q4_0( 24, ctx.stream(), src0->data, src1->data, dst->data, diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp similarity index 89% rename from ggml/src/ggml-cann.cpp rename to ggml/src/ggml-cann/ggml-cann.cpp index db5f8f1865..7763408814 100644 --- a/ggml/src/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -39,6 +39,8 @@ #include "ggml-common.h" +#define GGML_CANN_NAME "CANN" + /** * @brief Handles CANN errors by printing an error message and aborting. * @@ -487,23 +489,6 @@ struct ggml_backend_cann_buffer_context { ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); } }; -/** - * @brief Retrieve the name associated with a CANN buffer. - * - * This function returns the name of a CANN buffer, which is stored in the - * context of the buffer. - * - * @param buffer The CANN buffer whose name is to be retrieved. - * @return A pointer to a C-string containing the name of the buffer. - */ - -static const char* ggml_backend_cann_buffer_get_name( - ggml_backend_buffer_t buffer) { - return "CANN"; - - GGML_UNUSED(buffer); -} - /** * @brief Check if a buffer is a CANN buffer. * @@ -513,9 +498,10 @@ static const char* ggml_backend_cann_buffer_get_name( * @param buffer The buffer to check. * @return true if the buffer is a CANN buffer, false otherwise. */ +static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft); static bool ggml_backend_buffer_is_cann( ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cann_buffer_get_name; + return ggml_backend_buft_is_cann(buffer->buft); } /** @@ -851,13 +837,6 @@ static void ggml_backend_cann_buffer_set_tensor( void *transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); -#ifndef NDEBUG - void *check_buffer = malloc(size); - ggml_backend_cann_transform_back(tensor, transform_buffer, - check_buffer); - GGML_ASSERT(memcmp(data, check_buffer, size) == 0); - free(check_buffer); -#endif ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE)); @@ -969,8 +948,7 @@ static void ggml_backend_cann_buffer_clear( * This structure defines function pointers to operations that can be performed * on a CANN buffer within the backend. */ -static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { - /* .get_name = */ ggml_backend_cann_buffer_get_name, +static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { /* .free_buffer = */ ggml_backend_cann_buffer_free_buffer, /* .get_base = */ ggml_backend_cann_buffer_get_base, /* .init_tensor = */ ggml_backend_cann_buffer_init_tensor, @@ -1004,9 +982,10 @@ struct ggml_backend_cann_buffer_type_context { */ static const char* ggml_backend_cann_buffer_type_name( ggml_backend_buffer_type_t buft) { - return "CANN"; + ggml_backend_cann_buffer_type_context* buft_ctx = + (ggml_backend_cann_buffer_type_context*)buft->context; - GGML_UNUSED(buft); + return buft_ctx->name.c_str(); } /** @@ -1105,19 +1084,25 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size( GGML_UNUSED(buft); } +static bool ggml_backend_cann_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + /** * @brief Interface for managing CANN buffer types in the GGML backend. * * Provides function pointers for allocating, querying properties, and managing * memory for CANN buffer types in the GGML backend. */ -static ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = { +static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = { /* .get_name = */ ggml_backend_cann_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cann_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cann_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cann_buffer_type_get_alloc_size, - /* .is_host = */ NULL, + /* .is_host = */ ggml_backend_cann_buffer_type_is_host, }; /** @@ -1148,6 +1133,7 @@ ggml_backend_cann_buffer_type(int32_t device) { for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) { ggml_backend_cann_buffer_types[i] = { /* .iface = */ ggml_backend_cann_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), /* .context = */ new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i)}, @@ -1241,7 +1227,6 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cann_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; return buffer; @@ -1263,7 +1248,7 @@ ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), /* .context = */ nullptr, }; @@ -1463,24 +1448,6 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { delete backend; } -/** - * @brief Retrieves the default buffer type associated with the CANN backend. - * - * This function returns the buffer type specific to the device associated - * with the CANN backend. It is used to allocate buffers for computations - * performed by the backend. - * - * @param backend Pointer to the CANN backend structure. - * @return Pointer to the buffer type structure for the CANN backend. - */ -static ggml_backend_buffer_type_t -ggml_backend_cann_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - return ggml_backend_cann_buffer_type(cann_ctx->device); -} - /** * @brief Sets tensor data asynchronously in the CANN backend. * @@ -1510,13 +1477,6 @@ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, void *transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); -#ifndef NDEBUG - void *check_buffer = malloc(size); - ggml_backend_cann_transform_back(tensor, transform_buffer, - check_buffer); - GGML_ASSERT(memcmp(data, check_buffer, size)); - free(check_buffer); -#endif ACL_CHECK(aclrtMemcpyAsync( (char *)tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream())); @@ -1691,7 +1651,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( * @return bool Returns true if the operation is supported by the backend, * otherwise false. */ -static bool ggml_backend_cann_supports_op(ggml_backend_t backend, +static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_tensor* op) { switch (op->op) { case GGML_OP_UNARY: @@ -1782,7 +1742,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_t backend, return false; } - GGML_UNUSED(backend); + GGML_UNUSED(dev); } /** @@ -1800,31 +1760,6 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) { return buft->iface.get_name == ggml_backend_cann_buffer_type_name; } -/** - * @brief Checks if the CANN backend supports a specific backend buffer type. - * - * This function determines whether the CANN backend supports the given backend - * buffer type by comparing the device context of the backend and buffer type. - * It returns true if the devices are same between the backend context and - * buffer type context. - * - * @param backend Pointer to the CANN backend. - * @param buft Pointer to the backend buffer type to check. - * @return bool Returns true if the CANN backend supports the buffer type, - * otherwise false. - */ -static bool ggml_backend_cann_supports_buft( - ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (ggml_backend_buft_is_cann(buft)) { - ggml_backend_cann_context * cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_cann_buffer_type_context * buft_ctx = - (ggml_backend_cann_buffer_type_context *)buft->context; - return buft_ctx->device == cann_ctx->device; - } - return false; -} - /** * @brief Determines if a tensor operation should be offloaded to the CANN * backend. @@ -1839,54 +1774,14 @@ static bool ggml_backend_cann_supports_buft( * @return bool Returns true if the operation should be offloaded, otherwise * false. */ -static bool ggml_backend_cann_offload_op(ggml_backend_t backend, +static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor* op) { const int min_batch_size = 32; - GGML_UNUSED(backend); + GGML_UNUSED(dev); return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; } -/** - * @brief Creates a new event for the CANN backend. - * - * This function initializes a new event for the CANN backend by setting the - * device and creating an ACL runtime event. The created event is then wrapped - * in a ggml_backend_event structure and returned. - * - * @param backend Pointer to the CANN backend. - * @return ggml_backend_event_t Returns a pointer to the new event structure. - */ -static ggml_backend_event_t ggml_backend_cann_event_new( - ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - ggml_cann_set_device(cann_ctx->device); - - aclrtEvent event; - ACL_CHECK(aclrtCreateEvent(&event)); - - return new ggml_backend_event{ - /* .backend = */ backend, - /* .context = */ event, - }; -} - -/** - * @brief Frees a CANN backend event. - * - * This function destroys the ACL runtime event associated with the given CANN - * backend event and then deletes the event structure itself. - * - * @param event Pointer to the event structure to be freed. - */ -static void ggml_backend_cann_event_free(ggml_backend_event_t event) { - ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); - - delete event; -} - /** * @brief Records an event on the CANN backend stream. * @@ -1895,10 +1790,9 @@ static void ggml_backend_cann_event_free(ggml_backend_event_t event) { * * @param event Pointer to the event structure to be recorded. */ -static void ggml_backend_cann_event_record(ggml_backend_event_t event) { +static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) { ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)event->backend->context; - + (ggml_backend_cann_context*)backend->context; ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream())); } @@ -1916,8 +1810,7 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { ggml_backend_cann_context* cann_ctx = (ggml_backend_cann_context*)backend->context; - - if (ggml_backend_is_cann(event->backend)) { + if (ggml_backend_is_cann(backend)) { ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), (aclrtEvent)event->context)); } else { @@ -1925,17 +1818,6 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend, } } -/** - * @brief Synchronizes the given event on the CANN backend. - * - * This function waits for the specified event to complete on the ACL runtime. - * - * @param event Pointer to the event structure to be synchronized. - */ -static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) { - ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); -} - /** * @brief Structure defining the interface for the CANN backend. * @@ -1943,10 +1825,9 @@ static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) { * supported by the CANN backend, including name retrieval, memory * management, tensor operations, synchronization, and event handling. */ -static ggml_backend_i ggml_backend_cann_interface = { +static const ggml_backend_i ggml_backend_cann_interface = { /* .get_name = */ ggml_backend_cann_name, /* .free = */ ggml_backend_cann_free, - /* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cann_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cann_get_tensor_async, /* .cpy_tensor_async = */ ggml_backend_cann_cpy_tensor_async, @@ -1956,9 +1837,6 @@ static ggml_backend_i ggml_backend_cann_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cann_graph_compute, - /* .supports_op = */ ggml_backend_cann_supports_op, - /* .supports_buft = */ ggml_backend_cann_supports_buft, - /* .offload_op = */ ggml_backend_cann_offload_op, /* .event_record = */ ggml_backend_cann_event_record, /* .event_wait = */ ggml_backend_cann_event_wait, }; @@ -1977,6 +1855,234 @@ static ggml_guid_t ggml_backend_cann_guid() { return &guid; } +// backend device +struct ggml_backend_cann_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_get_device_memory(ctx->device, free, total); +} + +static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_cann_device_get_name(dev); + props->description = ggml_backend_cann_device_get_description(dev); + props->type = ggml_backend_cann_device_get_type(dev); + ggml_backend_cann_device_get_memory(dev, &props->memory_free, &props->memory_total); + + bool host_buffer = getenv("GGML_CANN_NO_PINNED") == nullptr; + + props->caps = { + /* .async = */ false, + /* .host_buffer = */ host_buffer, + /* .buffer_from_host_ptr = */ false, + /* .events = */ true, + }; +} + +static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ggml_backend_cann_init(ctx->device); +} + +/** + * @brief Checks if the CANN backend supports a specific backend buffer type. + * + * This function determines whether the CANN backend supports the given backend + * buffer type by comparing the device context of the backend and buffer type. + * It returns true if the devices are same between the backend context and + * buffer type context. + * + * @param backend Pointer to the CANN backend. + * @param buft Pointer to the backend buffer type to check. + * @return bool Returns true if the CANN backend supports the buffer type, + * otherwise false. + */ +static bool ggml_backend_cann_supports_buft( + ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (ggml_backend_buft_is_cann(buft)) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_buffer_type_context * buft_ctx = + (ggml_backend_cann_buffer_type_context *)buft->context; + return buft_ctx->device == dev_ctx->device; + } + return false; +} + +static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ggml_backend_cann_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return ggml_backend_cann_host_buffer_type(); +} + +/** + * @brief Creates a new event for the CANN backend device. + * + * This function initializes a new event for the CANN backend by setting the + * device and creating an ACL runtime event. The created event is then wrapped + * in a ggml_backend_event structure and returned. + * + * @param backend Pointer to the CANN backend. + * @return ggml_backend_event_t Returns a pointer to the new event structure. + */ +static ggml_backend_event_t ggml_backend_cann_device_event_new( + ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; + + ggml_cann_set_device(dev_ctx->device); + + aclrtEvent event; + ACL_CHECK(aclrtCreateEvent(&event)); + + return new ggml_backend_event{ + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), dev_ctx->device), + /* .context = */ event, + }; +} + +/** + * @brief Frees a CANN backend event. + * + * This function destroys the ACL runtime event associated with the given CANN + * backend event and then deletes the event structure itself. + * + * @param event Pointer to the event structure to be freed. + */ +static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); + + delete event; + GGML_UNUSED(dev); +} + +/** + * @brief Synchronizes the given event on the CANN backend. + * + * This function waits for the specified event to complete on the ACL runtime. + * + * @param event Pointer to the event structure to be synchronized. + */ +static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); + + GGML_UNUSED(dev); +} + +static const ggml_backend_device_i ggml_backend_cann_device_interface = { + /* .get_name = */ ggml_backend_cann_device_get_name, + /* .get_description = */ ggml_backend_cann_device_get_description, + /* .get_memory = */ ggml_backend_cann_device_get_memory, + /* .get_type = */ ggml_backend_cann_device_get_type, + /* .get_props = */ ggml_backend_cann_device_get_props, + /* .init_backend = */ ggml_backend_cann_device_init, // called for every card + /* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ NULL, // not supported for CANN + /* .supports_op = */ ggml_backend_cann_supports_op, + /* .supports_buft = */ ggml_backend_cann_supports_buft, + /* .offload_op = */ ggml_backend_cann_offload_op, + /* .event_new = */ ggml_backend_cann_device_event_new, + /* .event_free = */ ggml_backend_cann_device_event_free, + /* .event_synchronize = */ ggml_backend_cann_device_event_synchronize, +}; + + +// backend reg +struct ggml_backend_cann_reg_context { + std::vector devices; +}; + +static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return GGML_CANN_NAME; +} + +static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) { + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + return ctx->devices.size(); +} + +static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) { + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; +} + +static void * ggml_backend_cann_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) { + GGML_UNUSED(reg); + GGML_UNUSED(name); + // reserved for future use + return nullptr; +} + +static const ggml_backend_reg_i ggml_backend_cann_reg_interface = { + /* .get_name = */ ggml_backend_cann_reg_get_name, + /* .get_device_count = */ ggml_backend_cann_reg_get_device_count, + /* .get_device_get = */ ggml_backend_cann_reg_get_device, + /* .get_proc_address = */ ggml_backend_cann_reg_get_proc_address, +}; + +// backend registry, called only once for cann backend +ggml_backend_reg_t ggml_backend_cann_reg() { + static ggml_backend_reg reg; + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + aclInit(nullptr); + ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context; + + for (int i = 0; i < ggml_cann_info().device_count; i++) { + ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context(); + dev_ctx->description = aclrtGetSocName(); + dev_ctx->device = i; + dev_ctx->name = GGML_CANN_NAME + std::to_string(i); + ggml_cann_set_device(i); + ggml_backend_dev_t dev = new ggml_backend_device { + /* .interface = */ ggml_backend_cann_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx + }; + ctx->devices.push_back(dev); + } + + reg = ggml_backend_reg { + /* .interface = */ ggml_backend_cann_reg_interface, + /* .context = */ ctx + }; + } + + initialized = true; + } + + return ® +} + ggml_backend_t ggml_backend_cann_init(int32_t device) { aclInit(nullptr); if (device < 0 || device >= ggml_backend_cann_get_device_count()) { @@ -1993,7 +2099,7 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) { ggml_backend_t cann_backend = new ggml_backend{/* .guid = */ ggml_backend_cann_guid(), /* .interface = */ ggml_backend_cann_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), /* .context = */ ctx}; return cann_backend; diff --git a/ggml/src/ggml-cann/kernels/CMakeLists.txt b/ggml/src/ggml-cann/kernels/CMakeLists.txt index 5b4fef91b5..6a4e17cce5 100644 --- a/ggml/src/ggml-cann/kernels/CMakeLists.txt +++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt @@ -1,7 +1,3 @@ -if (NOT SOC_TYPE) - set (SOC_TYPE "Ascend910B3") -endif() - file(GLOB SRC_FILES get_row_f32.cpp get_row_f16.cpp @@ -13,7 +9,6 @@ file(GLOB SRC_FILES dup.cpp ) -string(TOLOWER ${SOC_TYPE} SOC_VERSION) set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR}) set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim") @@ -30,4 +25,6 @@ ascendc_library(ascendc_kernels STATIC ${SRC_FILES} ) +message(STATUS "CANN: compile ascend kernels witch SOC_VERSION:${SOC_VERSION}.") +ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}") # ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP) diff --git a/ggml/src/ggml-cann/kernels/dup.cpp b/ggml/src/ggml-cann/kernels/dup.cpp index e2c651152f..99f03e0588 100644 --- a/ggml/src/ggml-cann/kernels/dup.cpp +++ b/ggml/src/ggml-cann/kernels/dup.cpp @@ -5,6 +5,7 @@ using namespace AscendC; #define BUFFER_NUM 2 +const int64_t SUPPORTED_MAX_DIM = 65535; // currently the limit of max block dim supportted by dup kernel is 65535template template class DupByRows { @@ -19,6 +20,7 @@ class DupByRows { // Input has four dims. int64_t op_block_num = GetBlockNum(); int64_t op_block_idx = GetBlockIdx(); + assert(op_block_idx < SUPPORTED_MAX_DIM && op_block_idx >= 0, "Invalid block index:%d, max is:%d\n", op_block_idx, SUPPORTED_MAX_DIM); // param num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3]; @@ -51,24 +53,36 @@ class DupByRows { __aicore__ inline void copy_in() { LocalTensor src_local = src_queue.AllocTensor(); - - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = num_elem * sizeof(SRC_T); - DataCopyPadExtParams padParams; - DataCopyPad(src_local, src_gm, dataCopyParams, padParams); - + const size_t elem_per_block = 32 / sizeof(SRC_T); + size_t tail = num_elem % elem_per_block; + size_t cpy_elements_len = tail > 0 ? num_elem + 1 : num_elem; + DataCopy(src_local, src_gm, cpy_elements_len); src_queue.EnQue(src_local); } __aicore__ inline void copy_out() { LocalTensor dst_local = dst_queue.DeQue(); - +#ifdef ASCEND_310P + const size_t elem_per_block = 32 / sizeof(DST_T); + size_t tail = num_elem % elem_per_block; + size_t len = num_elem & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(dst_gm, dst_local, len); + } + if(tail != 0) { + for (size_t i = tail; i < elem_per_block; i++) { + dst_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(dst_gm[len], dst_local[len], elem_per_block); + SetAtomicNone(); + } +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = num_elem * sizeof(DST_T); DataCopyPad(dst_gm, dst_local, dataCopyParams); - +#endif dst_queue.FreeTensor(dst_local); } diff --git a/ggml/src/ggml-cann/kernels/get_row_f16.cpp b/ggml/src/ggml-cann/kernels/get_row_f16.cpp index c704b5b2ec..416b45104d 100644 --- a/ggml/src/ggml-cann/kernels/get_row_f16.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_f16.cpp @@ -14,7 +14,7 @@ class GET_ROW_F16 { int64_t *output_ne_ub, size_t *output_nb_ub) { // TODO, use template for F16/f32 int64_t op_block_num = GetBlockNum(); - int64_t op_block_idx = GetBlockIdx(); + op_block_idx = GetBlockIdx(); for (int i = 0; i < 4; i++) { input_ne[i] = input_ne_ub[i]; @@ -59,32 +59,42 @@ class GET_ROW_F16 { } __aicore__ inline void copy_in(uint32_t offset, size_t len) { + size_t origin_len = len; LocalTensor input_local = input_queue.AllocTensor(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(input_local, input_gm[offset], len); + const size_t elem_per_block = 32 / sizeof(half); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); if(tail != 0) { - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = tail * sizeof(half); - DataCopyPadExtParams padParams; - DataCopyPad(input_local[len], input_gm[offset + len], - dataCopyParams, padParams); + len += elem_per_block; } + DataCopy(input_local, input_gm[offset], len); input_queue.EnQue(input_local); } __aicore__ inline void copy_out(uint32_t offset, size_t len) { LocalTensor output_local = output_queue.DeQue(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(output_gm[offset], output_local, len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(output_gm[offset], output_local, len); + } + if(tail != 0) { +#ifdef ASCEND_310P + for (size_t i = tail; i < elem_per_block; i++) { + output_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(output_gm[offset + len], output_local[len], elem_per_block); + SetAtomicNone(); +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = tail * sizeof(float); DataCopyPad(output_gm[offset + len], output_local[len], dataCopyParams); +#endif } output_queue.FreeTensor(output_local); } @@ -150,6 +160,7 @@ class GET_ROW_F16 { GlobalTensor output_gm; TQue input_queue; TQue output_queue; + int64_t op_block_idx; }; template diff --git a/ggml/src/ggml-cann/kernels/get_row_f32.cpp b/ggml/src/ggml-cann/kernels/get_row_f32.cpp index 9db080af36..02116905b1 100644 --- a/ggml/src/ggml-cann/kernels/get_row_f32.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_f32.cpp @@ -13,7 +13,7 @@ class GET_ROW_F32 { int64_t *indices_ne_ub, size_t *indices_nb_ub, int64_t *output_ne_ub, size_t *output_nb_ub) { int64_t op_block_num = GetBlockNum(); - int64_t op_block_idx = GetBlockIdx(); + op_block_idx = GetBlockIdx(); for (int i = 0; i < 4; i++) { input_ne[i] = input_ne_ub[i]; @@ -55,31 +55,40 @@ class GET_ROW_F32 { __aicore__ inline void copy_in(uint32_t offset, size_t len) { LocalTensor input_local = input_queue.AllocTensor(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(input_local, input_gm[offset], len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); if(tail != 0) { - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = tail * sizeof(float); - DataCopyPadExtParams padParams; - DataCopyPad(input_local[len], input_gm[offset + len], - dataCopyParams, padParams); + len += elem_per_block; } + DataCopy(input_local, input_gm[offset], len); input_queue.EnQue(input_local); } __aicore__ inline void copy_out(uint32_t offset, size_t len) { LocalTensor output_local = output_queue.DeQue(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(output_gm[offset], output_local, len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(output_gm[offset], output_local, len); + } + if(tail != 0) { +#ifdef ASCEND_310P + for (size_t i = tail; i < elem_per_block; i++) { + output_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(output_gm[offset + len], output_local[len], elem_per_block); + SetAtomicNone(); +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = tail * sizeof(float); DataCopyPad(output_gm[offset + len], output_local[len], dataCopyParams); +#endif } output_queue.FreeTensor(output_local); } @@ -144,6 +153,7 @@ class GET_ROW_F32 { GlobalTensor output_gm; TQue input_queue; TQue output_queue; + int64_t op_block_idx; }; template diff --git a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp index a80bfeec24..377211096e 100644 --- a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp @@ -110,9 +110,12 @@ class GET_ROW_Q4_0 { LocalTensor output_local = output_queue.AllocTensor(); // TODO: cast more data to speed up. +#ifdef ASCEND_310P + // TODO: 310P support quantification +#else Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0); Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0); - +#endif // Only mul need compile by group. half scale = scale_gm.GetValue(scale_offset); diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt new file mode 100644 index 0000000000..2880523331 --- /dev/null +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -0,0 +1,266 @@ +add_library(ggml-cpu + ggml-cpu.c + ggml-cpu.cpp + ggml-cpu-aarch64.c + ggml-cpu-aarch64.h + ggml-cpu-quants.c + ggml-cpu-quants.h + ) + +target_link_libraries(ggml-cpu PRIVATE ggml-base) +target_include_directories(ggml-cpu PRIVATE . ..) + +if (APPLE AND GGML_ACCELERATE) + find_library(ACCELERATE_FRAMEWORK Accelerate) + if (ACCELERATE_FRAMEWORK) + message(STATUS "Accelerate framework found") + + add_compile_definitions(GGML_USE_ACCELERATE) + add_compile_definitions(ACCELERATE_NEW_LAPACK) + add_compile_definitions(ACCELERATE_LAPACK_ILP64) + + target_link_libraries(ggml-cpu PRIVATE ${ACCELERATE_FRAMEWORK}) + else() + message(WARNING "Accelerate framework not found") + endif() +endif() + +if (GGML_OPENMP) + find_package(OpenMP) + if (OpenMP_FOUND) + message(STATUS "OpenMP found") + + add_compile_definitions(GGML_USE_OPENMP) + + target_link_libraries(ggml-cpu PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + + # FIXME: should be replaced with a compiler id check + #if (GGML_MUSA) + # list(APPEND GGML_CPU_EXTRA_INCLUDES "/usr/lib/llvm-14/lib/clang/14.0.0/include") + # list(APPEND GGML_CPU_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-14/lib/libomp.so") + #endif() + else() + message(WARNING "OpenMP not found") + endif() +endif() + +if (GGML_LLAMAFILE) + message(STATUS "Using llamafile") + + add_compile_definitions(GGML_USE_LLAMAFILE) + + target_sources(ggml-cpu PRIVATE + llamafile/sgemm.cpp + llamafile/sgemm.h) +endif() + +if (GGML_CPU_HBM) + find_library(memkind memkind REQUIRED) + + message(STATUS "Using memkind for CPU HBM") + + add_compile_definitions(GGML_USE_CPU_HBM) + + target_link_libraries(ggml-cpu PUBLIC memkind) +endif() + +if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR + CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR + (NOT CMAKE_OSX_ARCHITECTURES AND + NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) + + message(STATUS "ARM detected") + + if (MSVC) + add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead + add_compile_definitions(__ARM_NEON) + add_compile_definitions(__ARM_FEATURE_FMA) + + set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) + string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) + if (GGML_COMPILER_SUPPORT_DOTPROD) + add_compile_definitions(__ARM_FEATURE_DOTPROD) + endif () + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + + if (GGML_COMPILER_SUPPORT_MATMUL_INT8) + add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + endif () + + check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + endif () + + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) + else() + check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + list(APPEND ARCH_FLAGS -mfp16-format=ieee) + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") + # Raspberry Pi 1, Zero + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") + if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") + # Android armeabi-v7a + list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) + else() + # Raspberry Pi 2 + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + endif() + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") + # Android arm64-v8a + # Raspberry Pi 3, 4, Zero 2 (32-bit) + list(APPEND ARCH_FLAGS -mno-unaligned-access) + endif() + if (GGML_SVE) + list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + endif() + endif() +elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) + message(STATUS "x86 detected") + if (MSVC) + # instruction set detection for MSVC only + if (GGML_NATIVE) + # TODO: improve, should not reference files from the parent folder + include(cmake/FindSIMD.cmake) + endif () + if (GGML_AVX512) + list(APPEND ARCH_FLAGS /arch:AVX512) + # MSVC has no compile-time flags enabling specific + # AVX512 extensions, neither it defines the + # macros corresponding to the extensions. + # Do it manually. + if (GGML_AVX512_VBMI) + add_compile_definitions($<$:__AVX512VBMI__>) + add_compile_definitions($<$:__AVX512VBMI__>) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512vbmi) + endif() + endif() + if (GGML_AVX512_VNNI) + add_compile_definitions($<$:__AVX512VNNI__>) + add_compile_definitions($<$:__AVX512VNNI__>) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512vnni) + endif() + endif() + if (GGML_AVX512_BF16) + add_compile_definitions($<$:__AVX512BF16__>) + add_compile_definitions($<$:__AVX512BF16__>) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() + endif() + if (GGML_AMX_TILE) + add_compile_definitions($<$:__AMX_TILE__>) + add_compile_definitions($<$:__AMX_TILE__>) + endif() + if (GGML_AMX_INT8) + add_compile_definitions($<$:__AMX_INT8__>) + add_compile_definitions($<$:__AMX_INT8__>) + endif() + if (GGML_AMX_BF16) + add_compile_definitions($<$:__AMX_BF16__>) + add_compile_definitions($<$:__AMX_BF16__>) + endif() + elseif (GGML_AVX2) + list(APPEND ARCH_FLAGS /arch:AVX2) + elseif (GGML_AVX) + list(APPEND ARCH_FLAGS /arch:AVX) + endif() + else() + if (GGML_NATIVE) + list(APPEND ARCH_FLAGS -march=native) + endif() + if (GGML_F16C) + list(APPEND ARCH_FLAGS -mf16c) + endif() + if (GGML_FMA) + list(APPEND ARCH_FLAGS -mfma) + endif() + if (GGML_AVX) + list(APPEND ARCH_FLAGS -mavx) + endif() + if (GGML_AVX2) + list(APPEND ARCH_FLAGS -mavx2) + endif() + if (GGML_AVX512) + list(APPEND ARCH_FLAGS -mavx512f) + list(APPEND ARCH_FLAGS -mavx512dq) + list(APPEND ARCH_FLAGS -mavx512bw) + endif() + if (GGML_AVX512_VBMI) + list(APPEND ARCH_FLAGS -mavx512vbmi) + endif() + if (GGML_AVX512_VNNI) + list(APPEND ARCH_FLAGS -mavx512vnni) + endif() + if (GGML_AVX512_BF16) + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() + if (GGML_AMX_TILE) + list(APPEND ARCH_FLAGS -mamx-tile) + endif() + if (GGML_AMX_INT8) + list(APPEND ARCH_FLAGS -mamx-int8) + endif() + if (GGML_AMX_BF16) + list(APPEND ARCH_FLAGS -mamx-bf16) + endif() + endif() +elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") + message(STATUS "PowerPC detected") + execute_process(COMMAND bash -c "grep POWER10 /proc/cpuinfo | head -n 1" OUTPUT_VARIABLE POWER10_M) + string(FIND "${POWER10_M}" "POWER10" substring_index) + if (NOT DEFINED substring_index OR "${substring_index}" STREQUAL "") + set(substring_index -1) + endif() + + if (${substring_index} GREATER_EQUAL 0) + list(APPEND ARCH_FLAGS -mcpu=power10) + elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") + list(APPEND ARCH_FLAGS -mcpu=powerpc64le) + else() + list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) + #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) + endif() +elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") + message(STATUS "loongarch64 detected") + + list(APPEND ARCH_FLAGS -march=loongarch64) + if (GGML_LASX) + list(APPEND ARCH_FLAGS -mlasx) + endif() + if (GGML_LSX) + list(APPEND ARCH_FLAGS -mlsx) + endif() +elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64") + message(STATUS "RISC-V detected") + if (GGML_RVV) + list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d) + endif() +else() + message(STATUS "Unknown architecture") +endif() + +if (GGML_CPU_AARCH64) + message(STATUS "Using runtime weight conversion of Q4_0 to Q4_0_x_x to enable optimized GEMM/GEMV kernels") + add_compile_definitions(GGML_USE_CPU_AARCH64) +endif() + +target_compile_options(ggml-cpu PRIVATE "$<$:${ARCH_FLAGS}>") +target_compile_options(ggml-cpu PRIVATE "$<$:${ARCH_FLAGS}>") + +if (EMSCRIPTEN) + set_target_properties(ggml-cpu PROPERTIES COMPILE_FLAGS "-msimd128") +endif() diff --git a/ggml/cmake/FindSIMD.cmake b/ggml/src/ggml-cpu/cmake/FindSIMD.cmake similarity index 100% rename from ggml/cmake/FindSIMD.cmake rename to ggml/src/ggml-cpu/cmake/FindSIMD.cmake diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c new file mode 100644 index 0000000000..96a16dfba1 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c @@ -0,0 +1,3560 @@ +// SPDX-FileCopyrightText: Copyright 2024 Arm Limited and/or its affiliates +// SPDX-License-Identifier: MIT +// + +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + +#include "ggml-quants.h" +#include "ggml-impl.h" +#include "ggml-cpu.h" +#include "ggml-cpu/ggml-cpu-impl.h" + +#include +#include +#include +#include +#include // for qsort +#include // for GGML_ASSERT + +#include "ggml-cpu-aarch64.h" + +#if defined(__GNUC__) +#pragma GCC diagnostic ignored "-Woverlength-strings" +#elif defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +#define UNUSED GGML_UNUSED + +// Functions to create the interleaved data layout formats + +// interleave 4 block_q4_0s in blocks of blck_size_interleave +// returns an interleaved block_q4_0x4 +// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks +// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave +// +// - in : an array of block_q4_0 pointers +// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of +// blck_size_interleave bytes +// - xor_mask : the mask to convert the nibbles in block_q4_0 quants bytes +// from bias offset form to pure sign form (this saves subtract +// operations durin unpacking) +// +#if defined(__AVX__) +#if defined(__F16C__) +#if defined(__AVX512F__) +#define GGML_F32Cx8x2_LOAD(x, y) _mm512_cvtph_ps(_mm256_set_m128i(_mm_loadu_si128((const __m128i *)(y)), _mm_loadu_si128((const __m128i *)(x)))) +#define GGML_F32Cx16_REPEAT_LOAD(x) _mm512_cvtph_ps(_mm256_set_m128i(x, x)) +#endif +// the _mm256_cvt intrinsics require F16C +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) +#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) _mm256_cvtph_ps(_mm_shuffle_epi32(_mm_maskload_epi32((int const*)(x), loadMask), 68)) +#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) _mm256_cvtph_ps(_mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)) +#else +#if defined(__AVX512F__) +static inline __m512 __avx512_f32cx8x2_load(ggml_fp16_t *x, ggml_fp16_t *y) { + float tmp[16]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + for (int i = 0; i < 8; i++) { + tmp[i + 8] = GGML_FP16_TO_FP32(y[i]); + } + + return _mm512_loadu_ps(tmp); +} +static inline __m512 __avx512_repeat_f32cx16_load(__m128i x) { + float tmp[16]; + uint16_t tmphalf[8]; + _mm_storeu_si128((__m128i*)tmphalf, x); + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 4] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 8] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 12] = GGML_FP16_TO_FP32(tmphalf[i]); + } + + return _mm512_loadu_ps(tmp); +} +#endif +static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline __m256 __avx_repeat_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + tmp[i + 4] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline __m256 __avx_rearranged_f32cx8_load(ggml_fp16_t *x, __m128i arrangeMask) { + uint16_t tmphalf[8]; + float tmp[8]; + + _mm_storeu_si128((__m128i*)tmphalf, _mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)); + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); + } + + return _mm256_loadu_ps(tmp); +} + +#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) +#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) __avx_repeat_f32cx8_load(x) +#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) __avx_rearranged_f32cx8_load(x, arrangeMask) +#if defined(__AVX512F__) +#define GGML_F32Cx8x2_LOAD(x, y) __avx512_f32cx8x2_load(x, y) +#define GGML_F32Cx16_REPEAT_LOAD(x) __avx512_repeat_f32cx16_load(x) +#endif +#endif +#endif + + +#if defined(__AVX2__) || defined(__AVX512F__) +#if defined(__AVX512F__) +// add int16_t pairwise and return as 512 bit int vector +static inline __m512i sum_i16_pairs_int_32x16(const __m512i x) { + const __m512i ones = _mm512_set1_epi16(1); + return _mm512_madd_epi16(ones, x); +} + +static inline __m512i mul_sum_us8_pairs_int32x16(const __m512i ax, const __m512i sy) { +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + const __m512i zero = _mm512_setzero_si512(); + return _mm512_dpbusd_epi32(zero, ax, sy); +#else + // Perform multiplication and create 16-bit values + const __m512i dot = _mm512_maddubs_epi16(ax, sy); + return sum_i16_pairs_int_32x16(dot); +#endif +} + +// multiply int8_t, add results pairwise twice and return as 512 bit int vector +static inline __m512i mul_sum_i8_pairs_int32x16(const __m512i x, const __m512i y) { + const __m512i zero = _mm512_setzero_si512(); + // Get absolute values of x vectors + const __m512i ax = _mm512_abs_epi8(x); + // Sign the values of the y vectors + __mmask64 blt0 = _mm512_movepi8_mask(x); + const __m512i sy = _mm512_mask_sub_epi8(y, blt0, zero, y); + return mul_sum_us8_pairs_int32x16(ax, sy); +} +#endif + +// add int16_t pairwise and return as 256 bit int vector +static inline __m256i sum_i16_pairs_int32x8(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + return _mm256_madd_epi16(ones, x); +} + +static inline __m256i mul_sum_us8_pairs_int32x8(const __m256i ax, const __m256i sy) { +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + const __m256i zero = _mm256_setzero_si256(); + return _mm256_dpbusd_epi32(zero, ax, sy); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_int32x8(dot); +#endif +} + +// Integer variant of the function defined in ggml-quants.c +// multiply int8_t, add results pairwise twice and return as 256 bit int vector +static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y) { +#if __AVXVNNIINT8__ + const __m256i zero = _mm256_setzero_si256(); + return _mm256_dpbssd_epi32(zero, x, y); +#else + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); + return mul_sum_us8_pairs_int32x8(ax, sy); +#endif +} +#endif + +static void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; + +#if defined(__ARM_NEON) + float32x4_t srcv[4][8]; + float id[4]; + + for (int i = 0; i < nb; i++) { + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int row_iter = 0; row_iter < 4; row_iter++) { + for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); + + for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); + for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); + for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < 8; j++) { + float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]); + int32x4_t vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[1][j], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[2][j], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[3][j], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3); + } + } +#else + // scalar + const int blck_size_interleave = 4; + float srcv[4][QK8_0]; + float id[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; + amax = MAX(amax, fabsf(srcv[row_iter][j])); + } + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < QK8_0 * 4; j++) { + int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; + int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; + src_offset += (j % blck_size_interleave); + + float x0 = srcv[src_id][src_offset] * id[src_id]; + y[i].qs[j] = roundf(x0); + } + } +#endif +} + +static void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; + +#if defined(__ARM_NEON) + float32x4_t srcv[4][8]; + float id[4]; + + for (int i = 0; i < nb; i++) { + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int row_iter = 0; row_iter < 4; row_iter++) { + for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); + + for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); + for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); + for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < 4; j++) { + float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]); + int32x4_t vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[1][2 * j], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[2][2 * j], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[3][2 * j], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3); + } + } +#elif defined(__AVX2__) || defined(__AVX__) + float id[4]; + __m256 srcv[4][4]; + __m256 idvec[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x + row_iter * k + i * 32 ); + __m256 v1 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 8 ); + __m256 v2 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 16 ); + __m256 v3 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 24 ); + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Divided by 127.f to mirror results in quantize_row_q8_0 + const float d = maxScalar / 127.f; + id[row_iter] = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; //d ? 1.0f / d : 0.0f; + + // Store the scale for the individual block + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + + // Store the values in blocks of eight values - Aim is to use these later for block interleaving + srcv[row_iter][0] = v0; + srcv[row_iter][1] = v1; + srcv[row_iter][2] = v2; + srcv[row_iter][3] = v3; + idvec[row_iter] = _mm256_set1_ps(id[row_iter]); + } + + // The loop iterates four times - The aim is to get 4 corresponding chunks of eight bytes from the original weight blocks that are interleaved + for (int j = 0; j < 4; j++) { + // Apply the multiplier + __m256 v0 = _mm256_mul_ps(srcv[0][j], idvec[0]); + __m256 v1 = _mm256_mul_ps(srcv[1][j], idvec[1]); + __m256 v2 = _mm256_mul_ps(srcv[2][j], idvec[2]); + __m256 v3 = _mm256_mul_ps(srcv[3][j], idvec[3]); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); + i2 = _mm256_packs_epi32( i2, i3 ); + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); + + // Permute and store the quantized weights in the required order after the pack instruction + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)(y[i].qs + 32 * j), i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j + 16), ni4); +#endif + } + } +#else + // scalar + const int blck_size_interleave = 8; + float srcv[4][QK8_0]; + float id[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; + amax = MAX(amax, fabsf(srcv[row_iter][j])); + } + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < QK8_0 * 4; j++) { + int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; + int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; + src_offset += (j % blck_size_interleave); + + float x0 = srcv[src_id][src_offset] * id[src_id]; + y[i].qs[j] = roundf(x0); + } + } +#endif +} + +void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) { + assert(nrow == 4); + UNUSED(nrow); + if (blck_size_interleave == 4) { + quantize_q8_0_4x4(x, vy, n_per_row); + } else if (blck_size_interleave == 8) { + quantize_q8_0_4x8(x, vy, n_per_row); + } else { + assert(false); + } +} + +void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + if (ggml_cpu_has_neon()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "movi v31.16b, #0x4\n" + "movi v30.16b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x8\n" + "1:" // Column loop + "add x22, %x[a_ptr], #0x2\n" + "movi v29.16b, #0x0\n" + "mov x21, %x[nb]\n" + "2:" // Block loop + "ldr q28, [%x[b_ptr], #0x0]\n" + "ldr q27, [x22, #0x0]\n" + "movi v26.4s, #0x0\n" + "sub x20, x22, #0x2\n" + "ldr q25, [x22, #0x10]\n" + "ldr q24, [%x[b_ptr], #0x10]\n" + "sub x21, x21, #0x1\n" + "add x22, x22, #0x22\n" + "ldr q23, [%x[b_ptr], #0x20]\n" + "ldr q22, [%x[b_ptr], #0x30]\n" + "ld1r { v21.8h }, [x20]\n" + "ldr q20, [%x[b_ptr], #-0x8]\n" + "sshl v16.16b, v28.16b, v31.16b\n" + "and v28.16b, v28.16b, v30.16b\n" + "sshl v19.16b, v24.16b, v31.16b\n" + "and v24.16b, v24.16b, v30.16b\n" + "add %x[b_ptr], %x[b_ptr], #0x48\n" + "sshl v18.16b, v23.16b, v31.16b\n" + "and v23.16b, v23.16b, v30.16b\n" + ".inst 0x4f9be21a // sdot v26.4s, v16.16b, v27.4b[0]\n" + "sshl v17.16b, v22.16b, v31.16b\n" + "and v22.16b, v22.16b, v30.16b\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v16.4s, v20.4h\n" + ".inst 0x4f99e39a // sdot v26.4s, v28.16b, v25.4b[0]\n" + "fmul v16.4s, v16.4s, v21.4s\n" + ".inst 0x4fbbe27a // sdot v26.4s, v19.16b, v27.4b[1]\n" + ".inst 0x4fb9e31a // sdot v26.4s, v24.16b, v25.4b[1]\n" + ".inst 0x4f9bea5a // sdot v26.4s, v18.16b, v27.4b[2]\n" + ".inst 0x4f99eafa // sdot v26.4s, v23.16b, v25.4b[2]\n" + ".inst 0x4fbbea3a // sdot v26.4s, v17.16b, v27.4b[3]\n" + ".inst 0x4fb9eada // sdot v26.4s, v22.16b, v25.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v29.4s, v26.4s, v16.4s\n" + "cbnz x21, 2b\n" + "sub %x[nc], %x[nc], #0x4\n" + "str q29, [%x[res_ptr], #0x0]\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "movi v2.16b, #0x4\n" + "movi v1.16b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x8\n" + "1:" // Column loop + "add x23, %x[a_ptr], #0x2\n" + "movi v0.16b, #0x0\n" + "mov x22, %x[nb]\n" + "2:" // Block loop + "ldr q31, [%x[b_ptr], #0x0]\n" + "ldr q30, [%x[b_ptr], #0x10]\n" + "mov x21, x23\n" + "movi v29.4s, #0x0\n" + "ldr q28, [%x[b_ptr], #0x20]\n" + "ldr q27, [%x[b_ptr], #0x30]\n" + "movi v26.4s, #0x0\n" + "sub x20, x23, #0x2\n" + "ld1r { v25.8h }, [x20]\n" + "ldr q24, [%x[b_ptr], #-0x8]\n" + "sub x22, x22, #0x1\n" + "add x23, x23, #0x22\n" + "ld1r { v23.2d }, [x21], #0x8\n" + "sshl v22.16b, v31.16b, v2.16b\n" + "sshl v16.16b, v30.16b, v2.16b\n" + "add %x[b_ptr], %x[b_ptr], #0x48\n" + "ld1r { v21.2d }, [x21], #0x8\n" + "sshl v20.16b, v28.16b, v2.16b\n" + "sshl v19.16b, v27.16b, v2.16b\n" + "ld1r { v18.2d }, [x21], #0x8\n" + "ld1r { v17.2d }, [x21], #0x8\n" + "and v31.16b, v31.16b, v1.16b\n" + "and v30.16b, v30.16b, v1.16b\n" + ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" + ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" + "and v28.16b, v28.16b, v1.16b\n" + "and v27.16b, v27.16b, v1.16b\n" + "fcvtl v25.4s, v25.4h\n" + "fcvtl v16.4s, v24.4h\n" + ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" + ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" + "fmul v16.4s, v16.4s, v25.4s\n" + ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" + ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" + ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" + ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" + "addp v29.4s, v29.4s, v26.4s\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v0.4s, v29.4s, v16.4s\n" + "cbnz x22, 2b\n" + "sub %x[nc], %x[nc], #0x4\n" + "str q0, [%x[res_ptr], #0x0]\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) + if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "ptrue p0.b\n" + "add %x[b_ptr], %x[b_ptr], #0x10\n" + "1:" // Column loop + "add x22, %x[a_ptr], #0x2\n" + "mov z31.b, #0x0\n" + "mov x21, %x[nb]\n" + "2:" // Block loop + "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n" + "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n" + "mov z28.s, #0x0\n" + "mov z27.s, #0x0\n" + "ld1rd { z26.d }, p0/Z, [x22]\n" + "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n" + "sub x20, x22, #0x2\n" + "sub x21, x21, #0x1\n" + "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n" + "ld1rd { z23.d }, p0/Z, [x22, #8]\n" + "lsl z22.b, z30.b, #0x4\n" + "lsl z16.b, z29.b, #0x4\n" + "and z30.b, z30.b, #0xf0\n" + "and z29.b, z29.b, #0xf0\n" + "ld1rd { z21.d }, p0/Z, [x22, #16]\n" + "ld1rd { z20.d }, p0/Z, [x22, #24]\n" + "lsl z19.b, z25.b, #0x4\n" + "and z25.b, z25.b, #0xf0\n" + "ld1rh { z17.h }, p0/Z, [x20]\n" + "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n" + "sdot z28.s, z22.b, z26.b\n" + "sdot z27.s, z16.b, z26.b\n" + "lsl z16.b, z24.b, #0x4\n" + "add x22, x22, #0x22\n" + "and z24.b, z24.b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x90\n" + "fcvt z17.s, p0/m, z17.h\n" + "fcvt z18.s, p0/m, z18.h\n" + "sdot z28.s, z19.b, z23.b\n" + "sdot z27.s, z16.b, z23.b\n" + "fmul z18.s, z18.s, z17.s\n" + "sdot z28.s, z30.b, z21.b\n" + "sdot z27.s, z29.b, z21.b\n" + "sdot z28.s, z25.b, z20.b\n" + "sdot z27.s, z24.b, z20.b\n" + "uzp1 z17.s, z28.s, z27.s\n" + "uzp2 z16.s, z28.s, z27.s\n" + "add z17.s, z17.s, z16.s\n" + "asr z17.s, z17.s, #0x4\n" + "scvtf z17.s, p0/m, z17.s\n" + "fmla z31.s, p0/M, z17.s, z18.s\n" + "cbnz x21, 2b\n" + "sub %x[nc], %x[nc], #0x8\n" + "st1w { z31.s }, p0, [%x[res_ptr]]\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" + ); + return; + } +#endif // #if defined(__ARM_FEATURE_SVE) +#elif defined(__AVX2__) + // Lookup table to convert signed nibbles to signed bytes + __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0); + __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0); + + // Permute mask used for easier vector processing at later stages + const __m256i m4b = _mm256_set1_epi8(0x0F); + + int64_t b_nb = n / QK4_0; + + const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; + const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy; + + // Process Q8_0 blocks one by one + for (int64_t y = 0; y < nr; y++) { + + // Pointers to LHS blocks of block_q8_0 format + const block_q8_0 * a_ptr = a_ptr_start + (y * nb); + + // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < nc / 8; x++) { + + // Pointers to RHS blocks + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulator + __m256 acc_row = _mm256_setzero_ps(); + + for (int64_t b = 0; b < nb; b++) { + // Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7) + const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1); + const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2); + const __m256i rhs_raw_vec_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 3); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_vec_0123_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_0, m4b)); // B0(0-7) B1(0-7) B2(0-7) B3(0-7) + const __m256i rhs_vec_4567_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_0, m4b)); // B4(0-7) B5(0-7) B6(0-7) B7(0-7) + const __m256i rhs_vec_0123_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) + const __m256i rhs_vec_4567_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) + + const __m256i rhs_vec_0123_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_0, 4), m4b)); // B0(16-23) B1(16-23) B2(16-23) B3(16-23) + const __m256i rhs_vec_4567_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_0, 4), m4b)); // B4(16-23) B5(16-23) B6(16-23) B7(16-23) + const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31) + const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31) + + // Load the scale values for the 8 blocks interleaved in block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask); + + // Load and convert to FP32 scale from block_q8_0 + const __m256 row_scale_f32 = _mm256_set1_ps(GGML_FP16_TO_FP32(a_ptr[b].d)); + + // Load the block values in block_q8_0 in batches of 16 bytes and replicate the same across 256 bit vector + __m256i lhs_vec_0 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)a_ptr[b].qs)); + __m256i lhs_vec_1 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)(a_ptr[b].qs + 16))); + + lhs_vec_0 = _mm256_permute2f128_si256(lhs_vec_0, lhs_vec_0, 0); // A0 (0-15) A0(0-15) + lhs_vec_1 = _mm256_permute2f128_si256(lhs_vec_1, lhs_vec_1, 0); // A0 (16-31) A0(16-31)) + + __m256i iacc = _mm256_setzero_si256(); + + // Dot product done within 32 bit lanes and accumulated in the same vector + // B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3) + // B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7) + // ........................................................................... + // B0(28-31) B4(28-31) B1(28-31) B5(28-31) B2(28-31) B6(28-31) B3(28-31) B7(28-31) with A0(28-31) + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_0 ,_mm256_shuffle_epi32(rhs_vec_4567_0, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 0))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_0, 177) ,rhs_vec_4567_0, 170), _mm256_shuffle_epi32(lhs_vec_0, 85))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_1 ,_mm256_shuffle_epi32(rhs_vec_4567_1, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 170))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_1, 177) ,rhs_vec_4567_1, 170), _mm256_shuffle_epi32(lhs_vec_0, 255))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_2 ,_mm256_shuffle_epi32(rhs_vec_4567_2, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 0))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_2, 177) ,rhs_vec_4567_2, 170), _mm256_shuffle_epi32(lhs_vec_1, 85))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255))); + + // Accumulated values multipled with appropriate scales + acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row); + } + + // Accumulated output values permuted so as to be stored in appropriate order post accumulation + acc_row = _mm256_permutevar8x32_ps(acc_row, finalpermutemask); + _mm256_storeu_ps(s + (y * nr + x * 8), acc_row); + } + } + return; +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + + vfloat32m1_t sumf = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const int64_t a0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t a1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t a2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t a3 = *(const int64_t *)&a_ptr[l].qs[24]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a1, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a2, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a3, vl / 4)); + + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_hi_m)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + // vector version needs Zvfhmin extension + const float a_scale = GGML_FP16_TO_FP32(a_ptr[l].d); + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scale, vl / 4); + sumf = __riscv_vfmacc_vv_f32m1(sumf, tmp1, b_scales_vec, vl / 4); + } + __riscv_vse32_v_f32m1(s + x * ncols_interleaved, sumf, vl / 4); + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) + { + float sumf[8]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + +void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + if (ggml_cpu_has_neon()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v23.16b, #0x0\n" + "movi v16.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v0.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v21.16b, #0x0\n" + "movi v8.16b, #0x0\n" + "movi v1.16b, #0x0\n" + "3:" // Block loop + "ldr q3, [x28, #0x0]\n" + "ldr q31, [x25, #0x0]\n" + "movi v28.16b, #0x4\n" + "movi v10.4s, #0x0\n" + "ldr q22, [x28, #0x10]\n" + "ldr q6, [x25, #0x10]\n" + "movi v29.4s, #0x0\n" + "movi v9.4s, #0x0\n" + "ldr q27, [x28, #0x20]\n" + "ldr q30, [x28, #0x30]\n" + "movi v20.4s, #0x0\n" + "movi v24.16b, #0xf0\n" + "ldr d2, [x25, #-0x8]\n" + "ldr d26, [x23, #-0x8]\n" + "sshl v12.16b, v3.16b, v28.16b\n" + "sub x20, x28, #0x8\n" + "ldr d17, [x20, #0x0]\n" + "and v3.16b, v3.16b, v24.16b\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" + ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" + ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" + ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" + "sshl v31.16b, v22.16b, v28.16b\n" + "and v22.16b, v22.16b, v24.16b\n" + "fcvtl v17.4s, v17.4h\n" + "fcvtl v2.4s, v2.4h\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" + ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" + ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" + ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" + "sshl v6.16b, v27.16b, v28.16b\n" + "sshl v28.16b, v30.16b, v28.16b\n" + "and v27.16b, v27.16b, v24.16b\n" + "and v30.16b, v30.16b, v24.16b\n" + "ldr q24, [x25, #0x20]\n" + ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x30]\n" + ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" + ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" + ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" + ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x40]\n" + ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x50]\n" + ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" + ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" + ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" + ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x60]\n" + ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" + ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" + ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" + ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" + "fmul v24.4s, v17.4s, v2.s[0]\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v15.4s, v10.4s, v24.4s\n" + "ldr q24, [x23, #0x0]\n" + "fmul v10.4s, v17.4s, v2.s[1]\n" + "fmla v19.4s, v29.4s, v10.4s\n" + "ldr q10, [x23, #0x10]\n" + "fmul v29.4s, v17.4s, v2.s[2]\n" + "fmul v2.4s, v17.4s, v2.s[3]\n" + "fmla v18.4s, v9.4s, v29.4s\n" + "movi v9.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" + "fmla v14.4s, v20.4s, v2.4s\n" + "movi v20.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x20]\n" + ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" + ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" + ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" + ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x30]\n" + ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x40]\n" + ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" + ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" + ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" + ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x50]\n" + ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x60]\n" + ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" + ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" + ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" + ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x0]\n" + ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" + ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" + ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" + ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" + "fmul v10.4s, v17.4s, v26.s[0]\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v11.4s, v9.4s, v10.4s\n" + "ldr q9, [x22, #0x10]\n" + "fmul v10.4s, v17.4s, v26.s[1]\n" + "fmla v13.4s, v29.4s, v10.4s\n" + "ldr d29, [x22, #-0x8]\n" + "fmul v10.4s, v17.4s, v26.s[2]\n" + "fmul v26.4s, v17.4s, v26.s[3]\n" + "fcvtl v29.4s, v29.4h\n" + "fmla v23.4s, v20.4s, v10.4s\n" + "movi v20.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v16.4s, v2.4s, v26.4s\n" + "movi v26.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x20]\n" + ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x30]\n" + ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x40]\n" + ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x50]\n" + ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x60]\n" + ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x21, #0x0]\n" + ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" + ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" + ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" + "fmul v9.4s, v17.4s, v29.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v25.4s, v20.4s, v9.4s\n" + "ldr q9, [x21, #0x10]\n" + "fmul v20.4s, v17.4s, v29.s[1]\n" + "fmla v7.4s, v10.4s, v20.4s\n" + "ldr d20, [x21, #-0x8]\n" + "fmul v10.4s, v17.4s, v29.s[2]\n" + "fmul v29.4s, v17.4s, v29.s[3]\n" + "fcvtl v20.4s, v20.4h\n" + "fmla v0.4s, v26.4s, v10.4s\n" + "movi v26.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v4.4s, v2.4s, v29.4s\n" + "movi v2.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" + "ldr q12, [x21, #0x20]\n" + "fmul v24.4s, v17.4s, v20.s[0]\n" + ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x30]\n" + "fmul v31.4s, v17.4s, v20.s[1]\n" + ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" + ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" + ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" + ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x40]\n" + "fmul v6.4s, v17.4s, v20.s[2]\n" + "fmul v20.4s, v17.4s, v20.s[3]\n" + ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x50]\n" + ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" + ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" + ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" + ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x60]\n" + ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" + "ldr q17, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" + ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" + ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" + ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" + ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" + ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" + ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" + ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "fmla v5.4s, v26.4s, v24.4s\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v21.4s, v10.4s, v31.4s\n" + "fmla v8.4s, v2.4s, v6.4s\n" + "fmla v1.4s, v29.4s, v20.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q16, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q0, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q21, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q8, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q1, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q7, [x24, #0x0]\n" + "ldr q5, [x25, #0x0]\n" + "movi v9.16b, #0x4\n" + "movi v4.4s, #0x0\n" + "ldr q3, [x24, #0x10]\n" + "ldr q2, [x25, #0x10]\n" + "movi v1.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q13, [x24, #0x20]\n" + "ldr q31, [x25, #0x20]\n" + "movi v30.4s, #0x0\n" + "movi v29.16b, #0xf0\n" + "ldr q28, [x24, #0x30]\n" + "ldr q27, [x25, #0x30]\n" + "sshl v20.16b, v7.16b, v9.16b\n" + "sub x20, x24, #0x8\n" + "ldr q26, [x25, #0x40]\n" + "ldr q25, [x25, #0x50]\n" + "sshl v17.16b, v3.16b, v9.16b\n" + "and v7.16b, v7.16b, v29.16b\n" + "ldr q24, [x25, #0x60]\n" + "ldr q16, [x25, #0x70]\n" + "sshl v22.16b, v13.16b, v9.16b\n" + "and v3.16b, v3.16b, v29.16b\n" + "ldr d21, [x20, #0x0]\n" + "ldr d12, [x25, #-0x8]\n" + ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" + ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" + ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" + ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" + "sshl v9.16b, v28.16b, v9.16b\n" + "subs x21, x21, #0x1\n" + "and v13.16b, v13.16b, v29.16b\n" + "and v28.16b, v28.16b, v29.16b\n" + "add x25, x25, #0x88\n" + "add x24, x24, #0x48\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v12.4s, v12.4h\n" + ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" + ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" + ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" + ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" + "fmul v11.4s, v21.4s, v12.s[0]\n" + "fmul v23.4s, v21.4s, v12.s[1]\n" + "fmul v17.4s, v21.4s, v12.s[2]\n" + ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" + "fmul v6.4s, v21.4s, v12.s[3]\n" + ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" + ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" + ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" + ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" + ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" + ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" + ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" + ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" + ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" + ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" + ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" + ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" + ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" + ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" + ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" + ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" + ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" + ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" + ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" + ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" + ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" + ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" + ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" + "scvtf v4.4s, v4.4s, #0x4\n" + "scvtf v1.4s, v1.4s, #0x4\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "fmla v15.4s, v4.4s, v11.4s\n" + "scvtf v30.4s, v30.4s, #0x4\n" + "fmla v19.4s, v1.4s, v23.4s\n" + "fmla v18.4s, v0.4s, v17.4s\n" + "fmla v14.4s, v30.4s, v6.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q14, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } + } +} + +void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v22.16b, #0x0\n" + "movi v23.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v6.16b, #0x0\n" + "movi v30.16b, #0x0\n" + "movi v24.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "3:" // Block loop + "ldr q21, [x28, #0x0]\n" + "ldr q16, [x28, #0x10]\n" + "movi v1.16b, #0x4\n" + "movi v19.4s, #0x0\n" + "ldr q27, [x25, #0x0]\n" + "ldr q15, [x25, #0x10]\n" + "movi v26.4s, #0x0\n" + "movi v18.4s, #0x0\n" + "ldr q29, [x28, #0x20]\n" + "ldr q3, [x28, #0x30]\n" + "movi v17.4s, #0x0\n" + "movi v0.16b, #0xf0\n" + "ldr d20, [x25, #-0x8]\n" + "ldr d9, [x23, #-0x8]\n" + "sshl v8.16b, v21.16b, v1.16b\n" + "sshl v31.16b, v16.16b, v1.16b\n" + "and v21.16b, v21.16b, v0.16b\n" + "and v16.16b, v16.16b, v0.16b\n" + "sub x20, x28, #0x8\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" + ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" + "ldr q27, [x25, #0x20]\n" + ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" + ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" + "sshl v15.16b, v29.16b, v1.16b\n" + "sshl v1.16b, v3.16b, v1.16b\n" + "and v29.16b, v29.16b, v0.16b\n" + "and v3.16b, v3.16b, v0.16b\n" + "ldr q0, [x25, #0x30]\n" + "fcvtl v20.4s, v20.4h\n" + ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" + "fcvtl v9.4s, v9.4h\n" + ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" + "ldr q27, [x25, #0x40]\n" + ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" + ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" + "ldr q0, [x25, #0x50]\n" + ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" + ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" + "ldr q27, [x25, #0x60]\n" + ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" + ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" + "ldr q0, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" + ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" + "ldr d27, [x20, #0x0]\n" + ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" + ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" + "fcvtl v27.4s, v27.4h\n" + "uzp1 v0.2d, v19.2d, v26.2d\n" + "uzp2 v26.2d, v19.2d, v26.2d\n" + "fmul v19.4s, v27.4s, v20.s[0]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v2.4s, v0.4s, v19.4s\n" + "ldr q19, [x23, #0x0]\n" + "uzp1 v0.2d, v18.2d, v17.2d\n" + "uzp2 v18.2d, v18.2d, v17.2d\n" + "fmul v17.4s, v27.4s, v20.s[1]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v10.4s, v26.4s, v17.4s\n" + "ldr q17, [x23, #0x10]\n" + "fmul v26.4s, v27.4s, v20.s[2]\n" + "fmul v20.4s, v27.4s, v20.s[3]\n" + "fmla v12.4s, v0.4s, v26.4s\n" + "ldr d0, [x22, #-0x8]\n" + "ldr d26, [x21, #-0x8]\n" + "fcvtl v0.4s, v0.4h\n" + "fmla v28.4s, v18.4s, v20.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x23, #0x20]\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x23, #0x40]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q19, [x23, #0x60]\n" + ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" + ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" + "uzp1 v19.2d, v20.2d, v18.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp2 v20.2d, v20.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v9.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v11.4s, v19.4s, v18.4s\n" + "ldr q18, [x22, #0x0]\n" + "fmul v19.4s, v27.4s, v9.s[1]\n" + "fmla v13.4s, v20.4s, v19.4s\n" + "movi v19.4s, #0x0\n" + "movi v20.4s, #0x0\n" + ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" + ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" + "ldr q17, [x23, #0x30]\n" + ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" + "ldr q17, [x23, #0x50]\n" + ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" + "ldr q17, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v9.s[2]\n" + "fmul v9.4s, v27.4s, v9.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v22.4s, v17.4s, v19.4s\n" + "ldr q17, [x22, #0x10]\n" + "movi v19.4s, #0x0\n" + ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" + "fmla v23.4s, v20.4s, v9.4s\n" + "movi v20.4s, #0x0\n" + "movi v9.4s, #0x0\n" + ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" + "ldr q18, [x22, #0x20]\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" + ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" + "ldr q18, [x22, #0x40]\n" + ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" + ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" + "ldr q18, [x22, #0x60]\n" + ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" + ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" + "ldr q17, [x22, #0x30]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" + "ldr q17, [x22, #0x50]\n" + ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" + "ldr q17, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v0.s[0]\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v25.4s, v17.4s, v19.4s\n" + "ldr q19, [x21, #0x0]\n" + "fmul v17.4s, v27.4s, v0.s[1]\n" + "fmla v5.4s, v20.4s, v17.4s\n" + "ldr q17, [x21, #0x10]\n" + "uzp1 v20.2d, v9.2d, v18.2d\n" + "uzp2 v9.2d, v9.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v0.s[2]\n" + "fmul v0.4s, v27.4s, v0.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "fmla v7.4s, v20.4s, v18.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x21, #0x20]\n" + "fmla v4.4s, v9.4s, v0.4s\n" + "movi v9.4s, #0x0\n" + "movi v0.4s, #0x0\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + "fmul v8.4s, v27.4s, v26.s[0]\n" + ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" + "ldr q17, [x21, #0x30]\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + "fmul v31.4s, v27.4s, v26.s[1]\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x21, #0x40]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + "fmul v15.4s, v27.4s, v26.s[2]\n" + "fmul v27.4s, v27.4s, v26.s[3]\n" + ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" + "ldr q1, [x21, #0x50]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q26, [x21, #0x60]\n" + ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" + ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" + "ldr q21, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" + ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" + ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" + ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" + "uzp1 v29.2d, v20.2d, v18.2d\n" + "uzp2 v21.2d, v20.2d, v18.2d\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "uzp1 v18.2d, v9.2d, v0.2d\n" + "uzp2 v16.2d, v9.2d, v0.2d\n" + "scvtf v21.4s, v21.4s, #0x4\n" + "fmla v6.4s, v29.4s, v8.4s\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v30.4s, v21.4s, v31.4s\n" + "fmla v24.4s, v18.4s, v15.4s\n" + "fmla v14.4s, v16.4s, v27.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q28, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q22, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q6, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q30, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q24, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q6, [x24, #0x0]\n" + "ldr q5, [x24, #0x10]\n" + "movi v17.16b, #0x4\n" + "movi v8.4s, #0x0\n" + "ldr q4, [x25, #0x0]\n" + "ldr q13, [x25, #0x10]\n" + "movi v27.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q31, [x24, #0x20]\n" + "ldr q14, [x24, #0x30]\n" + "movi v29.4s, #0x0\n" + "movi v22.16b, #0xf0\n" + "ldr q11, [x25, #0x20]\n" + "ldr q23, [x25, #0x30]\n" + "sshl v21.16b, v6.16b, v17.16b\n" + "sshl v16.16b, v5.16b, v17.16b\n" + "ldr q20, [x25, #0x40]\n" + "ldr q26, [x25, #0x50]\n" + "and v6.16b, v6.16b, v22.16b\n" + "and v5.16b, v5.16b, v22.16b\n" + "ldr q25, [x25, #0x60]\n" + "ldr q3, [x25, #0x70]\n" + "sshl v19.16b, v31.16b, v17.16b\n" + "sshl v18.16b, v14.16b, v17.16b\n" + "ldr d17, [x25, #-0x8]\n" + ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" + ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" + "and v31.16b, v31.16b, v22.16b\n" + ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" + ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" + "and v14.16b, v14.16b, v22.16b\n" + "sub x20, x24, #0x8\n" + "ldr d16, [x20, #0x0]\n" + "subs x21, x21, #0x1\n" + "add x25, x25, #0x88\n" + "fcvtl v17.4s, v17.4h\n" + "add x24, x24, #0x48\n" + ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" + ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" + ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" + ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" + "fcvtl v16.4s, v16.4h\n" + ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" + ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" + "fmul v23.4s, v16.4s, v17.s[0]\n" + "fmul v21.4s, v16.4s, v17.s[1]\n" + "fmul v1.4s, v16.4s, v17.s[2]\n" + "fmul v20.4s, v16.4s, v17.s[3]\n" + ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" + ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" + ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" + ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" + ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" + ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" + "uzp1 v19.2d, v8.2d, v27.2d\n" + "uzp2 v18.2d, v8.2d, v27.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp1 v17.2d, v0.2d, v29.2d\n" + "uzp2 v16.2d, v0.2d, v29.2d\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v2.4s, v19.4s, v23.4s\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v10.4s, v18.4s, v21.4s\n" + "fmla v12.4s, v17.4s, v1.4s\n" + "fmla v28.4s, v16.4s, v20.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q28, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x20, #0x4\n" + "mov x13, %x[nr]\n" + "mov z28.s, #-0x4\n" + "mov x12, #0x88\n" + "ptrue p1.b\n" + "whilelt p0.s, XZR, x20\n" + "cmp x13, #0x10\n" + "mul x12, %x[nb], x12\n" + "blt 4f\n" + "1:" // Row loop + "add x11, %x[b_ptr], #0x10\n" + "mov x10, %x[nc]\n" + "add x9, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x28, %x[a_ptr], #0x8\n" + "mov z24.b, #0x0\n" + "mov z15.b, #0x0\n" + "mov x27, %x[nb]\n" + "add x26, x28, x12\n" + "mov z12.b, #0x0\n" + "mov z0.b, #0x0\n" + "add x25, x26, x12\n" + "mov z13.b, #0x0\n" + "mov z1.b, #0x0\n" + "add x24, x25, x12\n" + "mov z20.b, #0x0\n" + "mov z25.b, #0x0\n" + "mov z11.b, #0x0\n" + "mov z16.b, #0x0\n" + "mov z19.b, #0x0\n" + "mov z26.b, #0x0\n" + "mov z8.b, #0x0\n" + "mov z29.b, #0x0\n" + "mov z27.b, #0x0\n" + "mov z10.b, #0x0\n" + "3:" // Block loop + "ld1b { z30.b }, p1/Z, [x11]\n" + "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n" + "mov z18.s, #0x0\n" + "mov z7.s, #0x0\n" + "ld1rqb { z3.b }, p1/Z, [x28]\n" + "ld1rqb { z5.b }, p1/Z, [x28, #16]\n" + "mov z9.s, #0x0\n" + "mov z22.s, #0x0\n" + "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n" + "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n" + "sub x20, x11, #0x10\n" + "sub x23, x28, #0x8\n" + "lsl z31.b, z30.b, #0x4\n" + "lsl z6.b, z21.b, #0x4\n" + "ld1h { z23.s }, p1/Z, [x20]\n" + "sub x22, x26, #0x8\n" + "and z30.b, z30.b, #0xf0\n" + "and z21.b, z21.b, #0xf0\n" + "sub x21, x25, #0x8\n" + "sub x20, x24, #0x8\n" + "lsl z14.b, z4.b, #0x4\n" + "lsl z2.b, z17.b, #0x4\n" + "subs x27, x27, #0x1\n" + "add x11, x11, #0x90\n" + ".inst 0x451f9872 // smmla z18.s, z3.b, z31.b\n" + ".inst 0x45069867 // smmla z7.s, z3.b, z6.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #32]\n" + "and z4.b, z4.b, #0xf0\n" + ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" + ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #48]\n" + "and z17.b, z17.b, #0xf0\n" + "fcvt z23.s, p1/m, z23.h\n" + ".inst 0x450e9872 // smmla z18.s, z3.b, z14.b\n" + ".inst 0x45029867 // smmla z7.s, z3.b, z2.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #64]\n" + ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" + ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #80]\n" + "fscale z23.s, p1/m, z23.s, z28.s\n" + ".inst 0x451e9872 // smmla z18.s, z3.b, z30.b\n" + ".inst 0x45159867 // smmla z7.s, z3.b, z21.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #96]\n" + ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" + ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #112]\n" + "add x28, x28, #0x88\n" + ".inst 0x45049872 // smmla z18.s, z3.b, z4.b\n" + ".inst 0x45119867 // smmla z7.s, z3.b, z17.b\n" + "ld1h { z3.s }, p0/Z, [x23]\n" + ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" + ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" + "fcvt z3.s, p1/m, z3.h\n" + "uzp1 z5.d, z18.d, z7.d\n" + "uzp2 z18.d, z18.d, z7.d\n" + "mov z3.q, z3.q[0]\n" + "uzp1 z7.d, z9.d, z22.d\n" + "uzp2 z22.d, z9.d, z22.d\n" + "fmul z9.s, z23.s, z3.s[0]\n" + "scvtf z5.s, p1/m, z5.s\n" + "scvtf z18.s, p1/m, z18.s\n" + "scvtf z7.s, p1/m, z7.s\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z24.s, p1/M, z5.s, z9.s\n" + "ld1rqb { z5.b }, p1/Z, [x26]\n" + "fmul z9.s, z23.s, z3.s[1]\n" + "fmla z15.s, p1/M, z18.s, z9.s\n" + "ld1rqb { z18.b }, p1/Z, [x26, #16]\n" + "fmul z9.s, z23.s, z3.s[2]\n" + "fmul z3.s, z23.s, z3.s[3]\n" + "fmla z12.s, p1/M, z7.s, z9.s\n" + "mov z9.s, #0x0\n" + "ld1h { z7.s }, p0/Z, [x22]\n" + ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" + "fmla z0.s, p1/M, z22.s, z3.s\n" + "mov z22.s, #0x0\n" + "ld1h { z3.s }, p0/Z, [x21]\n" + ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #32]\n" + "fcvt z7.s, p1/m, z7.h\n" + "fcvt z3.s, p1/m, z3.h\n" + ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" + ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #64]\n" + "mov z7.q, z7.q[0]\n" + "mov z3.q, z3.q[0]\n" + ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" + ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #96]\n" + ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" + ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" + "uzp1 z5.d, z9.d, z22.d\n" + "scvtf z5.s, p1/m, z5.s\n" + "uzp2 z22.d, z9.d, z22.d\n" + "fmul z9.s, z23.s, z7.s[0]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z13.s, p1/M, z5.s, z9.s\n" + "ld1rqb { z9.b }, p1/Z, [x25]\n" + "fmul z5.s, z23.s, z7.s[1]\n" + "fmla z1.s, p1/M, z22.s, z5.s\n" + "mov z5.s, #0x0\n" + "mov z22.s, #0x0\n" + ".inst 0x451f9a45 // smmla z5.s, z18.b, z31.b\n" + ".inst 0x45069a56 // smmla z22.s, z18.b, z6.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #48]\n" + ".inst 0x450e9a45 // smmla z5.s, z18.b, z14.b\n" + ".inst 0x45029a56 // smmla z22.s, z18.b, z2.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #80]\n" + ".inst 0x451e9a45 // smmla z5.s, z18.b, z30.b\n" + ".inst 0x45159a56 // smmla z22.s, z18.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #112]\n" + "add x26, x26, #0x88\n" + ".inst 0x45049a45 // smmla z5.s, z18.b, z4.b\n" + ".inst 0x45119a56 // smmla z22.s, z18.b, z17.b\n" + "uzp1 z18.d, z5.d, z22.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp2 z22.d, z5.d, z22.d\n" + "fmul z5.s, z23.s, z7.s[2]\n" + "fmul z7.s, z23.s, z7.s[3]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z20.s, p1/M, z18.s, z5.s\n" + "ld1rqb { z18.b }, p1/Z, [x25, #16]\n" + "ld1h { z5.s }, p0/Z, [x20]\n" + "fcvt z5.s, p1/m, z5.h\n" + "fmla z25.s, p1/M, z22.s, z7.s\n" + "mov z22.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9936 // smmla z22.s, z9.b, z31.b\n" + ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #32]\n" + "mov z5.q, z5.q[0]\n" + ".inst 0x450e9936 // smmla z22.s, z9.b, z14.b\n" + ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #64]\n" + ".inst 0x451e9936 // smmla z22.s, z9.b, z30.b\n" + ".inst 0x45159927 // smmla z7.s, z9.b, z21.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #96]\n" + ".inst 0x45049936 // smmla z22.s, z9.b, z4.b\n" + ".inst 0x45119927 // smmla z7.s, z9.b, z17.b\n" + "uzp1 z9.d, z22.d, z7.d\n" + "scvtf z9.s, p1/m, z9.s\n" + "uzp2 z22.d, z22.d, z7.d\n" + "fmul z7.s, z23.s, z3.s[0]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z11.s, p1/M, z9.s, z7.s\n" + "ld1rqb { z9.b }, p1/Z, [x24]\n" + "fmul z7.s, z23.s, z3.s[1]\n" + "fmla z16.s, p1/M, z22.s, z7.s\n" + "mov z22.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9a56 // smmla z22.s, z18.b, z31.b\n" + ".inst 0x45069a47 // smmla z7.s, z18.b, z6.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #48]\n" + ".inst 0x450e9a56 // smmla z22.s, z18.b, z14.b\n" + ".inst 0x45029a47 // smmla z7.s, z18.b, z2.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #80]\n" + ".inst 0x451e9a56 // smmla z22.s, z18.b, z30.b\n" + ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #112]\n" + "add x25, x25, #0x88\n" + ".inst 0x45049a56 // smmla z22.s, z18.b, z4.b\n" + ".inst 0x45119a47 // smmla z7.s, z18.b, z17.b\n" + "uzp1 z18.d, z22.d, z7.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp2 z7.d, z22.d, z7.d\n" + "fmul z22.s, z23.s, z3.s[2]\n" + "fmul z3.s, z23.s, z3.s[3]\n" + "scvtf z7.s, p1/m, z7.s\n" + "fmla z19.s, p1/M, z18.s, z22.s\n" + "ld1rqb { z18.b }, p1/Z, [x24, #16]\n" + "fmul z22.s, z23.s, z5.s[0]\n" + "fmla z26.s, p1/M, z7.s, z3.s\n" + "mov z3.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9923 // smmla z3.s, z9.b, z31.b\n" + ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" + "ld1rqb { z9.b }, p1/Z, [x24, #32]\n" + ".inst 0x450e9923 // smmla z3.s, z9.b, z14.b\n" + ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" + "mov z9.s, #0x0\n" + ".inst 0x451f9a49 // smmla z9.s, z18.b, z31.b\n" + "mov z31.s, #0x0\n" + ".inst 0x45069a5f // smmla z31.s, z18.b, z6.b\n" + "ld1rqb { z6.b }, p1/Z, [x24, #48]\n" + "ld1rqb { z18.b }, p1/Z, [x24, #64]\n" + ".inst 0x450e98c9 // smmla z9.s, z6.b, z14.b\n" + "fmul z14.s, z23.s, z5.s[1]\n" + ".inst 0x450298df // smmla z31.s, z6.b, z2.b\n" + "ld1rqb { z6.b }, p1/Z, [x24, #80]\n" + "fmul z2.s, z23.s, z5.s[2]\n" + "fmul z23.s, z23.s, z5.s[3]\n" + ".inst 0x451e9a43 // smmla z3.s, z18.b, z30.b\n" + ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x24, #96]\n" + ".inst 0x451e98c9 // smmla z9.s, z6.b, z30.b\n" + ".inst 0x451598df // smmla z31.s, z6.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x24, #112]\n" + "add x24, x24, #0x88\n" + ".inst 0x450498a3 // smmla z3.s, z5.b, z4.b\n" + ".inst 0x451198a7 // smmla z7.s, z5.b, z17.b\n" + ".inst 0x45049a49 // smmla z9.s, z18.b, z4.b\n" + ".inst 0x45119a5f // smmla z31.s, z18.b, z17.b\n" + "uzp1 z18.d, z3.d, z7.d\n" + "uzp2 z5.d, z3.d, z7.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp1 z6.d, z9.d, z31.d\n" + "uzp2 z9.d, z9.d, z31.d\n" + "scvtf z5.s, p1/m, z5.s\n" + "fmla z8.s, p1/M, z18.s, z22.s\n" + "scvtf z6.s, p1/m, z6.s\n" + "scvtf z9.s, p1/m, z9.s\n" + "fmla z29.s, p1/M, z5.s, z14.s\n" + "fmla z27.s, p1/M, z6.s, z2.s\n" + "fmla z10.s, p1/M, z9.s, z23.s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x10, x10, #0x8\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "st1w { z24.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z15.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z12.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z0.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z13.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z1.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z20.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z25.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z11.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z16.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z19.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z26.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z8.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z29.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z27.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z10.s }, p1, [x20]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x13, x13, #0x10\n" + "cmp x13, #0x10\n" + "mov %x[res_ptr], x9\n" + "madd %x[a_ptr], x20, x12, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x13, 9f\n" + "5:" // Row tail: Row loop + "add x25, %x[b_ptr], #0x10\n" + "mov x24, %x[nc]\n" + "add x23, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "mov z24.b, #0x0\n" + "mov z15.b, #0x0\n" + "add x28, %x[a_ptr], #0x8\n" + "mov x22, %x[nb]\n" + "mov z12.b, #0x0\n" + "mov z0.b, #0x0\n" + "7:" // Row tail: Block loop + "ld1b { z3.b }, p1/Z, [x25]\n" + "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n" + "mov z2.s, #0x0\n" + "mov z25.s, #0x0\n" + "ld1rqb { z26.b }, p1/Z, [x28]\n" + "ld1rqb { z21.b }, p1/Z, [x28, #16]\n" + "mov z27.s, #0x0\n" + "mov z19.s, #0x0\n" + "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n" + "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n" + "sub x21, x25, #0x10\n" + "sub x20, x28, #0x8\n" + "lsl z20.b, z3.b, #0x4\n" + "lsl z4.b, z6.b, #0x4\n" + "ld1rqb { z10.b }, p1/Z, [x28, #32]\n" + "ld1rqb { z23.b }, p1/Z, [x28, #48]\n" + "and z3.b, z3.b, #0xf0\n" + "and z6.b, z6.b, #0xf0\n" + "ld1rqb { z11.b }, p1/Z, [x28, #64]\n" + "ld1rqb { z7.b }, p1/Z, [x28, #80]\n" + "lsl z8.b, z29.b, #0x4\n" + "lsl z14.b, z16.b, #0x4\n" + "ld1rqb { z18.b }, p1/Z, [x28, #96]\n" + "ld1rqb { z30.b }, p1/Z, [x28, #112]\n" + ".inst 0x45149b42 // smmla z2.s, z26.b, z20.b\n" + ".inst 0x45049b59 // smmla z25.s, z26.b, z4.b\n" + "and z29.b, z29.b, #0xf0\n" + "ld1h { z17.s }, p1/Z, [x21]\n" + ".inst 0x45149abb // smmla z27.s, z21.b, z20.b\n" + ".inst 0x45049ab3 // smmla z19.s, z21.b, z4.b\n" + "and z16.b, z16.b, #0xf0\n" + "ld1h { z4.s }, p0/Z, [x20]\n" + "subs x22, x22, #0x1\n" + "add x28, x28, #0x88\n" + "fcvt z17.s, p1/m, z17.h\n" + "add x25, x25, #0x90\n" + ".inst 0x45089942 // smmla z2.s, z10.b, z8.b\n" + ".inst 0x450e9959 // smmla z25.s, z10.b, z14.b\n" + "fcvt z4.s, p1/m, z4.h\n" + ".inst 0x45089afb // smmla z27.s, z23.b, z8.b\n" + ".inst 0x450e9af3 // smmla z19.s, z23.b, z14.b\n" + "fscale z17.s, p1/m, z17.s, z28.s\n" + "mov z4.q, z4.q[0]\n" + ".inst 0x45039962 // smmla z2.s, z11.b, z3.b\n" + ".inst 0x45069979 // smmla z25.s, z11.b, z6.b\n" + "fmul z23.s, z17.s, z4.s[0]\n" + "fmul z9.s, z17.s, z4.s[1]\n" + "fmul z21.s, z17.s, z4.s[2]\n" + "fmul z4.s, z17.s, z4.s[3]\n" + ".inst 0x450398fb // smmla z27.s, z7.b, z3.b\n" + ".inst 0x450698f3 // smmla z19.s, z7.b, z6.b\n" + ".inst 0x451d9a42 // smmla z2.s, z18.b, z29.b\n" + ".inst 0x45109a59 // smmla z25.s, z18.b, z16.b\n" + ".inst 0x451d9bdb // smmla z27.s, z30.b, z29.b\n" + ".inst 0x45109bd3 // smmla z19.s, z30.b, z16.b\n" + "uzp1 z31.d, z2.d, z25.d\n" + "uzp2 z13.d, z2.d, z25.d\n" + "scvtf z31.s, p1/m, z31.s\n" + "uzp1 z17.d, z27.d, z19.d\n" + "uzp2 z18.d, z27.d, z19.d\n" + "scvtf z13.s, p1/m, z13.s\n" + "fmla z24.s, p1/M, z31.s, z23.s\n" + "scvtf z17.s, p1/m, z17.s\n" + "scvtf z18.s, p1/m, z18.s\n" + "fmla z15.s, p1/M, z13.s, z9.s\n" + "fmla z12.s, p1/M, z17.s, z21.s\n" + "fmla z0.s, p1/M, z18.s, z4.s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x13, #0x1\n" + "st1w { z24.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x13, #0x2\n" + "st1w { z15.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x13, #0x3\n" + "st1w { z12.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "st1w { z0.s }, p1, [x20]\n" + "8:" // Row tail: Accumulator store skip + "subs x24, x24, #0x8\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "bne 6b\n" + "subs x13, x13, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x12\n" + "mov %x[res_ptr], x23\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" + ); + return; + } +#endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) +#elif defined(__AVX2__) || defined(__AVX512F__) + { + const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; + const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; + int64_t b_nb = n / QK4_0; + int64_t y = 0; + // Mask to mask out nibbles from packed bytes + const __m256i m4b = _mm256_set1_epi8(0x0F); + const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); + // Lookup table to convert signed nibbles to signed bytes + __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + // Permute mask used for easier vector processing at later stages + __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); + int64_t xstart = 0; + int anr = nr - nr%16; // Used to align nr with boundary of 16 + #ifdef __AVX512F__ + int anc = nc - nc%16; // Used to align nc with boundary of 16 + // Mask to mask out nibbles from packed bytes expanded to 512 bit length + const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); + // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length + __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); + + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < anr / 4; y += 4) { + + const block_q8_0x4 * a_ptrs[4]; + + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; + } + + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { + + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + + // Master FP accumulators + __m512 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); + + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + + // Shuffle pattern two - right side input + + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + + // Process LHS in pairs of rows + for (int rp = 0; rp < 4; rp++) { + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); + __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); + __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); + __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); + __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); + + __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); + __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); + __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); + __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); + __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); + __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); + __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); + __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); + + // Shuffle pattern one - left side input + + const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m512i iacc_mat_00_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_01_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_10_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_11_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_00_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_01_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); + __m512i iacc_mat_10_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_11_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); + __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); + __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); + __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); + const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } + + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { + + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); + + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { + + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + + // Master FP accumulators + __m512 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); + + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + + // Shuffle pattern two - right side input + + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); + __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); + __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); + __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); + __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); + __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); + __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); + __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); + __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); + + __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); + __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); + __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); + __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); + __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); + __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); + __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); + __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); + + // Shuffle pattern one - left side input + + const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m512i iacc_mat_00_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_01_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_10_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_11_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_00_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_01_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); + __m512i iacc_mat_10_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_11_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); + __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); + __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); + __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); + const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); + + // Multiply with appropiate scales and accumulate + acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); + } + + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + if (anc != nc) { + xstart = anc/8; + y = 0; + } + #endif // __AVX512F__ + + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + + for (; y < anr / 4; y += 4) { + const block_q8_0x4 * a_ptrs[4]; + + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; + } + + // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = xstart; x < nc / 8; x++) { + + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulators + __m256 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + + // Shuffle pattern two - right side input + + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + + // Process LHS in groups of four + for (int rp = 0; rp < 4; rp++) { + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated into a 256 bit vector + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); + __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); + __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); + __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); + __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); + + // Shuffle pattern one - left side input + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m256i iacc_mat_00_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_01_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_10_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_11_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_00_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_01_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); + __m256i iacc_mat_10_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_11_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + // Straighten out to make 4 row vectors + __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); + __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); + __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); + __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } + + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { + + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); + + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + for (int64_t x = xstart; x < nc / 8; x++) { + + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulators + __m256 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + + // Shuffle pattern two - right side input + + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated into a 256 bit vector + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); + __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); + __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); + __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); + __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); + __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); + __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); + __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); + __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); + + // Shuffle pattern one - left side input + + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m256i iacc_mat_00_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_01_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_10_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_11_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_00_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_01_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); + __m256i iacc_mat_10_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_11_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); + __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); + __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); + __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); + + // Multiply with appropiate scales and accumulate + acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); + } + + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + return; + } +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + vfloat32m1_t sumf0 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf1 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf2 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf3 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + // vector version needs Zvfhmin extension + const float a_scales[4] = { + GGML_FP16_TO_FP32(a_ptr[l].d[0]), + GGML_FP16_TO_FP32(a_ptr[l].d[1]), + GGML_FP16_TO_FP32(a_ptr[l].d[2]), + GGML_FP16_TO_FP32(a_ptr[l].d[3]) + }; + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + + const int64_t A0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t A4 = *(const int64_t *)&a_ptr[l].qs[32]; + const int64_t A8 = *(const int64_t *)&a_ptr[l].qs[64]; + const int64_t Ac = *(const int64_t *)&a_ptr[l].qs[96]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l0; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A4, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A8, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ac, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l0 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l0)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[0], vl / 4); + sumf0 = __riscv_vfmacc_vv_f32m1(sumf0, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t A5 = *(const int64_t *)&a_ptr[l].qs[40]; + const int64_t A9 = *(const int64_t *)&a_ptr[l].qs[72]; + const int64_t Ad = *(const int64_t *)&a_ptr[l].qs[104]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l1; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A1, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A5, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A9, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ad, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l1 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l1)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[1], vl / 4); + sumf1 = __riscv_vfmacc_vv_f32m1(sumf1, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t A6 = *(const int64_t *)&a_ptr[l].qs[48]; + const int64_t Aa = *(const int64_t *)&a_ptr[l].qs[80]; + const int64_t Ae = *(const int64_t *)&a_ptr[l].qs[112]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l2; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A2, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A6, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Aa, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ae, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l2 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l2)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[2], vl / 4); + sumf2 = __riscv_vfmacc_vv_f32m1(sumf2, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A3 = *(const int64_t *)&a_ptr[l].qs[24]; + const int64_t A7 = *(const int64_t *)&a_ptr[l].qs[56]; + const int64_t Ab = *(const int64_t *)&a_ptr[l].qs[88]; + const int64_t Af = *(const int64_t *)&a_ptr[l].qs[120]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l3; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A3, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A7, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ab, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Af, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l3 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l3)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[3], vl / 4); + sumf3 = __riscv_vfmacc_vv_f32m1(sumf3, tmp1, b_scales_vec, vl / 4); + } + } + __riscv_vse32_v_f32m1(&s[(y * 4 + 0) * bs + x * ncols_interleaved], sumf0, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 1) * bs + x * ncols_interleaved], sumf1, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 2) * bs + x * ncols_interleaved], sumf2, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 3) * bs + x * ncols_interleaved], sumf3, vl / 4); + } + } + + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) + float sumf[4][8]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +// FIXME: this code is duplicated from ggml-aarch64.c +static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { + block_q4_0x4 out; + + for (int i = 0; i < 4; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_0 * 2 / blck_size_interleave; + + if (blck_size_interleave == 8) { + const uint64_t xor_mask = 0x8888888888888888ULL; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + // Using memcpy to avoid unaligned memory accesses + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + const uint32_t xor_mask = 0x88888888; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint32_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +// interleave 8 block_q4_0s in blocks of blck_size_interleave +// returns an interleaved block_q4_0x8 +// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks +// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave +static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) { + block_q4_0x8 out; + + for (int i = 0; i < 8; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_0 * 4 / blck_size_interleave; + const uint64_t xor_mask = 0x8888888888888888ULL; + + for (int i = 0; i < end; ++i) { + int src_id = i % 8; + int src_offset = (i / 8) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + + return out; +} + +static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_0); + GGML_ASSERT(interleave_block == 4 || interleave_block == 8); + + block_q4_0x4 * dst = (block_q4_0x4 *)t->data; + const block_q4_0 * src = (const block_q4_0 *)data; + block_q4_0 dst_tmp[4]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 4; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_0x4(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_0); + GGML_ASSERT(interleave_block == 8); + + block_q4_0x8 * dst = (block_q4_0x8*)t->data; + const block_q4_0 * src = (const block_q4_0*) data; + block_q4_0 dst_tmp[8]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 8; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++ ) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_0x8(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +// Prepare for optimized kernels if applicable +void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * restrict data, size_t data_size) { + if (cur->type == repack_type) { + memcpy(cur->data, data, data_size); + return; + } + + GGML_ASSERT(cur->type == GGML_TYPE_Q4_0); + + switch (repack_type) { + case GGML_TYPE_Q4_0_8_8: + repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_8: + repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_4: + repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size); + break; + default: + GGML_ABORT("Unsupported type"); + } +} + +enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur) { + if (cur->type == GGML_TYPE_Q4_0) { + // TODO: enable for AVX2 - currently disabled due to bad gemv performance + if (/* ggml_cpu_has_avx2() || */ (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) { + return GGML_TYPE_Q4_0_8_8; + } + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + return GGML_TYPE_Q4_0_4_8; + } + if (ggml_cpu_has_neon()) { + return GGML_TYPE_Q4_0_4_4; + } + } + + return cur->type; +} diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.h b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h new file mode 100644 index 0000000000..53b30c1dd2 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h @@ -0,0 +1,30 @@ +#pragma once + +#include "ggml.h" + +// GGML internal header + +#ifdef __cplusplus +extern "C" { +#endif + +// Quantization +void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave); + +// GEMV +void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); + +// GEMM +void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); + +void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * data, size_t data_size); +enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur); + +#ifdef __cplusplus +} +#endif + diff --git a/ggml/src/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h similarity index 52% rename from ggml/src/ggml-cpu-impl.h rename to ggml/src/ggml-cpu/ggml-cpu-impl.h index 5b45155b02..27a530b227 100644 --- a/ggml/src/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -27,80 +27,6 @@ extern "C" { #endif -/** - * Converts brain16 to float32. - * - * The bfloat16 floating point format has the following structure: - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───┐ - * 0b0000000000000000 brain16 - * - * Since bf16 has the same number of exponent bits as a 32bit float, - * encoding and decoding numbers becomes relatively straightforward. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───────────────────┐ - * 0b00000000000000000000000000000000 IEEE binary32 - * - * For comparison, the standard fp16 format has fewer exponent bits. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌─┴─┐┌─┴──────┐ - * 0b0000000000000000 IEEE binary16 - * - * @see IEEE 754-2008 - */ -static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { - union { - float f; - uint32_t i; - } u; - u.i = (uint32_t)h.bits << 16; - return u.f; -} - -/** - * Converts float32 to brain16. - * - * This is binary identical with Google Brain float conversion. - * Floats shall round to nearest even, and NANs shall be quiet. - * Subnormals aren't flushed to zero, except perhaps when used. - * This code should vectorize nicely if using modern compilers. - */ -static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { - ggml_bf16_t h; - union { - float f; - uint32_t i; - } u; - u.f = s; - if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ - h.bits = (u.i >> 16) | 64; /* force to quiet */ - return h; - } - h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; - return h; -} - -#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) -#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) - // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) #ifndef __FMA__ @@ -388,28 +314,6 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) #endif // defined(__ARM_NEON) -#if defined(__ARM_NEON) && !defined(_MSC_VER) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - ggml_fp16_internal_t tmp; - memcpy(&tmp, &h, sizeof(ggml_fp16_t)); - return (float)tmp; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - ggml_fp16_t res; - ggml_fp16_internal_t tmp = f; - memcpy(&res, &tmp, sizeof(ggml_fp16_t)); - return res; -} - -#else - #ifdef __wasm_simd128__ #include #else @@ -462,153 +366,6 @@ static __m256 __lasx_xvreplfr2vr_s(float val) { } #endif -#ifdef __F16C__ - -#ifdef _MSC_VER -#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) -#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) -#else -#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) -#endif - -#elif defined(__POWER9_VECTOR__) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) -/* the inline asm below is about 12% faster than the lookup method */ -#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - register float f; - register double d; - __asm__( - "mtfprd %0,%2\n" - "xscvhpdp %0,%0\n" - "frsp %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=f"(f): - /* in */ "r"(h)); - return f; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - register double d; - register ggml_fp16_t r; - __asm__( /* xscvdphp can work on double or single precision */ - "xscvdphp %0,%2\n" - "mffprd %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=r"(r): - /* in */ "f"(f)); - return r; -} - -#else - -// FP16 <-> FP32 -// ref: https://github.com/Maratyszcza/FP16 - -static inline float fp32_from_bits(uint32_t w) { - union { - uint32_t as_bits; - float as_value; - } fp32; - fp32.as_bits = w; - return fp32.as_value; -} - -static inline uint32_t fp32_to_bits(float f) { - union { - float as_value; - uint32_t as_bits; - } fp32; - fp32.as_value = f; - return fp32.as_bits; -} - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - const uint32_t w = (uint32_t) h << 16; - const uint32_t sign = w & UINT32_C(0x80000000); - const uint32_t two_w = w + w; - - const uint32_t exp_offset = UINT32_C(0xE0) << 23; -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float exp_scale = 0x1.0p-112f; -#else - const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); -#endif - const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; - - const uint32_t magic_mask = UINT32_C(126) << 23; - const float magic_bias = 0.5f; - const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; - - const uint32_t denormalized_cutoff = UINT32_C(1) << 27; - const uint32_t result = sign | - (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); - return fp32_from_bits(result); -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float scale_to_inf = 0x1.0p+112f; - const float scale_to_zero = 0x1.0p-110f; -#else - const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); - const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); -#endif - float base = (fabsf(f) * scale_to_inf) * scale_to_zero; - - const uint32_t w = fp32_to_bits(f); - const uint32_t shl1_w = w + w; - const uint32_t sign = w & UINT32_C(0x80000000); - uint32_t bias = shl1_w & UINT32_C(0xFF000000); - if (bias < UINT32_C(0x71000000)) { - bias = UINT32_C(0x71000000); - } - - base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; - const uint32_t bits = fp32_to_bits(base); - const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); - const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); - const uint32_t nonsign = exp_bits + mantissa_bits; - return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); -} - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#endif // __F16C__ - -#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) - -#ifdef __ARM_FEATURE_SVE -#include -#endif // __ARM_FEATURE_SVE - -// precomputed f32 table for f16 (256 KB) -// defined in ggml.c, initialized in ggml_init() -extern float ggml_table_f32_f16[1 << 16]; - -// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, -// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. -// This is also true for POWER9. -#if !defined(GGML_FP16_TO_FP32) -inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { - uint16_t s; - memcpy(&s, &f, sizeof(uint16_t)); - return ggml_table_f32_f16[s]; -} - -#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) -#endif - -#if !defined(GGML_FP32_TO_FP16) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) -#endif - #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c new file mode 100644 index 0000000000..f0e276b698 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -0,0 +1,10822 @@ +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + +#include "ggml-quants.h" +#include "ggml-cpu-quants.h" +#include "ggml-impl.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu.h" + +#include +#include +#include +#include +#include // for qsort +#include // for GGML_ASSERT + +#define GROUP_MAX_EPS 1e-15f +#define GROUP_MAX_EPS_IQ3_XXS 1e-8f +#define GROUP_MAX_EPS_IQ2_S 1e-8f +#define GROUP_MAX_EPS_IQ1_M 1e-7f +#define GROUP_MAX_EPS_IQ1_S 1e-12f + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid warnings for hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) +#endif + +#define UNUSED GGML_UNUSED + +// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = _mm_sign_epi8(x, x); + // Sign the values of the y vectors + const __m128i sy = _mm_sign_epi8(y, x); + // Perform multiplication and create 16-bit values + const __m128i dot = _mm_maddubs_epi16(ax, sy); + const __m128i ones = _mm_set1_epi16(1); + return _mm_madd_epi16(ones, dot); +} + +#if __AVX__ || __AVX2__ || __AVX512F__ +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = _mm256_extractf128_ps(x, 1); + res = _mm_add_ps(res, _mm256_castps256_ps128(x)); + res = _mm_add_ps(res, _mm_movehl_ps(res, res)); + res = _mm_add_ss(res, _mm_movehdup_ps(res)); + return _mm_cvtss_f32(res); +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); + const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); + const __m128i sum64 = _mm_add_epi32(hi64, sum128); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + const __m128i hi64 = _mm_unpackhi_epi64(a, a); + const __m128i sum64 = _mm_add_epi32(hi64, a); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +#if defined(__AVX2__) || defined(__AVX512F__) +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = _mm256_set_epi64x( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); + const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytes = _mm256_or_si256(bytes, bit_mask); + return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8( 0xF ); + return _mm256_and_si256(lowMask, bytes); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + const __m256i summed_pairs = _mm256_madd_epi16(ones, x); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_float(dot); +#endif +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { +#if __AVXVNNIINT8__ + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); + return mul_sum_us8_pairs_float(ax, sy); +#endif +} + +static inline __m128i packNibbles( __m256i bytes ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh +#if __AVX512F__ + const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 + bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh + return _mm256_cvtepi16_epi8(bytes); // abcd_efgh +#else + const __m256i lowByte = _mm256_set1_epi16( 0xFF ); + __m256i high = _mm256_andnot_si256( lowByte, bytes ); + __m256i low = _mm256_and_si256( lowByte, bytes ); + high = _mm256_srli_epi16( high, 4 ); + bytes = _mm256_or_si256( low, high ); + + // Compress uint16_t lanes into bytes + __m128i r0 = _mm256_castsi256_si128( bytes ); + __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); + return _mm_packus_epi16( r0, r1 ); +#endif +} +#elif defined(__AVX__) +static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m128i lowByte = _mm_set1_epi16( 0xFF ); + __m128i high = _mm_andnot_si128( lowByte, bytes1 ); + __m128i low = _mm_and_si128( lowByte, bytes1 ); + high = _mm_srli_epi16( high, 4 ); + bytes1 = _mm_or_si128( low, high ); + high = _mm_andnot_si128( lowByte, bytes2 ); + low = _mm_and_si128( lowByte, bytes2 ); + high = _mm_srli_epi16( high, 4 ); + bytes2 = _mm_or_si128( low, high ); + + return _mm_packus_epi16( bytes1, bytes2); +} + +static inline __m128i mul_add_epi8_sse(const __m128i x, const __m128i y) { + const __m128i ax = _mm_sign_epi8(x, x); + const __m128i sy = _mm_sign_epi8(y, x); + return _mm_maddubs_epi16(ax, sy); +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); + const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); + __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); + __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); + const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytesl = _mm_or_si128(bytesl, bit_mask); + bytesh = _mm_or_si128(bytesh, bit_mask); + bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); + bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); + return MM256_SET_M128I(bytesh, bytesl); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + // Load 16 bytes from memory + __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); + __m128i tmph = _mm_srli_epi16(tmpl, 4); + const __m128i lowMask = _mm_set1_epi8(0xF); + tmpl = _mm_and_si128(lowMask, tmpl); + tmph = _mm_and_si128(lowMask, tmph); + return MM256_SET_M128I(tmph, tmpl); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { + const __m128i ones = _mm_set1_epi16(1); + const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); + const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); + const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + const __m128i axl = _mm256_castsi256_si128(ax); + const __m128i axh = _mm256_extractf128_si256(ax, 1); + const __m128i syl = _mm256_castsi256_si128(sy); + const __m128i syh = _mm256_extractf128_si256(sy, 1); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + const __m128i xl = _mm256_castsi256_si128(x); + const __m128i xh = _mm256_extractf128_si256(x, 1); + const __m128i yl = _mm256_castsi256_si128(y); + const __m128i yh = _mm256_extractf128_si256(y, 1); + // Get absolute values of x vectors + const __m128i axl = _mm_sign_epi8(xl, xl); + const __m128i axh = _mm_sign_epi8(xh, xh); + // Sign the values of the y vectors + const __m128i syl = _mm_sign_epi8(yl, xl); + const __m128i syh = _mm_sign_epi8(yh, xh); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +// larger version of mul_sum_i8_pairs_float where x and y are each represented by four 128-bit vectors +static inline __m256 mul_sum_i8_quad_float(const __m128i x_1_0, const __m128i x_1_1, const __m128i x_2_0, const __m128i x_2_1, + const __m128i y_1_0, const __m128i y_1_1, const __m128i y_2_0, const __m128i y_2_1) { + const __m128i mone = _mm_set1_epi16(1); + + const __m128i p16_1_0 = mul_add_epi8_sse(x_1_0, y_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(x_1_1, y_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(x_2_0, y_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(x_2_1, y_2_1); + const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); + const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); + const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); + const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); + const __m128i p_1 = _mm_add_epi32(p_1_0, p_1_1); + const __m128i p_2 = _mm_add_epi32(p_2_0, p_2_1); + return _mm256_cvtepi32_ps(MM256_SET_M128I(p_2, p_1)); +} + +// quad fp16 delta calculation +static inline __m256 quad_fp16_delta_float(const float x0, const float y0, const float x1, const float y1) { + // GGML_FP16_TO_FP32 is faster than Intel F16C + return _mm256_set_m128(_mm_set1_ps(GGML_FP16_TO_FP32(x1) * GGML_FP16_TO_FP32(y1)), + _mm_set1_ps(GGML_FP16_TO_FP32(x0) * GGML_FP16_TO_FP32(y0))); +} +#endif +#elif defined(__SSSE3__) +// horizontally add 4x4 floats +static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { + __m128 res_0 =_mm_hadd_ps(a, b); + __m128 res_1 =_mm_hadd_ps(c, d); + __m128 res =_mm_hadd_ps(res_0, res_1); + res =_mm_hadd_ps(res, res); + res =_mm_hadd_ps(res, res); + + return _mm_cvtss_f32(res); +} +#endif // __AVX__ || __AVX2__ || __AVX512F__ +#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) + +#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) +#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s +#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) +#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) +#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) +#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) +#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) +#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) +#define B8(c,s ) B7(c,s, c), B7(c,s, s) + +// precomputed tables for expanding 8bits to 8 bytes: +static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 +static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 +#endif + +#if defined(__loongarch_asx) + +#ifdef __clang__ +#define VREGS_PREFIX "$vr" +#define XREGS_PREFIX "$xr" +#else // GCC +#define VREGS_PREFIX "$f" +#define XREGS_PREFIX "$f" +#endif +#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" +// Convert __m128i to __m256i +static inline __m256i ____m256i(__m128i in) { + __m256i out = __lasx_xvldi(0); + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX"\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "+f" (out) : [in] "f" (in) + ); + return out; +} +// Convert two __m128i to __m256i +static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { + __m256i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".ifnc %[out], %[hi] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" + " xvori.b $xr\\i, $xr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out), [hi] "+f" (inhi) + : [lo] "f" (inlo) + ); + return out; +} +// Convert __m256i low part to __m128i +static inline __m128i lasx_extracti128_lo(__m256i in) { + __m128i out; + __asm__ volatile ( + ".ifnc %[out], %[in] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " vori.b $vr\\i, $vr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} +// Convert __m256i high part to __m128i +static inline __m128i lasx_extracti128_hi(__m256i in) { + __m128i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} + +static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { + v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; + return (__m256i)__ret; +} + +static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { + v4i32 __ret = {d, c, b, a}; + return (__m128i)__ret; +} + +static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { + v4i64 __ret = {d, c, b, a}; + return (__m256i)__ret; +} + +static __m256i lasx_insertf128( __m128i x, __m128i y) { + return lasx_set_q(x, y); +} + +static __m128i lsx_shuffle_b(__m128i a, __m128i b) { + __m128i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lsx_vreplgr2vr_b(f); + zero = __lsx_vldi(0); + tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones + return __lsx_vshuf_b(a, zero, tmp2); +} + +static __m256i lasx_shuffle_b(__m256i a, __m256i b) { + __m256i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lasx_xvreplgr2vr_b(f); + zero = __lasx_xvldi(0); + tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones + return __lasx_xvshuf_b(a, zero, tmp2); +} + +static __m256i lasx_extu8_16(__m128i a) { + __m128i zero = __lsx_vldi(0); + __m128i vlo = __lsx_vilvl_b(zero, a); + __m128i vhi = __lsx_vilvh_b(zero, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext8_16(__m128i a) { + __m128i sign = __lsx_vslti_b(a, 0); + __m128i vlo = __lsx_vilvl_b(sign, a); + __m128i vhi = __lsx_vilvh_b(sign, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext16_32(__m128i a) { + __m256i tmp1; + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); + return tmp1; +} + +static __m128i lasx_extracti128( __m256i a, int pos) { + __m128i ret; + if( pos == 0) + { + ret = lasx_extracti128_lo(a); + } else { + ret = lasx_extracti128_hi(a); + } + return ret; +} + +static __m128 lasx_extractf128( __m256 a, int pos) { + __m128 ret; + if( pos == 0) + { + ret = (__m128)lasx_extracti128_lo((__m256i)a); + } else { + ret = (__m128)lasx_extracti128_hi((__m256i)a); + } + return ret; +} + +static __m128i lsx_hadd_h(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_h(b, a); + __m128i tmp2 = __lsx_vpickod_h(b, a); + return __lsx_vadd_h(tmp1, tmp2); +} + +static __m128i lsx_hadd_w(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_w(b, a); + __m128i tmp2 = __lsx_vpickod_w(b, a); + return __lsx_vadd_w(tmp1, tmp2); +} + +static __m128 lsx_hadd_s(__m128 a, __m128 b) { + __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); + __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); + + return __lsx_vfadd_s(tmp1, tmp2); +} + +static __m256i lasx_maddubs_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_h_b(a, b); + tmp2 = __lasx_xvmulwod_h_b(a, b); + return __lasx_xvsadd_h(tmp1, tmp2); +} + +static __m256i lasx_madd_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_w_h(a, b); + tmp2 = __lasx_xvmulwod_w_h(a, b); + return __lasx_xvadd_w(tmp1, tmp2); +} + +static __m256i lasx_packs_w(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_w(a, 15); + tmp1 = __lasx_xvsat_w(b, 15); + return __lasx_xvpickev_h(tmp1, tmp); +} + +static __m256i lasx_packs_h(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_h(a, 7); + tmp1 = __lasx_xvsat_h(b, 7); + return __lasx_xvpickev_b(tmp1, tmp); +} + +static __m128i lsx_packs_w(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_w(a, 15); + tmp1 = __lsx_vsat_w(b, 15); + return __lsx_vpickev_h(tmp1, tmp); +} + +static __m128i lsx_packs_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_h(a, 7); + tmp1 = __lsx_vsat_h(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + +static __m128i lsx_packus_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_hu(a, 7); + tmp1 = __lsx_vsat_hu(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + + +static __m128i lsx_maddubs_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_h_b(a, b); + tmp2 = __lsx_vmulwod_h_b(a, b); + return __lsx_vsadd_h(tmp1, tmp2); +} + +static __m128i lsx_madd_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_w_h(a, b); + tmp2 = __lsx_vmulwod_w_h(a, b); + return __lsx_vadd_w(tmp1, tmp2); +} + +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = __lsx_vsigncov_b(x, x); + // Sign the values of the y vectors + const __m128i sy = __lsx_vsigncov_b(x, y); + // Perform multiplication and create 16-bit values + const __m128i dot = lsx_maddubs_h(ax, sy); + const __m128i ones = __lsx_vreplgr2vr_h(1); + return lsx_madd_h(ones, dot); +} + +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = lasx_extractf128(x, 1); + ft_union tmp; + res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); + tmp.i = __lsx_vpickve2gr_w(res, 0); + return tmp.f; +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + + __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); + __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); + + __m128i tmp1_128 = lasx_extracti128_lo(tmp1); + __m128i tmp2_128 = lasx_extracti128_lo(tmp2); + + __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); + + __m128i ev = __lsx_vpickev_w(sum128, sum128); + __m128i od = __lsx_vpickod_w(sum128, sum128); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + __m128i ev = __lsx_vpickev_w(a, a); + __m128i od = __lsx_vpickod_w(a, a); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = lasx_set_d( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + + __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); + const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); + bytes = __lasx_xvor_v(bytes, bit_mask); + return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); + __m128i hi = __lsx_vsrli_h(lo, 4); + return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + __m256i v = __lasx_xvpackod_h(x, x); + __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); + return __lasx_xvffint_s_w(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + // Perform multiplication and create 16-bit values + const __m256i dot = lasx_maddubs_h(ax, sy); + return sum_i16_pairs_float(dot); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + + // Get absolute values of x vectors + const __m256i ax = __lasx_xvsigncov_b(x, x); + // Sign the values of the y vectors + const __m256i sy = __lasx_xvsigncov_b(x, y); + + return mul_sum_us8_pairs_float(ax, sy); +} + +static inline __m128i packNibbles( __m256i bytes ) { + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); + __m256i high = __lasx_xvandn_v(lowByte, bytes); + __m256i low = __lasx_xvand_v(lowByte, bytes); + high = __lasx_xvsrli_h(high, 4); + bytes = __lasx_xvor_v(low, high); + // Compress uint16_t lanes into bytes + __m128i *r0 = (__m128i *)&bytes; + __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); + __m128i *r1 = (__m128i *)&tmp_h128; + + __m128i zero = __lsx_vldi(0); + __m128i tmp, tmp2, tmp3; + + tmp = __lsx_vmax_h(zero, *r0); + tmp2 = __lsx_vsat_hu(tmp, 7); + + tmp = __lsx_vmax_h(zero, *r1); + tmp3 = __lsx_vsat_hu(tmp, 7); + return __lsx_vpickev_b(tmp3, tmp2); +} +#endif //__loongarch_asx + +void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q4_0_ref(x, y, k); +} + +void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q4_1_ref(x, y, k); +} + +void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q5_0_ref(x, y, k); +} + +void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q5_1_ref(x, y, k); +} + +void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + } + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + } + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = maxScalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_0); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + } + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union fi; + __m256 v0 = (__m256)__lasx_xvld( x , 0); + __m256 v1 = (__m256)__lasx_xvld( x , 32); + __m256 v2 = (__m256)__lasx_xvld( x , 64); + __m256 v3 = (__m256)__lasx_xvld( x , 96); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); + fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = fi.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128( i0, 0 ); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + + } +#else + GGML_UNUSED(nb); + // scalar + quantize_row_q8_0_ref(x, y, k); +#endif +} + +void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + int32x4_t accv = vdupq_n_s32(0); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + + accv = vaddq_s32(accv, vi); + } + + y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv)); + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + v128_t accv = wasm_i32x4_splat(0); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + + accv = wasm_i32x4_add(accv, vi); + } + + y[i].s = GGML_FP32_TO_FP16( + d * (wasm_i32x4_extract_lane(accv, 0) + + wasm_i32x4_extract_lane(accv, 1) + + wasm_i32x4_extract_lane(accv, 2) + + wasm_i32x4_extract_lane(accv, 3))); + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float max_scalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Compute the sum of the quants and set y[i].s + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)))); + + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); + const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1))); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_1); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + + // compute sum for y[i].s + vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); + vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); + + // set y[i].s + int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); + y[i].s = GGML_FP32_TO_FP16(sum*d); + } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + vector int accv = vec_splats(0); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + + accv = vec_add(accv, vi[j]); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + + accv = vec_add(accv, vec_sld(accv, accv, 4)); + accv = vec_add(accv, vec_sld(accv, accv, 8)); + y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + } + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union ft; + __m256 v0 = (__m256)__lasx_xvld( x , 0 ); + __m256 v1 = (__m256)__lasx_xvld( x , 32 ); + __m256 v2 = (__m256)__lasx_xvld( x , 64 ); + __m256 v3 = (__m256)__lasx_xvld( x , 96 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); + ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = ft.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = __lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128(i0, 0); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0 ); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); + const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + } +#else + GGML_UNUSED(nb); + // scalar + quantize_row_q8_1_ref(x, y, k); +#endif +} + +// +// 2-6 bit quantization in super-blocks +// + +// +// ===================== Helper functions +// +static inline int nearest_int(float fval) { + assert(fabsf(fval) <= 4194303.f); + float val = fval + 12582912.f; + int i; memcpy(&i, &val, sizeof(int)); + return (i & 0x007fffff) - 0x00400000; +} + +static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type, + const float * restrict qw) { + float max = 0; + float amax = 0; + for (int i = 0; i < n; ++i) { + float ax = fabsf(x[i]); + if (ax > amax) { amax = ax; max = x[i]; } + } + if (amax < GROUP_MAX_EPS) { // all zero + for (int i = 0; i < n; ++i) { + L[i] = 0; + } + return 0.f; + } + float iscale = -nmax / max; + if (rmse_type == 0) { + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + L[i] = nmax + MAX(-nmax, MIN(nmax-1, l)); + } + return 1/iscale; + } + bool return_early = false; + if (rmse_type < 0) { + rmse_type = -rmse_type; + return_early = true; + } + float sumlx = 0; + float suml2 = 0; +#ifdef HAVE_BUGGY_APPLE_LINKER + // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7 + for (volatile int i = 0; i < n; ++i) { +#else + for (int i = 0; i < n; ++i) { +#endif + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l + nmax; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + float scale = suml2 ? sumlx/suml2 : 0.0f; + if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale; + float best = scale * sumlx; + for (int is = -9; is <= 9; ++is) { + if (is == 0) { + continue; + } + iscale = -(nmax + 0.1f*is) / max; + sumlx = suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + if (suml2 > 0 && sumlx*sumlx > best*suml2) { + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + L[i] = nmax + MAX(-nmax, MIN(nmax-1, l)); + } + scale = sumlx/suml2; best = scale*sumlx; + } + } + return scale; +} + +static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, bool do_rmse) { + float max = 0; + float amax = 0; + for (int i = 0; i < n; ++i) { + float ax = fabsf(x[i]); + if (ax > amax) { amax = ax; max = x[i]; } + } + if (amax < GROUP_MAX_EPS) { // all zero + for (int i = 0; i < n; ++i) { L[i] = 0; } + return 0.f; + } + float iscale = -nmax / max; + if (do_rmse) { + float sumlx = 0; + float suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l; + float w = x[i]*x[i]; + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + for (int itry = 0; itry < 5; ++itry) { + int n_changed = 0; + for (int i = 0; i < n; ++i) { + float w = x[i]*x[i]; + float slx = sumlx - w*x[i]*L[i]; + if (slx > 0) { + float sl2 = suml2 - w*L[i]*L[i]; + int new_l = nearest_int(x[i] * sl2 / slx); + new_l = MAX(-nmax, MIN(nmax-1, new_l)); + if (new_l != L[i]) { + slx += w*x[i]*new_l; + sl2 += w*new_l*new_l; + if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) { + L[i] = new_l; sumlx = slx; suml2 = sl2; + ++n_changed; + } + } + } + } + if (!n_changed) { + break; + } + } + for (int i = 0; i < n; ++i) { + L[i] += nmax; + } + return sumlx / suml2; + } + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l + nmax; + } + return 1/iscale; +} + +static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min, + int ntry, float alpha) { + float min = x[0]; + float max = x[0]; + for (int i = 1; i < n; ++i) { + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + } + if (max == min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = 0; + return 0.f; + } + if (min > 0) min = 0; + float iscale = nmax/(max - min); + float scale = 1/iscale; + for (int itry = 0; itry < ntry; ++itry) { + float sumlx = 0; int suml2 = 0; + bool did_change = false; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + if (l != L[i]) { + L[i] = l; + did_change = true; + } + sumlx += (x[i] - min)*l; + suml2 += l*l; + } + scale = sumlx/suml2; + float sum = 0; + for (int i = 0; i < n; ++i) { + sum += x[i] - scale*L[i]; + } + min = alpha*min + (1 - alpha)*sum/n; + if (min > 0) min = 0; + iscale = 1/scale; + if (!did_change) break; + } + *the_min = -min; + return scale; +} + +static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights, + uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, + float rmin, float rdelta, int nstep, bool use_mad) { + float min = x[0]; + float max = x[0]; + float sum_w = weights[0]; + float sum_x = sum_w * x[0]; +#ifdef HAVE_BUGGY_APPLE_LINKER + // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7 + for (volatile int i = 1; i < n; ++i) { +#else + for (int i = 1; i < n; ++i) { +#endif + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + float w = weights[i]; + sum_w += w; + sum_x += w * x[i]; + } + if (min > 0) min = 0; + if (max == min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = -min; + return 0.f; + } + float iscale = nmax/(max - min); + float scale = 1/iscale; + float best_mad = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + L[i] = MAX(0, MIN(nmax, l)); + float diff = scale * L[i] + min - x[i]; + diff = use_mad ? fabsf(diff) : diff * diff; + float w = weights[i]; + best_mad += w * diff; + } + if (nstep < 1) { + *the_min = -min; + return scale; + } + for (int is = 0; is <= nstep; ++is) { + iscale = (rmin + rdelta*is + nmax)/(max - min); + float sum_l = 0, sum_l2 = 0, sum_xl = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + Laux[i] = l; + float w = weights[i]; + sum_l += w*l; + sum_l2 += w*l*l; + sum_xl += w*l*x[i]; + } + float D = sum_w * sum_l2 - sum_l * sum_l; + if (D > 0) { + float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D; + float this_min = (sum_l2 * sum_x - sum_l * sum_xl)/D; + if (this_min > 0) { + this_min = 0; + this_scale = sum_xl / sum_l2; + } + float mad = 0; + for (int i = 0; i < n; ++i) { + float diff = this_scale * Laux[i] + this_min - x[i]; + diff = use_mad ? fabsf(diff) : diff * diff; + float w = weights[i]; + mad += w * diff; + } + if (mad < best_mad) { + for (int i = 0; i < n; ++i) { + L[i] = Laux[i]; + } + best_mad = mad; + scale = this_scale; + min = this_min; + } + } + } + *the_min = -min; + return scale; +} + +static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { + if (j < 4) { + *d = q[j] & 63; *m = q[j + 4] & 63; + } else { + *d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} + +//========================- 2-bit (de)-quantization + +void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) { + quantize_row_q2_K_ref(x, vy, k); +} + +//========================= 3-bit (de)-quantization + +void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { + quantize_row_q3_K_ref(x, vy, k); +} + +// ====================== 4-bit (de)-quantization + +void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q4_K * restrict y = vy; + quantize_row_q4_K_ref(x, y, k); +} + +// ====================== 5-bit (de)-quantization + +void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q5_K * restrict y = vy; + quantize_row_q5_K_ref(x, y, k); +} + +// ====================== 6-bit (de)-quantization + +void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q6_K * restrict y = vy; + quantize_row_q6_K_ref(x, y, k); +} + +// ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs) + +void quantize_row_tq1_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_tq1_0 * restrict y = vy; + quantize_row_tq1_0_ref(x, y, k); +} + +void quantize_row_tq2_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_tq2_0 * restrict y = vy; + quantize_row_tq2_0_ref(x, y, k); +} + +static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +//===================================== Q8_K ============================================== + +void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q8_K_ref(x, y, k); +} + +//===================================== Dot products ================================= + +// +// Helper functions +// +#if __AVX__ || __AVX2__ || __AVX512F__ + +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return _mm256_loadu_si256((const __m256i*)k_shuffle + i); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return _mm256_loadu_si256((const __m256i*)k_shuffle + i); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return _mm_loadu_si128((const __m128i*)k_shuffle + i); +} +#elif defined(__loongarch_asx) +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return __lsx_vld((const __m128i*)k_shuffle + i, 0); +} +#endif + +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_0 * restrict vx0 = vx; + const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_0 * restrict b_x0 = &vx0[i]; + const block_q4_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t x0_l = vsubq_s8(v0_0l, s8b); + const int8x16_t x0_h = vsubq_s8(v0_0h, s8b); + const int8x16_t x1_l = vsubq_s8(v0_1l, s8b); + const int8x16_t x1_h = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif + + int ib = 0; + float sumf = 0; + +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + const int vector_length = ggml_cpu_get_sve_cnt()*8; + + // VLA Implementation using switch case + switch (vector_length) { + case 128: + { + // predicate for activating higher lanes for 4 float32 elements + const svbool_t ph4 = svptrue_pat_b32(SV_VL4); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx0r, 0x0F)); + const svint8_t qx0h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx0r, 0x04)); + const svint8_t qx1l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx1r, 0x0F)); + const svint8_t qx1h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx1r, 0x04)); + + // sub 8 + const svint8_t qx0ls = svsub_n_s8_x(svptrue_b8(), qx0h, 8); + const svint8_t qx0hs = svsub_n_s8_x(svptrue_b8(), qx0l, 8); + const svint8_t qx1ls = svsub_n_s8_x(svptrue_b8(), qx1h, 8); + const svint8_t qx1hs = svsub_n_s8_x(svptrue_b8(), qx1l, 8); + + // load y + const svint8_t qy0h = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy0l = svld1_s8(svptrue_b8(), y0->qs + 16); + const svint8_t qy1h = svld1_s8(svptrue_b8(), y1->qs); + const svint8_t qy1l = svld1_s8(svptrue_b8(), y1->qs + 16); + + // dot product + sumv0 = svmla_n_f32_x(ph4, sumv0, svcvt_f32_s32_x(ph4, svadd_x(ph4, + svdot_s32(svdup_n_s32(0), qx0ls, qy0l), + svdot_s32(svdup_n_s32(0), qx0hs, qy0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(ph4, sumv1, svcvt_f32_s32_x(ph4, svadd_x(ph4, + svdot_s32(svdup_n_s32(0), qx1ls, qy1l), + svdot_s32(svdup_n_s32(0), qx1hs, qy1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 256: + { + // predicate for activating higher lanes for 16 int8 elements + const svbool_t ph16 = svptrue_pat_b8(SV_VL16); + // predicate for activating lower lanes for 16 int8 elements + const svbool_t pl16 = svnot_b_z(svptrue_b8(), ph16); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 512: + { + // predicate for activating higher lanes for 32 int8 elements + const svbool_t ph32 = svptrue_pat_b8(SV_VL32); + + // predicate for activating higher lanes for 16 int8 elements + const svbool_t ph16 = svptrue_pat_b8(SV_VL16); + // predicate for activating lower lanes for 16 int8 elements from first 32 int8 activated lanes + const svbool_t pl16 = svnot_b_z(ph32, ph16); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(ph32, x0->qs); + const svuint8_t qx1r = svld1rq_u8(ph32, x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(ph32, qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(ph32, qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(ph32, y0->qs); + const svint8_t qy1 = svld1_s8(ph32, y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(ph32, sumv0, svcvt_f32_s32_x(ph32, + svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(ph32, sumv1, svcvt_f32_s32_x(ph32, + svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(ph32, svadd_f32_x(ph32, sumv0, sumv1)); + } break; + default: + assert(false && "Unsupported vector length"); + break; + } + +#elif defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); + const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); + const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); + const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + // dot product into int32x4_t + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = _mm256_set1_epi8( 8 ); + qx = _mm256_sub_epi8( qx, off ); + + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + __m256 accum = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m128i q4b_1_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_1), _mm_set1_epi8(8)); + const __m128i q4b_1_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_1, 4)), _mm_set1_epi8(8)); + const __m128i q4b_2_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_2), _mm_set1_epi8(8)); + const __m128i q4b_2_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_2, 4)), _mm_set1_epi8(8)); + + const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); + const __m128i p_1 = _mm_add_epi16(p16_1_0, p16_1_1); + const __m128i p_2 = _mm_add_epi16(p16_2_0, p16_2_1); + const __m256 p = sum_i16_pairs_float(p_2, p_1); + + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); +#elif defined(__SSSE3__) + // set constants + const __m128i lowMask = _mm_set1_epi8(0xF); + const __m128i off = _mm_set1_epi8(8); + + // Initialize accumulator with zeros + __m128 acc_0 = _mm_setzero_ps(); + __m128 acc_1 = _mm_setzero_ps(); + __m128 acc_2 = _mm_setzero_ps(); + __m128 acc_3 = _mm_setzero_ps(); + + for (; ib + 1 < nb; ib += 2) { + _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs); + + __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); + __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); + __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16)); + bx_1 = _mm_sub_epi8(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); + + const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + + __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); + __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + bx_2 = _mm_sub_epi8(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); + __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16)); + bx_3 = _mm_sub_epi8(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = _mm_cvtepi32_ps(i32_0); + __m128 p1 = _mm_cvtepi32_ps(i32_1); + __m128 p2 = _mm_cvtepi32_ps(i32_2); + __m128 p3 = _mm_cvtepi32_ps(i32_3); + + // Apply the scale + __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); + __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); + __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); + __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); + + // Acummulate + acc_0 = _mm_add_ps(p0_d, acc_0); + acc_1 = _mm_add_ps(p1_d, acc_1); + acc_2 = _mm_add_ps(p2_d, acc_2); + acc_3 = _mm_add_ps(p3_d, acc_3); + } + + sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (; ib < nb; ++ib) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + // subtract offset + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector signed char v8 = vec_splats((signed char)0x8); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 8 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_sub(q4x0, v8); + q4x1 = vec_sub(q4x1, v8); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi0 = vec_sum4s(qv1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = __lasx_xvreplgr2vr_b( 8 ); + qx = __lasx_xvsub_b( qx, off ); + + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__loongarch_sx) + // set constants + const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); + const __m128i off = __lsx_vreplgr2vr_b(8); + + // Initialize accumulator with zeros + __m128 acc_0 = __lsx_vldi(0); + __m128 acc_1 = __lsx_vldi(0); + __m128 acc_2 = __lsx_vldi(0); + __m128 acc_3 = __lsx_vldi(0); + + for (; ib + 1 < nb; ib += 2) { + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); + __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); + __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); + __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); + + // Acummulate + acc_0 = __lsx_vfadd_s(p0_d, acc_0); + acc_1 = __lsx_vfadd_s(p1_d, acc_1); + acc_2 = __lsx_vfadd_s(p2_d, acc_2); + acc_3 = __lsx_vfadd_s(p3_d, acc_3); + } + + sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#endif + for (; ib < nb; ++ib) { + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[ib].qs[j] & 0x0F) - 8; + const int v1 = (x[ib].qs[j] >> 4) - 8; + + sumi0 += (v0 * y[ib].qs[j]); + sumi1 += (v1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); + } + + *s = sumf; +} + +void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_1; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_1 * restrict vx0 = vx; + const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); + const block_q8_1 * restrict vy0 = vy; + const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t summs0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_1 * restrict b_x0 = &vx0[i]; + const block_q4_1 * restrict b_x1 = &vx1[i]; + const block_q8_1 * restrict b_y0 = &vy0[i]; + const block_q8_1 * restrict b_y1 = &vy1[i]; + + float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; + summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + // mmla into int32x4_t + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + sumv2 = vaddq_f32(sumv2, summs0); + + vst1_f32(s, vget_low_f32 (sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif + + int ib = 0; + float sumf = 0; + + // TODO: add WASM SIMD +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs = 0; + + for (; ib + 1 < nb; ib += 2) { + const block_q4_1 * restrict x0 = &x[ib + 0]; + const block_q4_1 * restrict x1 = &x[ib + 1]; + const block_q8_1 * restrict y0 = &y[ib + 0]; + const block_q8_1 * restrict y1 = &y[ib + 1]; + + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + // dot product into int32x4_t + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; +#elif defined(__AVX2__) || defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0; + + // Main loop + for (; ib < nb; ++ib) { + const float d0 = GGML_FP16_TO_FP32(x[ib].d); + const float d1 = GGML_FP16_TO_FP32(y[ib].d); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + const __m256 d0v = _mm256_set1_ps( d0 ); + const __m256 d1v = _mm256_set1_ps( d1 ); + + // Compute combined scales + const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[ib].qs); + const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs ); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y +#if defined(__AVX2__) + acc = _mm256_fmadd_ps( d0d1, xy, acc ); +#else + acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); +#endif + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (; ib < nb; ++ib) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); + vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask); + vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_msum(q8y0, q4x0, vsumi0); + vsumi0 = vec_msum(q8y1, q4x1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0; + + // Main loop + for (; ib < nb; ++ib) { + const float d0 = GGML_FP16_TO_FP32(x[ib].d); + const float d1 = GGML_FP16_TO_FP32(y[ib].d); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); + const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); + + // Compute combined scales + const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[ib].qs); + const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y + acc = __lasx_xvfmadd_s( d0d1, xy, acc ); + } + + sumf = hsum_float_8(acc) + summs; +#endif + for (; ib < nb; ++ib) { + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[ib].qs[j] & 0x0F); + const int v1 = (x[ib].qs[j] >> 4); + + sumi0 += (v0 * y[ib].qs[j]); + sumi1 += (v1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + + *s = sumf; +} + +void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + int ib = 0; + float sumf = 0; + + assert(n % qk == 0); + assert(qk == QK5_0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + for (; ib + 1 < nb; ib += 2) { + const block_q5_0 * restrict x0 = &x[ib]; + const block_q5_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + // extract the 5th bit via lookup table ((!b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_1[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_1[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (; ib < nb; ++ib) { + const block_q5_0 * restrict x0 = &x[ib]; + const block_q8_0 * restrict y0 = &y[ib]; + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_1[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); + const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( + wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); + } + + sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); + qx = _mm256_or_si256(qx, bxhi); + + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps(d, q, acc); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8((char)0xF0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + + __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); + const __m256i bxhi = bytes_from_bits_32(x[ib].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_andnot_si128(bxhil, mask); + bxhih = _mm_andnot_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx_0 = MM256_SET_M128I(bxh, bxl); + + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0); + + /* Multiply q with scale and accumulate */ + acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); + } + + sumf = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // These temporary registers are for masking and shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); + + vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); + vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (; ib < nb; ++ib) { + memcpy(&qh, x[ib].qh, sizeof(uint32_t)); + + // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + + // ((qh & (1u << (j + 16))) >> (j + 12)); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); + vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])}; + vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + + vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); + vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl( 16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); + qx = __lasx_xvor_v(qx, bxhi); + + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s(d, q, acc); + } + + sumf = hsum_float_8(acc); +#endif + for (; ib < nb; ++ib) { + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); + + const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16); + const int32_t x1 = (int8_t)(((x[ib].qs[j] >> 4) | xh_1) - 16); + + sumi0 += (x0 * y[ib].qs[j]); + sumi1 += (x1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; + } + + *s = sumf; +} + +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_1; + const int nb = n / qk; + + int ib = 0; + float sumf = 0; + + assert(n % qk == 0); + assert(qk == QK5_1); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs0 = 0.0f; + float summs1 = 0.0f; + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + for (; ib + 1 < nb; ib += 2) { + const block_q5_1 * restrict x0 = &x[ib]; + const block_q5_1 * restrict x1 = &x[ib + 1]; + const block_q8_1 * restrict y0 = &y[ib]; + const block_q8_1 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); + summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); + + // extract the 5th bit via lookup table ((b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_0[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_0[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit + const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + float summs = 0.0f; + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (; ib < nb; ++ib) { + const block_q5_1 * restrict x0 = &x[ib]; + const block_q8_1 * restrict y0 = &y[ib]; + + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_0[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit + const v128_t v0lf = wasm_v128_or(v0l, qhl); + const v128_t v0hf = wasm_v128_or(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, + wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); + } + + sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); + qx = _mm256_or_si256(qx, bxhi); + + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8(0x10); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); + const __m256i bxhi = bytes_from_bits_32(x[ib].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_and_si128(bxhil, mask); + bxhih = _mm_and_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx_0 = MM256_SET_M128I(bxh, bxl); + + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); + + acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // temporary registers for shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (; ib < nb; ++ib) { + memcpy(&qh, x[ib].qh, sizeof(uint32_t)); + + // load qh + vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); + + // ((qh >> (j + 0)) << 4) & 0x10; + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); + + // ((qh >> (j + 12)) ) & 0x10; + vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); + vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + + vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0); + vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl( 16, y[ib].qs); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_msum(q8y0, q5x0, vsumi0); + vsumi0 = vec_msum(q8y1, q5x1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); + qx = __lasx_xvor_v(qx, bxhi); + + const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); + } + + sumf = hsum_float_8(acc) + summs; +#endif + for (; ib < nb; ++ib) { + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; + const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; + + const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0; + const int32_t x1 = (x[ib].qs[j] >> 4) | xh_1; + + sumi0 += (x0 * y[ib].qs[j]); + sumi1 += (x1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + + *s = sumf; +} + +void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q8_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q8_0 * restrict vx0 = vx; + const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q8_0 * restrict b_x0 = &vx0[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + + const block_q8_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const int8x16_t x0_l = vld1q_s8(b_x0->qs); + const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16); + const int8x16_t x1_l = vld1q_s8(b_x1->qs); + const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif + + int ib = 0; + float sumf = 0; + +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + const int vector_length = ggml_cpu_get_sve_cnt()*8; + + //VLA Implemenation for SVE + switch (vector_length) { + case 128: + { + // predicate for activating lanes for 16 Int8 elements + const svbool_t ph16 = svptrue_pat_b8 (SV_VL16); + const svbool_t pl16 = svptrue_pat_b32(SV_VL4); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svint8_t qx0_0 = svld1_s8(ph16, x0->qs); + const svint8_t qx0_1 = svld1_s8(ph16, x0->qs+16); + const svint8_t qx1_0 = svld1_s8(ph16, x1->qs); + const svint8_t qx1_1 = svld1_s8(ph16, x1->qs+16); + + // load y + const svint8_t qy0_0 = svld1_s8(ph16, y0->qs); + const svint8_t qy0_1 = svld1_s8(ph16, y0->qs+16); + const svint8_t qy1_0 = svld1_s8(ph16, y1->qs); + const svint8_t qy1_1 = svld1_s8(ph16, y1->qs+16); + + sumv0 = svmla_n_f32_x(pl16, sumv0, svcvt_f32_s32_x(pl16, svadd_x(pl16, + svdot_s32(svdup_n_s32(0), qx0_0, qy0_0), + svdot_s32(svdup_n_s32(0), qx0_1, qy0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(pl16, sumv1, svcvt_f32_s32_x(pl16, svadd_x(pl16, + svdot_s32(svdup_n_s32(0), qx1_0, qy1_0), + svdot_s32(svdup_n_s32(0), qx1_1, qy1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(pl16, svadd_f32_x(pl16, sumv0, sumv1)); + } break; + case 256: + { + //printf("sve256"); + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); + const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 512: + { + // predicate for activating high 256 bit + const svbool_t ph32 = svptrue_pat_b8(SV_VL32); + // predicate for activating low 256 bit + const svbool_t pl32 = svnot_b_z(svptrue_b8(), ph32); + + // predicate for activating high lanes for 8 float32 elements + const svbool_t ph8 = svptrue_pat_b32(SV_VL8); + // predicate for activating low lanes for 8 float32 elements + const svbool_t pl8 = svnot_b_z(svptrue_b32(), ph8); + + svfloat32_t sumv00 = svdup_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + //load 32 int8_t in first half of vector and put another 32 int8_t in second vector lower bits + // and add them to make one 64 element vector + // load x + const svint8_t qx_32 = svld1_s8(ph32, x0->qs); + svint8_t qx_64 = svld1_s8(pl32, x0->qs + 2); + + qx_64 = svadd_s8_x(svptrue_b8(), qx_32, qx_64); + + // load y + const svint8_t qy_32 = svld1_s8(ph32, y0->qs); + svint8_t qy_64 = svld1_s8(pl32, y0->qs + 2); + + qy_64 = svadd_s8_x(svptrue_b8(), qy_32, qy_64); + + // scale creation + const float32_t deq1 = GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d); + const float32_t deq2 = GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d); + + // duplicate deq1 in first half of vector and deq2 in second half of vector + const svfloat32_t temp = svdup_f32_m(svdup_f32_z(ph8, deq1), pl8, deq2); + + const svfloat32_t sumvt = svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx_64, qy_64)); + + sumv00 = svmla_f32_m(svptrue_b32(), sumv00, sumvt, temp); + } + + sumf = svaddv_f32(svptrue_b32(), sumv00); + break; + } + default: + assert(false && "Unsupported vector length"); + break; + } +#elif defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const int8x16_t x0_0 = vld1q_s8(x0->qs); + const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); + const int8x16_t x1_0 = vld1q_s8(x1->qs); + const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); + + // load y + const int8x16_t y0_0 = vld1q_s8(y0->qs); + const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); + const int8x16_t y1_0 = vld1q_s8(y1->qs); + const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), + ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), + ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + // Compute combined scale for the block + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs); + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = _mm256_fmadd_ps( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + __m256 accum = _mm256_setzero_ps(); + + for (; ib + 1 < nb; ib += 2) { + const __m128i qx_1_0 = _mm_loadu_si128((const __m128i *)x[ib].qs); + const __m128i qx_1_1 = _mm_loadu_si128((const __m128i *)x[ib].qs + 1); + const __m128i qx_2_0 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i qx_2_1 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs + 1); + const __m128i qy_1_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); + const __m128i qy_1_1 = _mm_loadu_si128((const __m128i *)y[ib].qs + 1); + const __m128i qy_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i qy_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m256 p = mul_sum_i8_quad_float(qx_1_0, qx_1_1, qx_2_0, qx_2_1, qy_1_0, qy_1_1, qy_2_0, qy_2_1); + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk); + + for (; ib < nb; ++ib) { + // load elements + vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl); + vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl); + + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); + vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); + + sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); + } +#elif defined(__POWER9_VECTOR__) + const vector signed int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 8 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char q8x0 = vec_xl( 0, x[ib].qs); + vector signed char q8x1 = vec_xl(16, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_mule(q8x0, q8y0); + vector signed short qv1 = vec_mulo(q8x0, q8y0); + vector signed short qv2 = vec_mule(q8x1, q8y1); + vector signed short qv3 = vec_mulo(q8x1, q8y1); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); + vsumi0 = vec_sum4s(qv2, vsumi0); + vsumi1 = vec_sum4s(qv3, vsumi1); + + vsumi0 = vec_add(vsumi0, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + // Compute combined scale for the block + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0); + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#endif + for (; ib < nb; ++ib) { + int sumi = 0; + + for (int j = 0; j < qk; j++) { + sumi += x[ib].qs[j]*y[ib].qs[j]; + } + + sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); + } + + *s = sumf; +} + +void ggml_vec_dot_tq1_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq1_0 * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + float sumf = 0.0f; + + uint8_t k_shift[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; + + const uint8x16_t shift = vld1q_u8(k_shift); + + for (int i = 0; i < nb; ++i) { +#if defined(__ARM_FEATURE_DOTPROD) + int32x4_t sumi0 = vdupq_n_s32(0); + int32x4_t sumi1 = vdupq_n_s32(0); +#else + int16x8_t sumi0 = vdupq_n_s16(0); + int16x8_t sumi1 = vdupq_n_s16(0); +#endif + + // first 32 bytes of 5 elements + { + uint8x16_t qx0 = vld1q_u8(x[i].qs + 0); + uint8x16_t qx1 = vld1q_u8(x[i].qs + 16); + uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(3)); + uint8x16_t qx3 = vmulq_u8(qx1, vdupq_n_u8(3)); + uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(9)); + uint8x16_t qx5 = vmulq_u8(qx1, vdupq_n_u8(9)); + uint8x16_t qx6 = vmulq_u8(qx0, vdupq_n_u8(27)); + uint8x16_t qx7 = vmulq_u8(qx1, vdupq_n_u8(27)); + uint8x16_t qx8 = vmulq_u8(qx0, vdupq_n_u8(81)); + uint8x16_t qx9 = vmulq_u8(qx1, vdupq_n_u8(81)); + + // multiply by 3 and keep the 2 bits above 8 bits + int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); + int8x16_t sqx6 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx6, vshrq_n_u8(qx6, 1)), 6)); + int8x16_t sqx7 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx7, vshrq_n_u8(qx7, 1)), 6)); + int8x16_t sqx8 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx8, vshrq_n_u8(qx8, 1)), 6)); + int8x16_t sqx9 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx9, vshrq_n_u8(qx9, 1)), 6)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + 0); + const int8x16_t qy1 = vld1q_s8(y[i].qs + 16); + const int8x16_t qy2 = vld1q_s8(y[i].qs + 32); + const int8x16_t qy3 = vld1q_s8(y[i].qs + 48); + const int8x16_t qy4 = vld1q_s8(y[i].qs + 64); + const int8x16_t qy5 = vld1q_s8(y[i].qs + 80); + const int8x16_t qy6 = vld1q_s8(y[i].qs + 96); + const int8x16_t qy7 = vld1q_s8(y[i].qs + 112); + const int8x16_t qy8 = vld1q_s8(y[i].qs + 128); + const int8x16_t qy9 = vld1q_s8(y[i].qs + 144); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); + sumi0 = vdotq_s32(sumi0, sqx6, qy6); + sumi1 = vdotq_s32(sumi1, sqx7, qy7); + sumi0 = vdotq_s32(sumi0, sqx8, qy8); + sumi1 = vdotq_s32(sumi1, sqx9, qy9); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx8), vget_low_s8(qy8)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx8), vget_high_s8(qy8)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx9), vget_low_s8(qy9)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx9), vget_high_s8(qy9)); +#endif + } + + // last 16 bytes of 5-element, along with the 4 bytes of 4 elements + { + uint8x16_t qx0 = vld1q_u8(x[i].qs + 32); + uint8x16_t qx1 = vmulq_u8(qx0, vdupq_n_u8(3)); + uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(9)); + uint8x16_t qx3 = vmulq_u8(qx0, vdupq_n_u8(27)); + uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(81)); + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned + uint8x16_t qx5 = vreinterpretq_u8_u32(vdupq_n_u32(qh)); + qx5 = vmulq_u8(qx5, shift); + + // multiply by 3 and keep the 2 bits above 8 bits + int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + 160); + const int8x16_t qy1 = vld1q_s8(y[i].qs + 176); + const int8x16_t qy2 = vld1q_s8(y[i].qs + 192); + const int8x16_t qy3 = vld1q_s8(y[i].qs + 208); + const int8x16_t qy4 = vld1q_s8(y[i].qs + 224); + const int8x16_t qy5 = vld1q_s8(y[i].qs + 240); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); +#endif + } + + const int16x8_t ysum0 = vld1q_s16(y[i].bsums); + const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vaddq_s32(sumi0, sumi1); + sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); + + sumf += d * (float) vaddvq_s32(sumi0); +#else + sumi0 = vaddq_s16(sumi0, sumi1); + sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); + + sumf += d * (float) vaddlvq_s16(sumi0); +#endif + } + + *s = sumf; + +#elif defined(__AVX2__) + __m256 sumf = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + // 16-bit sums + __m256i sumi0 = _mm256_setzero_si256(); + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + + // first 32 bytes of 5 elements + { + __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs)); + // 8-bit multiplies with shifts, masks and adds + __m256i qx1 = _mm256_add_epi8(qx0, _mm256_add_epi8(qx0, qx0)); // 1 * 3 + __m256i qx2 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx0, 3), _mm256_set1_epi8(-8)), qx0); // 1 * 9 + __m256i qx3 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx1, 3), _mm256_set1_epi8(-8)), qx1); // 3 * 9 + __m256i qx4 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx2, 3), _mm256_set1_epi8(-8)), qx2); // 9 * 9 + + // TODO: can _mm256_mulhi_epu16 be faster even if 16-bits? + + // Cancel the +1 from avg so that it behaves like a halving add + qx0 = _mm256_subs_epu8(qx0, _mm256_set1_epi8(1)); + qx1 = _mm256_subs_epu8(qx1, _mm256_set1_epi8(1)); + qx2 = _mm256_subs_epu8(qx2, _mm256_set1_epi8(1)); + qx3 = _mm256_subs_epu8(qx3, _mm256_set1_epi8(1)); + qx4 = _mm256_subs_epu8(qx4, _mm256_set1_epi8(1)); + // Multiply by 3 and get the top 2 bits + qx0 = _mm256_avg_epu8(qx0, _mm256_avg_epu8(qx0, _mm256_setzero_si256())); + qx1 = _mm256_avg_epu8(qx1, _mm256_avg_epu8(qx1, _mm256_setzero_si256())); + qx2 = _mm256_avg_epu8(qx2, _mm256_avg_epu8(qx2, _mm256_setzero_si256())); + qx3 = _mm256_avg_epu8(qx3, _mm256_avg_epu8(qx3, _mm256_setzero_si256())); + qx4 = _mm256_avg_epu8(qx4, _mm256_avg_epu8(qx4, _mm256_setzero_si256())); + qx0 = _mm256_and_si256(_mm256_srli_epi16(qx0, 6), _mm256_set1_epi8(3)); + qx1 = _mm256_and_si256(_mm256_srli_epi16(qx1, 6), _mm256_set1_epi8(3)); + qx2 = _mm256_and_si256(_mm256_srli_epi16(qx2, 6), _mm256_set1_epi8(3)); + qx3 = _mm256_and_si256(_mm256_srli_epi16(qx3, 6), _mm256_set1_epi8(3)); + qx4 = _mm256_and_si256(_mm256_srli_epi16(qx4, 6), _mm256_set1_epi8(3)); + + const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 0)); + const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 32)); + const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 64)); + const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 96)); + const __m256i qy4 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 128)); + + qx0 = _mm256_maddubs_epi16(qx0, qy0); + qx1 = _mm256_maddubs_epi16(qx1, qy1); + qx2 = _mm256_maddubs_epi16(qx2, qy2); + qx3 = _mm256_maddubs_epi16(qx3, qy3); + qx4 = _mm256_maddubs_epi16(qx4, qy4); + + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); + sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); + sumi2 = _mm256_add_epi16(sumi2, qx4); + } + + // last 16 bytes of 5-element, along with the 4 bytes of 4 elements + { + __m128i qx0 = _mm_loadu_si128((const __m128i *) (x[i].qs + 32)); + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned + __m256i qx5_l = _mm256_cvtepu8_epi16(_mm_set1_epi32(qh)); + __m128i qx1 = _mm_add_epi8(qx0, _mm_add_epi8(qx0, qx0)); // 1 * 3 + __m128i qx2 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx0, 3), _mm_set1_epi8(-8)), qx0); // 1 * 9 + __m128i qx3 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx1, 3), _mm_set1_epi8(-8)), qx1); // 3 * 9 + __m128i qx4 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx2, 3), _mm_set1_epi8(-8)), qx2); // 9 * 9 + __m256i qx01 = MM256_SET_M128I(qx1, qx0); + __m256i qx23 = MM256_SET_M128I(qx3, qx2); + + // avx2 does not have 8-bit multiplies, so 16-bit it is. + qx5_l = _mm256_mullo_epi16(qx5_l, _mm256_set_epi16(27, 27, 27, 27, 9, 9, 9, 9, 3, 3, 3, 3, 1, 1, 1, 1)); + qx5_l = _mm256_and_si256(qx5_l, _mm256_set1_epi16(0xFF)); + __m128i qx5 = _mm_packus_epi16(_mm256_castsi256_si128(qx5_l), _mm256_extracti128_si256(qx5_l, 1)); + + __m256i qx45 = MM256_SET_M128I(qx5, qx4); + + // Cancel the +1 from avg so that it behaves like a halving add + qx01 = _mm256_subs_epu8(qx01, _mm256_set1_epi8(1)); + qx23 = _mm256_subs_epu8(qx23, _mm256_set1_epi8(1)); + qx45 = _mm256_subs_epu8(qx45, _mm256_set1_epi8(1)); + // Multiply by 3 and get the top 2 bits + qx01 = _mm256_avg_epu8(qx01, _mm256_avg_epu8(qx01, _mm256_setzero_si256())); + qx23 = _mm256_avg_epu8(qx23, _mm256_avg_epu8(qx23, _mm256_setzero_si256())); + qx45 = _mm256_avg_epu8(qx45, _mm256_avg_epu8(qx45, _mm256_setzero_si256())); + qx01 = _mm256_and_si256(_mm256_srli_epi16(qx01, 6), _mm256_set1_epi8(3)); + qx23 = _mm256_and_si256(_mm256_srli_epi16(qx23, 6), _mm256_set1_epi8(3)); + qx45 = _mm256_and_si256(_mm256_srli_epi16(qx45, 6), _mm256_set1_epi8(3)); + + const __m256i qy01 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 160)); + const __m256i qy23 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 192)); + const __m256i qy45 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 224)); + + qx01 = _mm256_maddubs_epi16(qx01, qy01); + qx23 = _mm256_maddubs_epi16(qx23, qy23); + qx45 = _mm256_maddubs_epi16(qx45, qy45); + + sumi0 = _mm256_add_epi16(sumi0, qx01); + sumi1 = _mm256_add_epi16(sumi1, qx23); + sumi2 = _mm256_add_epi16(sumi2, qx45); + } + + const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); + + sumi0 = _mm256_sub_epi16(sumi0, ysum); + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(sumi1, sumi2)); + sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); + + sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); + } + + *s = hsum_float_8(sumf); + +#else + const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243}; + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + int sum = 0; + + for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) { + for (size_t l = 0; l < 5; ++l) { + for (size_t m = 0; m < 32; ++m) { + uint8_t q = x[i].qs[j + m] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[j*5 + l*32 + m]; + } + } + } + for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) { + for (size_t l = 0; l < 5; ++l) { + for (size_t m = 0; m < 16; ++m) { + uint8_t q = x[i].qs[j + m] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[j*5 + l*16 + m]; + } + } + } + + for (size_t l = 0; l < 4; ++l) { + for (size_t j = 0; j < sizeof(x->qh); ++j) { + uint8_t q = x[i].qh[j] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j]; + } + } + + sumf += (float) sum * (GGML_FP16_TO_FP32(x[i].d) * y[i].d); + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_tq2_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq2_0 * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + float sumf = 0.0f; + + const uint8x16_t m3 = vdupq_n_u8(3); + + for (int i = 0; i < nb; ++i) { +#if defined(__ARM_FEATURE_DOTPROD) + int32x4_t sumi0 = vdupq_n_s32(0); + int32x4_t sumi1 = vdupq_n_s32(0); +#else + int16x8_t sumi0 = vdupq_n_s16(0); + int16x8_t sumi1 = vdupq_n_s16(0); +#endif + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + uint8x16_t qx0 = vld1q_u8(x[i].qs + j); + uint8x16_t qx1 = vld1q_u8(x[i].qs + j + 16); + uint8x16_t qx2 = vshrq_n_u8(qx0, 2); + uint8x16_t qx3 = vshrq_n_u8(qx1, 2); + uint8x16_t qx4 = vshrq_n_u8(qx0, 4); + uint8x16_t qx5 = vshrq_n_u8(qx1, 4); + uint8x16_t qx6 = vshrq_n_u8(qx0, 6); + uint8x16_t qx7 = vshrq_n_u8(qx1, 6); + + int8x16_t sqx0 = vreinterpretq_s8_u8(vandq_u8(qx0, m3)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vandq_u8(qx1, m3)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vandq_u8(qx2, m3)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vandq_u8(qx3, m3)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vandq_u8(qx4, m3)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vandq_u8(qx5, m3)); + int8x16_t sqx6 = vreinterpretq_s8_u8(vandq_u8(qx6, m3)); + int8x16_t sqx7 = vreinterpretq_s8_u8(vandq_u8(qx7, m3)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + j*4 + 0); + const int8x16_t qy1 = vld1q_s8(y[i].qs + j*4 + 16); + const int8x16_t qy2 = vld1q_s8(y[i].qs + j*4 + 32); + const int8x16_t qy3 = vld1q_s8(y[i].qs + j*4 + 48); + const int8x16_t qy4 = vld1q_s8(y[i].qs + j*4 + 64); + const int8x16_t qy5 = vld1q_s8(y[i].qs + j*4 + 80); + const int8x16_t qy6 = vld1q_s8(y[i].qs + j*4 + 96); + const int8x16_t qy7 = vld1q_s8(y[i].qs + j*4 + 112); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); + sumi0 = vdotq_s32(sumi0, sqx6, qy6); + sumi1 = vdotq_s32(sumi1, sqx7, qy7); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); +#endif + } + + const int16x8_t ysum0 = vld1q_s16(y[i].bsums); + const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vaddq_s32(sumi0, sumi1); + sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); + + sumf += d * (float) vaddvq_s32(sumi0); +#else + sumi0 = vaddq_s16(sumi0, sumi1); + sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); + + sumf += d * (float) vaddlvq_s16(sumi0); +#endif + } + + *s = sumf; + +#elif defined(__AVX2__) + __m256 sumf = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + // 16-bit sums, because 256*127 still fits + __m256i sumi0 = _mm256_setzero_si256(); + __m256i sumi1 = _mm256_setzero_si256(); + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs + j)); + __m256i qx1 = _mm256_srli_epi16(qx0, 2); + __m256i qx2 = _mm256_srli_epi16(qx0, 4); + __m256i qx3 = _mm256_srli_epi16(qx0, 6); + + // 0, 1, 2 (should not be 3) + qx0 = _mm256_and_si256(qx0, _mm256_set1_epi8(3)); + qx1 = _mm256_and_si256(qx1, _mm256_set1_epi8(3)); + qx2 = _mm256_and_si256(qx2, _mm256_set1_epi8(3)); + qx3 = _mm256_and_si256(qx3, _mm256_set1_epi8(3)); + + const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 0)); + const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 32)); + const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 64)); + const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 96)); + + qx0 = _mm256_maddubs_epi16(qx0, qy0); + qx1 = _mm256_maddubs_epi16(qx1, qy1); + qx2 = _mm256_maddubs_epi16(qx2, qy2); + qx3 = _mm256_maddubs_epi16(qx3, qy3); + + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); + sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); + } + + const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); + + sumi0 = _mm256_add_epi16(sumi0, sumi1); + sumi0 = _mm256_sub_epi16(sumi0, ysum); + sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); + + sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); + } + + *s = hsum_float_8(sumf); + +#else + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + int32_t sumi = 0; + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + for (size_t l = 0; l < 4; ++l) { + for (size_t k = 0; k < 32; ++k) { + sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1); + } + } + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + sumf += (float) sumi * d; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q2_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + const uint8x16_t m3 = vdupq_n_u8(0x3); + const uint8x16_t m4 = vdupq_n_u8(0xF); + + const int32x4_t vzero = vdupq_n_s32(0); + + ggml_int8x16x2_t q2bytes; + uint8_t aux[16]; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint8_t * restrict sc = x[i].scales; + + const uint8x16_t mins_and_scales = vld1q_u8(sc); + const uint8x16_t scales = vandq_u8(mins_and_scales, m4); + vst1q_u8(aux, scales); + + const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}}; + const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), + vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); + const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), + vmull_s16(vget_high_s16(mins16.val[1]), vget_high_s16(q8sums.val[1]))); + sum += dmin * vaddvq_s32(vaddq_s32(s0, s1)); + + int isum = 0; + int is = 0; + +// We use this macro instead of a function call because for some reason +// the code runs 2-3% slower, even if the function is declared inline +#define MULTIPLY_ACCUM_WITH_SCALE(index)\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; + +#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ + q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ + q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ + MULTIPLY_ACCUM_WITH_SCALE((index)); + + for (int j = 0; j < QK_K/128; ++j) { + const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; + + ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); + q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); + + MULTIPLY_ACCUM_WITH_SCALE(0); + + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2); + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4); + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6); + + is += 8; + } + + sum += d * isum; + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + const __m128i m4 = _mm_set1_epi8(0xF); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales8 = _mm_and_si128(mins_and_scales, m4); + const __m128i mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); + const __m256i mins = _mm256_cvtepi8_epi16(mins8); + const __m256i prod = _mm256_madd_epi16(mins, _mm256_loadu_si256((const __m256i*)y[i].bsums)); + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc); + + const __m256i all_scales = _mm256_cvtepi8_epi16(scales8); + const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); + const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); + const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; + + __m256i sumi = _mm256_setzero_si256(); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = _mm256_loadu_si256((const __m256i*)q2); q2 += 32; + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i q2_0 = _mm256_and_si256(q2bits, m3); + const __m256i q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3); + const __m256i q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3); + const __m256i q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3); + + __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); + __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); + __m256i p2 = _mm256_maddubs_epi16(q2_2, q8_2); + __m256i p3 = _mm256_maddubs_epi16(q2_3, q8_3); + + p0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = _mm256_add_epi32(p0, p1); + p2 = _mm256_add_epi32(p2, p3); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2)); + } + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(0x3); + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // load mins and scales from block_q2_K.scales[QK_K/16] + const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales16 = _mm_and_si128(mins_and_scales, m4); + const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); + const __m128i mins_0 = _mm_cvtepi8_epi16(mins16); + const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16)); + + // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2 + const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0])); + const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8])); + + // sumf += -dmin * summs in 32bits*8 + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc); + + const __m128i scales_0 = _mm_cvtepi8_epi16(scales16); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16)); + const __m128i scales[2] = { scales_0, scales_1 }; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + + // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // load 2bits*16*8 from block_q2_K.qs[QK_K/4] + __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_0 = _mm_and_si128(q2bits, m3); + const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_1 = _mm_and_si128(q2bits, m3); + const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + + // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8 + __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0); + __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1); + __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2); + __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3); + __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4); + __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5); + __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6); + __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7); + + // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8 + __m128i shuffle = _mm_set1_epi16(0x0100); + p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0); + shuffle = _mm_add_epi16(shuffle, m2); + p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1); + shuffle = _mm_add_epi16(shuffle, m2); + p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2); + shuffle = _mm_add_epi16(shuffle, m2); + p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3); + shuffle = _mm_add_epi16(shuffle, m2); + p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4); + shuffle = _mm_add_epi16(shuffle, m2); + p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5); + shuffle = _mm_add_epi16(shuffle, m2); + p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6); + shuffle = _mm_add_epi16(shuffle, m2); + p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7); + + p0 = _mm_add_epi32(p0, p1); + p2 = _mm_add_epi32(p2, p3); + p4 = _mm_add_epi32(p4, p5); + p6 = _mm_add_epi32(p6, p7); + + // isum in 32bits*4*2 + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6)); + } + + // sumf += dall * isum - dmin * summs in 32bits + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + float sumf = 0; + uint8_t temp_01[32] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * q2 = x[i].qs; + const int8_t * q8 = y[i].qs; + const uint8_t * sc = x[i].scales; + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + size_t vl = 16; + + vuint8m1_t scales = __riscv_vle8_v_u8m1(sc, vl); + vuint8m1_t aux = __riscv_vand_vx_u8m1(scales, 0x0F, vl); + + vint16m1_t q8sums = __riscv_vle16_v_i16m1(y[i].bsums, vl); + + vuint8mf2_t scales_2 = __riscv_vle8_v_u8mf2(sc, vl); + vuint8mf2_t mins8 = __riscv_vsrl_vx_u8mf2(scales_2, 0x4, vl); + vint16m1_t mins = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(mins8, vl)); + vint32m2_t prod = __riscv_vwmul_vv_i32m2(q8sums, mins, vl); + vint32m1_t vsums = __riscv_vredsum_vs_i32m2_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + + sumf += dmin * __riscv_vmv_x_s_i32m1_i32(vsums); + + vl = 32; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t v_b = __riscv_vle8_v_u8m1(temp_01, vl); + + uint8_t is=0; + int isum=0; + + for (int j = 0; j < QK_K/128; ++j) { + // load Q2 + vuint8m1_t q2_x = __riscv_vle8_v_u8m1(q2, vl); + + vuint8m1_t q2_0 = __riscv_vand_vx_u8m1(q2_x, 0x03, vl); + vuint8m1_t q2_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x2, vl), 0x03 , vl); + vuint8m1_t q2_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x4, vl), 0x03 , vl); + vuint8m1_t q2_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x6, vl), 0x03 , vl); + + // duplicate scale elements for product + vuint8m1_t sc0 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 0+is, vl), vl); + vuint8m1_t sc1 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 2+is, vl), vl); + vuint8m1_t sc2 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 4+is, vl), vl); + vuint8m1_t sc3 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 6+is, vl), vl); + + vint16m2_t p0 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_0, sc0, vl)); + vint16m2_t p1 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_1, sc1, vl)); + vint16m2_t p2 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_2, sc2, vl)); + vint16m2_t p3 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_3, sc3, vl)); + + // load Q8 + vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); + vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8+64, vl); + vint8m1_t q8_3 = __riscv_vle8_v_i8m1(q8+96, vl); + + vint32m4_t s0 = __riscv_vwmul_vv_i32m4(p0, __riscv_vwcvt_x_x_v_i16m2(q8_0, vl), vl); + vint32m4_t s1 = __riscv_vwmul_vv_i32m4(p1, __riscv_vwcvt_x_x_v_i16m2(q8_1, vl), vl); + vint32m4_t s2 = __riscv_vwmul_vv_i32m4(p2, __riscv_vwcvt_x_x_v_i16m2(q8_2, vl), vl); + vint32m4_t s3 = __riscv_vwmul_vv_i32m4(p3, __riscv_vwcvt_x_x_v_i16m2(q8_3, vl), vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s0, s1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s2, s3, vl), isum0, vl); + + isum += __riscv_vmv_x_s_i32m1_i32(isum1); + + q2+=32; q8+=128; is=8; + + } + + sumf += dall * isum; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); + vector signed char vscales = vec_and(q2xmins, lowScaleMask); + + q2xmins = vec_sr(q2xmins, v4); + vector signed short q2xmins0 = vec_unpackh(q2xmins); + vector signed short q2xmins1 = vec_unpackl(q2xmins); + + vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); + vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); + vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); + vector signed char qxs1 = (vector signed char)vec_xl(16, q2); + q2 += 32; + + vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask); + vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask); + vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask); + vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask); + vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask); + vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed int qv0 = vec_msum(q8y00, q2x00, v0); + vector signed int qv1 = vec_msum(q8y01, q2x01, v0); + vector signed int qv2 = vec_msum(q8y02, q2x02, v0); + vector signed int qv3 = vec_msum(q8y03, q2x03, v0); + vector signed int qv4 = vec_msum(q8y10, q2x10, v0); + vector signed int qv5 = vec_msum(q8y11, q2x11, v0); + vector signed int qv6 = vec_msum(q8y12, q2x12, v0); + vector signed int qv7 = vec_msum(q8y13, q2x13, v0); + + vector signed short vscales_07 = vec_unpackh(vscales); + vector signed int vscales_03 = vec_unpackh(vscales_07); + vector signed int vscales_47 = vec_unpackl(vscales_07); + vector signed int vs0 = vec_splat(vscales_03, 0); + vector signed int vs1 = vec_splat(vscales_03, 1); + vector signed int vs2 = vec_splat(vscales_03, 2); + vector signed int vs3 = vec_splat(vscales_03, 3); + vector signed int vs4 = vec_splat(vscales_47, 0); + vector signed int vs5 = vec_splat(vscales_47, 1); + vector signed int vs6 = vec_splat(vscales_47, 2); + vector signed int vs7 = vec_splat(vscales_47, 3); + vscales = vec_sld(vscales, vscales, 8); + + vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1); + vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2); + vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3); + vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4); + vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5); + vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6); + vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m128i m4 = __lsx_vreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); + const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); + const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); + const __m256i mins = lasx_ext8_16(mins8); + const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); + + const __m256i all_scales = lasx_ext8_16(scales8); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); + const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); + const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); + const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); + + __m256i p0 = lasx_maddubs_h(q2_0, q8_0); + __m256i p1 = lasx_maddubs_h(q2_1, q8_1); + __m256i p2 = lasx_maddubs_h(q2_2, q8_2); + __m256i p3 = lasx_maddubs_h(q2_3, q8_3); + + p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = __lasx_xvadd_w(p0, p1); + p2 = __lasx_xvadd_w(p2, p3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); + } + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#else + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * q2 = x[i].qs; + const int8_t * q8 = y[i].qs; + const uint8_t * sc = x[i].scales; + + int summs = 0; + for (int j = 0; j < 16; ++j) { + summs += y[i].bsums[j] * (sc[j] >> 4); + } + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + int isum = 0; + int is = 0; + int d; + for (int k = 0; k < QK_K/128; ++k) { + int shift = 0; + for (int j = 0; j < 4; ++j) { + d = sc[is++] & 0xF; + int isuml = 0; + for (int l = 0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); + isum += d * isuml; + d = sc[is++] & 0xF; + isuml = 0; + for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); + isum += d * isuml; + shift += 2; + q8 += 32; + } + q2 += 32; + } + sumf += dall * isum - dmin * summs; + } + *s = sumf; +#endif +} + +void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const uint32_t kmask1 = 0x03030303; + const uint32_t kmask2 = 0x0f0f0f0f; + + const block_q3_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + uint32_t aux[3]; + uint32_t utmp[4]; + + const uint8x16_t m3b = vdupq_n_u8(0x3); + const int32x4_t vzero = vdupq_n_s32(0); + + const uint8x16_t m0 = vdupq_n_u8(1); + const uint8x16_t m1 = vshlq_n_u8(m0, 1); + const uint8x16_t m2 = vshlq_n_u8(m0, 2); + const uint8x16_t m3 = vshlq_n_u8(m0, 3); + const int8_t m32 = 32; + + ggml_int8x16x4_t q3bytes; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); + + ggml_uint8x16x4_t q3h; + + int32_t isum = 0; + + // Set up scales + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= m32; + + for (int j = 0; j < QK_K/128; ++j) { + + const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; + const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; + const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; + + q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); + q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); + q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); + q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); + + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; + + scale += 4; + + q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); + q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); + q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); + q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); + + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; + + scale += 4; + + if (j == 0) { + qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); + qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); + } + + } + sum += d * isum; + + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + const __m256i mone = _mm256_set1_epi8(1); + const __m128i m32 = _mm_set1_epi8(32); + + __m256 acc = _mm256_setzero_ps(); + + uint32_t aux[3]; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = _mm_set_epi32( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = _mm_sub_epi8(scales128, m32); + const __m256i all_scales = _mm256_cvtepi8_epi16(scales128); + const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); + const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); + const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; + + // high bit + const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask); + + // integer accumulator + __m256i sumi = _mm256_setzero_si256(); + + int bit = 0; + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = _mm256_loadu_si256((const __m256i*)q3); q3 += 32; + + // prepare low and high bits + const __m256i q3l_0 = _mm256_and_si256(q3bits, m3); + const __m256i q3h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3); + const __m256i q3h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3); + const __m256i q3h_2 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3); + const __m256i q3h_3 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + // load Q8 quants + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); + __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); + __m256i q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2); + __m256i q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3); + + __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); + __m256i p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2); + __m256i p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + p16_2 = _mm256_sub_epi16(p16_2, q8s_2); + p16_3 = _mm256_sub_epi16(p16_3, q8s_3); + + // multiply with scales + p16_0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + + // accumulate + p16_0 = _mm256_add_epi32(p16_0, p16_1); + p16_2 = _mm256_add_epi32(p16_2, p16_3); + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2)); + + } + + // multiply with block scale and accumulate + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(3); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m32 = _mm_set1_epi8(32); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + const uint32_t *aux; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // Set up scales + aux = (const uint32_t *)x[i].scales; + __m128i scales128 = _mm_set_epi32( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = _mm_sub_epi8(scales128, m32); + const __m128i scales_0 = _mm_cvtepi8_epi16(scales128); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128)); + const __m128i scales[2] = { scales_0, scales_1 }; + + // high bit *128*2 from block_q3_K.hmask[QK_K/8] + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]); + + // integer accumulator + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4] + const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + + // prepare low and high bits + const int bit = j << 2; + + const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); + const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); + const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); + const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2); + + const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3); + const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3); + const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + + const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3); + const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3); + const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + + const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3); + const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3); + const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + + // load Q8 quants from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0); + __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1); + __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2); + __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3); + __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4); + __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5); + __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6); + __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7); + + __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7); + + p16_0 = _mm_sub_epi16(p16_0, q8s_0); + p16_1 = _mm_sub_epi16(p16_1, q8s_1); + p16_2 = _mm_sub_epi16(p16_2, q8s_2); + p16_3 = _mm_sub_epi16(p16_3, q8s_3); + p16_4 = _mm_sub_epi16(p16_4, q8s_4); + p16_5 = _mm_sub_epi16(p16_5, q8s_5); + p16_6 = _mm_sub_epi16(p16_6, q8s_6); + p16_7 = _mm_sub_epi16(p16_7, q8s_7); + + // multiply with scales + __m128i shuffle = _mm_set1_epi16(0x0100); + p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0); + shuffle = _mm_add_epi16(shuffle, m2); + p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1); + shuffle = _mm_add_epi16(shuffle, m2); + p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2); + shuffle = _mm_add_epi16(shuffle, m2); + p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3); + shuffle = _mm_add_epi16(shuffle, m2); + p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4); + shuffle = _mm_add_epi16(shuffle, m2); + p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5); + shuffle = _mm_add_epi16(shuffle, m2); + p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6); + shuffle = _mm_add_epi16(shuffle, m2); + p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7); + + // accumulate + p16_0 = _mm_add_epi32(p16_0, p16_1); + p16_2 = _mm_add_epi32(p16_2, p16_3); + p16_4 = _mm_add_epi32(p16_4, p16_5); + p16_6 = _mm_add_epi32(p16_6, p16_7); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6)); + + } + + // multiply with block scale and accumulate + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + uint32_t aux[3]; + uint32_t utmp[4]; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= 32; + + + size_t vl = 32; + uint8_t m = 1; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t vqh = __riscv_vle8_v_u8m1(qh, vl); + + int sum_t = 0; + + for (int j = 0; j < QK_K; j += 128) { + + vl = 32; + + // load Q3 + vuint8m1_t q3_x = __riscv_vle8_v_u8m1(q3, vl); + + vint8m1_t q3_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q3_x, 0x03, vl)); + vint8m1_t q3_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x2, vl), 0x03 , vl)); + vint8m1_t q3_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x4, vl), 0x03 , vl)); + vint8m1_t q3_3 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x6, vl), 0x03 , vl)); + + // compute mask for subtraction + vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl); + vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl); + vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl); + vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl); + vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl); + m <<= 1; + + // load Q8 and take product with Q3 + vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); + vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); + vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); + vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); + + vl = 16; + + // retrieve lane to multiply with scale + vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); + vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); + vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); + vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); + vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); + vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); + vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); + vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); + vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); + vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); + + sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); + + q3 += 32; q8 += 128; scale += 8; + + } + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + sumf += d*sum_t; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowMask1 = vec_splats((int8_t)0xf); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector signed char v1 = vec_splats((signed char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + UNUSED(kmask1); + UNUSED(kmask2); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(u0, lowMask1); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2)); + vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4); + vector signed char u31 = vec_and(u3, lowMask2); + + u1 = vec_or(u1, u30); + u2 = vec_or(vec_sr(u0, v4), u31); + + vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2); + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); + + vscales = vec_sub(vscales, off); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); + vector signed char qxs1 = (vector signed char)vec_xl(16, q3); + q3 += 32; + + //the low 2 bits + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); + + //the 3rd bit + vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); + vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); + vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); + vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); + vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); + vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); + vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); + vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); + qxhs0 = vec_sr(qxhs0, v4); + qxhs1 = vec_sr(qxhs1, v4); + + vector signed char q3x00 = vec_sub(qxs00, qxh00); + vector signed char q3x01 = vec_sub(qxs01, qxh01); + vector signed char q3x02 = vec_sub(qxs02, qxh02); + vector signed char q3x03 = vec_sub(qxs03, qxh03); + vector signed char q3x10 = vec_sub(qxs10, qxh10); + vector signed char q3x11 = vec_sub(qxs11, qxh11); + vector signed char q3x12 = vec_sub(qxs12, qxh12); + vector signed char q3x13 = vec_sub(qxs13, qxh13); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); + vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); + vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); + vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); + vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); + vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); + + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs2, vsumi1); + vsumi2 = vec_msum(qv02, vs4, vsumi2); + vsumi3 = vec_msum(qv03, vs6, vsumi3); + vsumi4 = vec_msum(qv10, vs1, vsumi4); + vsumi5 = vec_msum(qv11, vs3, vsumi5); + vsumi6 = vec_msum(qv12, vs5, vsumi6); + vsumi7 = vec_msum(qv13, vs7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + const __m128i m32 = __lsx_vreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + uint32_t aux[3]; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = lsx_set_w( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = __lsx_vsub_b(scales128, m32); + const __m256i all_scales = lasx_ext8_16(scales128); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + // high bit + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); + + // integer accumulator + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + int is = 0; + __m256i xvbit; + + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + + xvbit = __lasx_xvreplgr2vr_h(bit); + // prepare low and high bits + const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + // load Q8 quants + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); + __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); + __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); + __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + // multiply with scales + p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + + // accumulate + p16_0 = __lasx_xvadd_w(p16_0, p16_1); + p16_2 = __lasx_xvadd_w(p16_2, p16_3); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); + } + // multiply with block scale and accumulate + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME + } + + *s = hsum_float_8(acc); + +#else + // scalar version + // This function is written like this so the compiler can manage to vectorize most of it + // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the + // manually vectorized version above. Every other version I tried would run at least 4 times slower. + // The ideal situation would be if we could just write the code once, and the compiler would + // automatically produce the best possible set of machine instructions, instead of us having to manually + // write vectorized versions for AVX, ARM_NEON, etc. + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + uint32_t auxs[4]; + const int8_t * scales = (const int8_t*)auxs; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict hm = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + uint8_t m = 1; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + q3 += 32; + } + a = aux8; + + memcpy(auxs, x[i].scales, 12); + uint32_t tmp = auxs[2]; + auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); + auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); + auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); + auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); + for (int j = 0; j < QK_K/16; ++j) { + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; + +#endif + +} + +void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + uint32_t utmp[4]; + +#ifdef __ARM_NEON + const uint8x16_t m4b = vdupq_n_u8(0xf); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x2_t q4bytes; + ggml_int8x16x2_t q8bytes; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); + + memcpy(utmp, x[i].scales, 12); + + uint32x2_t mins8 = { 0 }; + mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0); + mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1); + + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[0] &= kmask1; + + const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8))); + const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), + vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); + sumf -= dmin * vaddvq_s32(prod); + + const uint8_t * scales = (const uint8_t *)utmp; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + int32_t sumi1 = 0; + int32_t sumi2 = 0; + + for (int j = 0; j < QK_K/64; ++j) { + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); + q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + sumi1 += vaddvq_s32(p1) * scales[2*j+0]; + + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); + q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); + + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + + sumi2 += vaddvq_s32(p2) * scales[2*j+1]; + } + + sumf += d * (sumi1 + sumi2); + + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + + __m256 acc = _mm256_setzero_ps(); + __m128 acc_m = _mm_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); + acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m); + + const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); + const __m256i scales = MM256_SET_M128I(sc128, sc128); + + __m256i sumi = _mm256_setzero_si256(); + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4l = _mm256_and_si256(q4bits, m4); + const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); + + const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); + p16l = _mm256_madd_epi16(scale_l, p16l); + + const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); + p16h = _mm256_madd_epi16(scale_h, p16h); + const __m256i sumj = _mm256_add_epi32(p16l, p16h); + + sumi = _mm256_add_epi32(sumi, sumj); + } + + __m256 vd = _mm256_set1_ps(d); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); + + } + + acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); + acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); + + *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); + +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + __m128 acc_m = _mm_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_0 = _mm_and_si128(q4bits, m4); + const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_1 = _mm_and_si128(q4bits, m4); + const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + + const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_0 = _mm_add_epi32(sumi_0, p16l); + const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16l = _mm_maddubs_epi16(q4l_1, q8l_1); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_1 = _mm_add_epi32(sumi_1, p16l); + + const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_0 = _mm_add_epi32(sumi_0, p16h); + const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16h = _mm_maddubs_epi16(q4h_1, q8h_1); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_1 = _mm_add_epi32(sumi_1, p16h); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); + acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); + + *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); + +#elif defined __riscv_v_intrinsic + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + size_t vl = 8; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); + vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); + vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); + vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); + vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); + + vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + vl = 32; + + int32_t sum_1 = 0; + int32_t sum_2 = 0; + + vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); + + for (int j = 0; j < QK_K/64; ++j) { + // load Q4 + vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); + + // load Q8 and multiply it with lower Q4 nibble + vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q4_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); + vint16m2_t qv_0 = __riscv_vwmul_vv_i16m2(q4_0, q8_0, vl); + vint16m1_t vs_0 = __riscv_vredsum_vs_i16m2_i16m1(qv_0, vzero, vl); + + sum_1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[2*j+0]; + + // load Q8 and multiply it with upper Q4 nibble + vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); + vint8m1_t q4_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); + vint16m2_t qv_1 = __riscv_vwmul_vv_i16m2(q4_1, q8_1, vl); + vint16m1_t vs_1 = __riscv_vredsum_vs_i16m2_i16m1(qv_1, vzero, vl); + + sum_2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[2*j+1]; + + q4 += 32; q8 += 64; + + } + + sumf += d*(sum_1 + sum_2); + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((uint8_t)2); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); + + vector signed short vscales = vec_unpackh(utmps); + vector signed short q4xmins = vec_unpackl(utmps); + vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); + vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); + + vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; j+=2) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + vector signed char qxs2 = (vector signed char)vec_xl(32, q4); + vector signed char qxs3 = (vector signed char)vec_xl(48, q4); + q4 += 64; + + vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4); + vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4); + vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask); + vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4); + vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask); + vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y20 = vec_xl( 64, q8); + vector signed char q8y30 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed int qv00 = vec_msum(q8y00, q4x00, v0); + vector signed int qv01 = vec_msum(q8y01, q4x01, v0); + vector signed int qv10 = vec_msum(q8y10, q4x10, v0); + vector signed int qv11 = vec_msum(q8y11, q4x11, v0); + vector signed int qv20 = vec_msum(q8y20, q4x20, v0); + vector signed int qv21 = vec_msum(q8y21, q4x21, v0); + vector signed int qv30 = vec_msum(q8y30, q4x30, v0); + vector signed int qv31 = vec_msum(q8y31, q4x31, v0); + + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); + vector signed int vs2 = vec_splat(vscales_h, 2); + vector signed int vs3 = vec_splat(vscales_h, 3); + vscales = vec_sld(vscales, vscales, 8); + + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3); + + vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + __m128 acc_m = (__m128)__lsx_vldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4l = __lasx_xvand_v(q4bits, m4); + const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); + + const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16l = lasx_maddubs_h(q4l, q8l); + p16l = lasx_madd_h(scale_l, p16l); + + const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16h = lasx_maddubs_h(q4h, q8h); + p16h = lasx_madd_h(scale_h, p16h); + const __m256i sumj = __lasx_xvadd_w(p16l, p16h); + + sumi = __lasx_xvadd_w(sumi, sumj); + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + + } + + acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); + __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); + acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + + + ft_union fi; + fi.i = __lsx_vpickve2gr_w(acc_m, 0); + *s = hsum_float_8(acc) + fi.f ; +#else + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + a += 32; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + a += 32; q4 += 32; + } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/32; ++j) { + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + uint32_t utmp[4]; + +#ifdef __ARM_NEON + const uint8x16_t m4b = vdupq_n_u8(0xf); + const uint8x16_t mone = vdupq_n_u8(1); + const uint8x16_t mtwo = vdupq_n_u8(2); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x4_t q5bytes; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8x8_t mins8 = vld1_u8((const uint8_t*)utmp + 8); + const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(mins8)); + const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), + vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); + int32_t sumi_mins = vaddvq_s32(prod); + + const uint8_t * scales = (const uint8_t *)utmp; + + const uint8_t * restrict q5 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); + + ggml_uint8x16x4_t q5h; + + int32_t sumi = 0; + + for (int j = 0; j < QK_K/64; ++j) { + + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); + q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); + q5h.val[2] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[0]), 3); + q5h.val[3] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[1]), 3); + qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 2); + qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 2); + + q5bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[0], m4b), q5h.val[0])); + q5bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[1], m4b), q5h.val[1])); + q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); + q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); + + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; + } + + sumf += d * sumi - dmin * sumi_mins; + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m128i mzero = _mm_setzero_si128(); + const __m256i mone = _mm256_set1_epi8(1); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); + const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); + summs += dmin * _mm_extract_epi32(hsum, 0); + + const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); + const __m256i scales = MM256_SET_M128I(sc128, sc128); + + const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh); + __m256i hmask = mone; + + __m256i sumi = _mm256_setzero_si256(); + + int bit = 0; + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); q5 += 32; + + const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); + const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); + const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); + const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); + const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + __m256i p16_0 = _mm256_maddubs_epi16(q5_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q5_1, q8_1); + + p16_0 = _mm256_madd_epi16(scale_0, p16_0); + p16_1 = _mm256_madd_epi16(scale_1, p16_1); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + + } + + __m256 vd = _mm256_set1_ps(d); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i mzero = _mm_setzero_si128(); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); + summs += dmin * _mm_extract_epi32(hsum, 0); + + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]); + __m128i hmask = mone; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + int bit = 0; + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + + __m128i q5l_0 = _mm_and_si128(q5bits_0, m4); + __m128i q5l_1 = _mm_and_si128(q5bits_1, m4); + __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + __m128i q5_0 = _mm_add_epi8(q5l_0, q5h_0); + __m128i q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1); + p16_0 = _mm_madd_epi16(scale_0, p16_0); + p16_1 = _mm_madd_epi16(scale_0, p16_1); + + q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4); + q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4); + q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + q5_0 = _mm_add_epi8(q5l_0, q5h_0); + q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1); + p16_2 = _mm_madd_epi16(scale_1, p16_2); + p16_3 = _mm_madd_epi16(scale_1, p16_3); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#elif defined __riscv_v_intrinsic + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + float sumf = 0; + float sums = 0.0; + + size_t vl; + + for (int i = 0; i < nb; ++i) { + + vl = 8; + + const uint8_t * restrict q5 = x[i].qs; + const uint8_t * restrict hm = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + + vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); + vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); + vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); + vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); + vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); + + vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); + + vl = 32; + int32_t aux32 = 0; + int is = 0; + + uint8_t m = 1; + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t vqh = __riscv_vle8_v_u8m1(hm, vl); + + for (int j = 0; j < QK_K/64; ++j) { + // load Q5 and Q8 + vuint8m1_t q5_x = __riscv_vle8_v_u8m1(q5, vl); + vint8m1_t q8_y1 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q8_y2 = __riscv_vle8_v_i8m1(q8+32, vl); + + // compute mask for addition + vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl)); + vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl); + vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl); + m <<= 1; + + vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl)); + vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl); + vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl); + m <<= 1; + + vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl); + vint16m2_t v1 = __riscv_vwmul_vv_i16m2(q5_m2, q8_y2, vl); + + vint32m4_t vs1 = __riscv_vwmul_vx_i32m4(v0, scales[is++], vl); + vint32m4_t vs2 = __riscv_vwmul_vx_i32m4(v1, scales[is++], vl); + + vint32m1_t vacc1 = __riscv_vredsum_vs_i32m4_i32m1(vs1, vzero, vl); + vint32m1_t vacc2 = __riscv_vredsum_vs_i32m4_i32m1(vs2, vzero, vl); + + aux32 += __riscv_vmv_x_s_i32m1_i32(vacc1) + __riscv_vmv_x_s_i32m1_i32(vacc2); + q5 += 32; q8 += 64; + + } + + vfloat32m1_t vaux = __riscv_vfmul_vf_f32m1(__riscv_vfmv_v_f_f32m1(aux32, 1), d, 1); + sums += __riscv_vfmv_f_s_f32m1_f32(vaux); + + } + + *s = sumf+sums; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v1 = vec_splats((unsigned char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed short vscales = vec_unpackh(utmps); + + vector signed short q5xmins = vec_unpackl(utmps); + vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); + vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); + + vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q5, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); + vector signed char qxs1 = (vector signed char)vec_xl(16, q5); + q5 += 32; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + + vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); + vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); + vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); + vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); + qxhs0 = vec_sr(qxhs0, v2); + qxhs1 = vec_sr(qxhs1, v2); + + vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00); + vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01); + vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10); + vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl(16, q8); + vector signed char q8y01 = vec_xl(32, q8); + vector signed char q8y11 = vec_xl(48, q8); + q8 += 64; + + vector signed int qv00 = vec_msum(q8y00, q5x00, v0); + vector signed int qv01 = vec_msum(q8y01, q5x01, v0); + vector signed int qv10 = vec_msum(q8y10, q5x10, v0); + vector signed int qv11 = vec_msum(q8y11, q5x11, v0); + + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); + vscales = vec_sld(vscales, vscales, 12); + + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1); + vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2); + vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m128i mzero = __lsx_vldi(0); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); + summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); + __m256i hmask = mone; + + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + __m256i xvbit; + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + + xvbit = __lasx_xvreplgr2vr_h(bit++); + const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); + const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); + hmask = __lasx_xvslli_h(hmask, 1); + + xvbit = __lasx_xvreplgr2vr_h(bit++); + const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); + const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); + hmask = __lasx_xvslli_h(hmask, 1); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); + + p16_0 = lasx_madd_h(scale_0, p16_0); + p16_1 = lasx_madd_h(scale_1, p16_1); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#else + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const uint8_t * restrict hm = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + uint8_t m = 1; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + q4 += 32; + } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/32; ++j) { + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q6_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + float sum = 0; + + const uint8x16_t m4b = vdupq_n_u8(0xF); + const int32x4_t vzero = vdupq_n_s32(0); + //const int8x16_t m32s = vdupq_n_s8(32); + + const uint8x16_t mone = vdupq_n_u8(3); + + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; + + for (int i = 0; i < nb; ++i) { + + const float d_all = GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const int8_t * restrict scale = x[i].scales; + + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const int8x16_t scales = vld1q_s8(scale); + const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}}; + + const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), + vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), + vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[1]), vget_low_s16 (q6scales.val[1])), + vmull_s16(vget_high_s16(q8sums.val[1]), vget_high_s16(q6scales.val[1])))); + int32_t isum_mins = vaddvq_s32(prod); + + int32_t isum = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; + ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); + q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); + uint8x16_t shifted = vshrq_n_u8(qhbits.val[0], 2); + q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 2); + q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + + //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); + //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); + //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])), m32s); + //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])), m32s); + q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])); + q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])); + q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); + q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + + scale += 4; + + q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + shifted = vshrq_n_u8(qhbits.val[0], 4); + q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 4); + q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[0], 6); + q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 6); + q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + + //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])), m32s); + //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])), m32s); + //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])), m32s); + //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])), m32s); + q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])); + q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])); + q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); + q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + scale += 4; + } + //sum += isum * d_all * y[i].d; + sum += d_all * y[i].d * (isum - 32 * isum_mins); + + } + *s = sum; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i m2 = _mm256_set1_epi8(3); + const __m256i m32s = _mm256_set1_epi8(32); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); + + __m256i sumi = _mm256_setzero_si256(); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); + is += 4; + + const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32; + + const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4); + const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4); + + const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); + const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1); + const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); + __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); + __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2); + __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3); + + __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); + __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2); + __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + p16_2 = _mm256_sub_epi16(p16_2, q8s_2); + p16_3 = _mm256_sub_epi16(p16_3, q8s_3); + + p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); + p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2); + p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3)); + + } + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(3); + const __m128i m15 = _mm_set1_epi8(15); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + // handle the q6_k -32 offset separately using bsums + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)y[i].bsums); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)y[i].bsums + 1); + const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales_16_0 = _mm_cvtepi8_epi16(scales); + const __m128i scales_16_1 = _mm_cvtepi8_epi16(_mm_bsrli_si128(scales, 8)); + const __m128i q8sclsub_0 = _mm_slli_epi32(_mm_madd_epi16(q8sums_0, scales_16_0), 5); + const __m128i q8sclsub_1 = _mm_slli_epi32(_mm_madd_epi16(q8sums_1, scales_16_1), 5); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + + const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4); + const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4); + const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, _mm_set1_epi8(12)), 2); + const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, _mm_set1_epi8(12)), 2); + const __m128i q4h_4 = _mm_and_si128(q4bitsH_0, _mm_set1_epi8(48)); + const __m128i q4h_5 = _mm_and_si128(q4bitsH_1, _mm_set1_epi8(48)); + const __m128i q4h_6 = _mm_srli_epi16(_mm_and_si128(q4bitsH_0, _mm_set1_epi8(-64)), 2); + const __m128i q4h_7 = _mm_srli_epi16(_mm_and_si128(q4bitsH_1, _mm_set1_epi8(-64)), 2); + + const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + + const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m15), q4h_0); + const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m15), q4h_1); + const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m15), q4h_2); + const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m15), q4h_3); + const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m15), q4h_4); + const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m15), q4h_5); + const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m15), q4h_6); + const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m15), q4h_7); + + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7); + + const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); + is += 4; + + p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_0, 8)), p16_1); + p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); + p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_1, 8)), p16_3); + p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4); + p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_2, 8)), p16_5); + p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6); + p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_3, 8)), p16_7); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7)); + + } + + sumi_0 = _mm_sub_epi32(sumi_0, q8sclsub_0); + sumi_1 = _mm_sub_epi32(sumi_1, q8sclsub_1); + const __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const int8_t * restrict scale = x[i].scales; + + size_t vl; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + + int sum_t = 0; + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + vl = 32; + + // load qh + vuint8m1_t qh_x = __riscv_vle8_v_u8m1(qh, vl); + + // load Q6 + vuint8m1_t q6_0 = __riscv_vle8_v_u8m1(q6, vl); + vuint8m1_t q6_1 = __riscv_vle8_v_u8m1(q6+32, vl); + + vuint8m1_t q6a_0 = __riscv_vand_vx_u8m1(q6_0, 0x0F, vl); + vuint8m1_t q6a_1 = __riscv_vand_vx_u8m1(q6_1, 0x0F, vl); + vuint8m1_t q6s_0 = __riscv_vsrl_vx_u8m1(q6_0, 0x04, vl); + vuint8m1_t q6s_1 = __riscv_vsrl_vx_u8m1(q6_1, 0x04, vl); + + vuint8m1_t qh_0 = __riscv_vand_vx_u8m1(qh_x, 0x03, vl); + vuint8m1_t qh_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x2, vl), 0x03 , vl); + vuint8m1_t qh_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x4, vl), 0x03 , vl); + vuint8m1_t qh_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x6, vl), 0x03 , vl); + + vuint8m1_t qhi_0 = __riscv_vor_vv_u8m1(q6a_0, __riscv_vsll_vx_u8m1(qh_0, 0x04, vl), vl); + vuint8m1_t qhi_1 = __riscv_vor_vv_u8m1(q6a_1, __riscv_vsll_vx_u8m1(qh_1, 0x04, vl), vl); + vuint8m1_t qhi_2 = __riscv_vor_vv_u8m1(q6s_0, __riscv_vsll_vx_u8m1(qh_2, 0x04, vl), vl); + vuint8m1_t qhi_3 = __riscv_vor_vv_u8m1(q6s_1, __riscv_vsll_vx_u8m1(qh_3, 0x04, vl), vl); + + vint8m1_t a_0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_0), 32, vl); + vint8m1_t a_1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_1), 32, vl); + vint8m1_t a_2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_2), 32, vl); + vint8m1_t a_3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_3), 32, vl); + + // load Q8 and take product + vint16m2_t va_q_0 = __riscv_vwmul_vv_i16m2(a_0, __riscv_vle8_v_i8m1(q8, vl), vl); + vint16m2_t va_q_1 = __riscv_vwmul_vv_i16m2(a_1, __riscv_vle8_v_i8m1(q8+32, vl), vl); + vint16m2_t va_q_2 = __riscv_vwmul_vv_i16m2(a_2, __riscv_vle8_v_i8m1(q8+64, vl), vl); + vint16m2_t va_q_3 = __riscv_vwmul_vv_i16m2(a_3, __riscv_vle8_v_i8m1(q8+96, vl), vl); + + vl = 16; + + vint32m2_t vaux_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 0), scale[is+0], vl); + vint32m2_t vaux_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 1), scale[is+1], vl); + vint32m2_t vaux_2 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 0), scale[is+2], vl); + vint32m2_t vaux_3 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 1), scale[is+3], vl); + vint32m2_t vaux_4 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 0), scale[is+4], vl); + vint32m2_t vaux_5 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 1), scale[is+5], vl); + vint32m2_t vaux_6 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 0), scale[is+6], vl); + vint32m2_t vaux_7 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 1), scale[is+7], vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_0, vaux_1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_2, vaux_3, vl), isum0, vl); + vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_4, vaux_5, vl), isum1, vl); + vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_6, vaux_7, vl), isum2, vl); + + sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); + + q6 += 64; qh += 32; q8 += 128; is=8; + + } + + sumf += d * sum_t; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict qs = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q6, 0, 0); + __builtin_prefetch(qh, 0, 0); + __builtin_prefetch(q8, 0, 0); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); + vector signed char qxs1 = (vector signed char)vec_xl(16, q6); + vector signed char qxs2 = (vector signed char)vec_xl(32, q6); + vector signed char qxs3 = (vector signed char)vec_xl(48, q6); + q6 += 64; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + vector signed char qxs20 = vec_and(qxs2, lowMask); + vector signed char qxs21 = vec_sr(qxs2, v4); + vector signed char qxs30 = vec_and(qxs3, lowMask); + vector signed char qxs31 = vec_sr(qxs3, v4); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); + qh += 32; + + vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); + vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); + vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); + vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); + vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); + vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); + vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); + vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); + + vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); + vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); + vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); + vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); + vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); + vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); + vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); + vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y20 = vec_xl( 32, q8); + vector signed char q8y30 = vec_xl( 48, q8); + vector signed char q8y01 = vec_xl( 64, q8); + vector signed char q8y11 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); + vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); + vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); + vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); + vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); + vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); + + vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); + qs += 8; + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vector signed short vs4 = vec_splat(vscales, 4); + vector signed short vs5 = vec_splat(vscales, 5); + vector signed short vs6 = vec_splat(vscales, 6); + vector signed short vs7 = vec_splat(vscales, 7); + + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs4, vsumi1); + vsumi2 = vec_msum(qv10, vs1, vsumi2); + vsumi3 = vec_msum(qv11, vs5, vsumi3); + vsumi4 = vec_msum(qv20, vs2, vsumi4); + vsumi5 = vec_msum(qv21, vs6, vsumi5); + vsumi6 = vec_msum(qv30, vs3, vsumi6); + vsumi7 = vec_msum(qv31, vs7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i m2 = __lasx_xvreplgr2vr_b(3); + const __m256i m32s = __lasx_xvreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); + + __m256i sumi = __lasx_xvldi(0); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); + is += 4; + + const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; + + const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); + const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); + + const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); + const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); + const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); + __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); + __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); + __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); + p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); + p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); + p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); + } + + acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + } + + *s = hsum_float_8(acc); + +#else + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + } + a += 128; + q4 += 64; + qh += 32; + } + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/16; ++j) { + int scale = x[i].scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +#if defined (__AVX__) || defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) +static const int8_t keven_signs_q2xs[1024] = { + 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, + 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, + 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, + 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, + 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, + 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, + 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, + 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, + 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, + 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, + 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, + 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, + 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, + 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, + 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, + 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, + 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, + 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, + 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, + 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, + 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, + 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, + 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, + 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, + 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, + 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, + 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, + 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, + 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, + 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, + 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, + 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, +}; +#endif + +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 8])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 9]))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[10])), vld1_s8((const void *)(iq2xxs_grid + aux8[11]))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 7) & 127)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 21) & 127)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]), q2u.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]), q2u.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[1] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[3] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.25f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m128i q2_1_0 = _mm_set_epi64x(iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2xxs_grid[aux8[3]], iq2xxs_grid[aux8[2]]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]]); + const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); + const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127]); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + memcpy(aux32, q2, 4*sizeof(uint32_t)); + q2 += 8; + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = aux32[1] >> 28; + const uint16_t ls1 = aux32[3] >> 28; + + vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + + const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + uint32_t aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(aux32, q2, 2*sizeof(uint32_t)); + q2 += 4; + const uint32_t ls = 2*(aux32[1] >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} + +void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + int32x4x4_t scales32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint8x8_t scales8 = vld1_u8(x[i].scales); + const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf)); + const uint8x8_t scales_h = vshr_n_u8(scales8, 4); + uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h)); + scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1)); + const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales)); + const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales)); + scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1))); + scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1))); + scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2))); + scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); + int32x4_t sumi = vdupq_n_s32(0); + for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511)))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]); + const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4)); + sumi = vmlaq_s32(sumi, p, scales32.val[ib64]); + q2 += 8; + } + sumf += d*vaddvq_s32(sumi); + } + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + const __m256i mone = _mm256_set1_epi8(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); + const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); + const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); + const __m256i m511 = _mm256_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = _mm_set1_epi64x(aux64); + stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); + const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2); q2 += 16; + aux_gindex = _mm256_and_si256(q2_data, m511); + + const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9); + const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13); + const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits); + + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); + const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); + const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); + const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); + + __m256i signs; + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone)); + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const __m256i dot3 = _mm256_maddubs_epi16(q2_3, q8s_3); + const __m256i dot4 = _mm256_maddubs_epi16(q2_4, q8s_4); + + const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3))); + + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4)); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const __m128i mone = _mm_set1_epi8(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i bit_selector_mask_0 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes); + const __m128i bit_selector_mask_1 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes + 1); + const __m128i block_sign_shuffle_1_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1); + const __m128i block_sign_shuffle_1_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1 + 1); + const __m128i block_sign_shuffle_2_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2); + const __m128i block_sign_shuffle_2_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2 + 1); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m128i bit_helper_0 = _mm_loadu_si128((const __m128i*)k_bit_helper); + const __m128i bit_helper_1 = _mm_loadu_si128((const __m128i*)k_bit_helper + 1); + const __m128i m511 = _mm_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = _mm_set1_epi64x(aux64); + stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); + const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m128i q2_data_0 = _mm_loadu_si128((const __m128i*)q2); + const __m128i q2_data_1 = _mm_loadu_si128((const __m128i*)q2 + 1); q2 += 16; + aux_gindex = MM256_SET_M128I(_mm_and_si128(q2_data_1, m511), _mm_and_si128(q2_data_0, m511)); + + const __m128i partial_sign_bits_0 = _mm_srli_epi16(q2_data_0, 9); + const __m128i partial_sign_bits_1 = _mm_srli_epi16(q2_data_1, 9); + const __m128i partial_sign_bits_upper_0 = _mm_srli_epi16(q2_data_0, 13); + const __m128i partial_sign_bits_upper_1 = _mm_srli_epi16(q2_data_1, 13); + const __m128i partial_sign_bits_for_counting_0 = _mm_xor_si128(partial_sign_bits_0, partial_sign_bits_upper_0); + const __m128i partial_sign_bits_for_counting_1 = _mm_xor_si128(partial_sign_bits_1, partial_sign_bits_upper_1); + + const __m128i odd_bits_0 = _mm_shuffle_epi8(bit_helper_0, partial_sign_bits_for_counting_0); + const __m128i odd_bits_1 = _mm_shuffle_epi8(bit_helper_1, partial_sign_bits_for_counting_1); + const __m128i full_sign_bits_0 = _mm_or_si128(partial_sign_bits_0, odd_bits_0); + const __m128i full_sign_bits_1 = _mm_or_si128(partial_sign_bits_1, odd_bits_1); + + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_3_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_3_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_4_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_4_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i q2_1_0 = _mm_set_epi64x(iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]]); + const __m128i q2_3_0 = _mm_set_epi64x(iq2xs_grid[gindex[9]], iq2xs_grid[gindex[8]]); + const __m128i q2_3_1 = _mm_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]]); + const __m128i q2_4_0 = _mm_set_epi64x(iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + const __m128i q2_4_1 = _mm_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]]); + + // AVX2 full_signs_1 is full_sign_bits_0 here + // AVX2 full_signs_2 is full_sign_bits_1 here + __m128i signs_0, signs_1; + signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_3_0 = _mm_sign_epi8(q8_3_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_3_1 = _mm_sign_epi8(q8_3_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_4_0 = _mm_sign_epi8(q8_4_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_4_1 = _mm_sign_epi8(q8_4_1, _mm_or_si128(signs_1, mone)); + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const __m128i dot3_0 = _mm_maddubs_epi16(q2_3_0, q8s_3_0); + const __m128i dot3_1 = _mm_maddubs_epi16(q2_3_1, q8s_3_1); + const __m128i dot4_0 = _mm_maddubs_epi16(q2_4_0, q8s_4_0); + const __m128i dot4_1 = _mm_maddubs_epi16(q2_4_1, q8s_4_1); + + __m128i sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0)); + const __m128i sc1_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc1_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1)); + const __m128i sc2_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc2_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2)); + const __m128i sc3_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc3_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3)); + const __m128i sc4_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc4_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot1_0, sc1_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot1_1, sc1_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot2_0, sc2_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot2_1, sc2_1)); + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot3_0, sc3_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot3_1, sc3_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot4_0, sc4_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot4_1, sc4_1)); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__loongarch_asx) + + const __m256i mone = __lasx_xvreplgr2vr_b(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); + const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); + const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); + const __m256i m511 = __lasx_xvreplgr2vr_h(511); + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = __lsx_vreplgr2vr_d(aux64); + stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); + const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; + aux_gindex = __lasx_xvand_v(q2_data, m511); + + const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); + const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); + const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); + + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + + const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); + const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); + const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); + const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); + + __m256i signs; + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); + + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); + const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); + + const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); + + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; + q2 += 8; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1; + const uint16_t ls2 = 2*(sc[ib32] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls1; + sumi = 0; + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls2; + q2 += 4; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} + +void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const uint8x16_t m1 = vdupq_n_u8(1); + const int32x4_t vzero = vdupq_n_s32(0); + + uint8x16x2_t vs; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); + q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); + q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); + q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); + qs += 8; + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); + q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + signs += 4; + + q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); + q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); + + const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); + sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); + sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); + sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); + } + sumf += d*(sumi1 + sumi2); + } + + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + uint64_t aux64; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); + const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); + const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); + const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); + + uint64_t aux64; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m128i scales16_0 = _mm_cvtepi8_epi16(scales8); + const __m128i scales16_1 = _mm_cvtepi8_epi16(_mm_srli_si128(scales8, 8)); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q2_1_0 = _mm_set_epi64x(iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)]); + qs += 8; + + __m128i aux128_0 = _mm_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); + __m128i aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); + const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); + + aux128_0 = _mm_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); + aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); + const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); + + signs += 4; + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 0))); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 1))); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 0))); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 1))); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q2 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; + q2 += 8; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); + vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); + vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); + vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); + vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + uint64_t aux64; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + __m128i tmp1; + memcpy(&aux64, x[i].scales, 8); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); + const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); + const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + int bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); + int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += ls1 * sumi1 + ls2 * sumi2; + qs += 4; + signs += 4; + } + + sumf += d * bsum; + } + + *s = 0.125f * sumf; + +#endif + +} + +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); + q3 += 16; + q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); + q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); + q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); + q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.5f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q2_1_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + const __m128i q2_1_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); + q3 += 8; + const __m128i q2_2_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + const __m128i q2_2_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127]); + const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q3 = x[i].qs; + const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); + const int8_t * restrict q8 = y[i].qs; + +#pragma GCC unroll 1 + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; + vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; + vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; + vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + q3 += 16; + + vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; + vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; + vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; + + vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); + vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); + vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); + vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(signs[0] >> 28); + const uint16_t ls1 = (uint16_t)(signs[1] >> 28); + signs += 2; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.25f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + + const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.25f * hsum_float_8(accumf); + +#else + + uint32_t aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); + const uint32_t ls = 2*(aux32 >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + q3 += 8; + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.25f * sumf; +#endif +} + +void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + typedef union { + uint16x8_t vec_index; + uint16_t index[8]; + } vec_index_t; + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; + + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); + + uint8x16x2_t vs; + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + vec_index_t idx; + + uint32_t scales32[2]; + const uint8_t * scales8 = (const uint8_t *)scales32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(scales32, x[i].scales, 4); + scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; + scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; + + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; + idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); + idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); + + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); + + q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); + + signs += 4; + + q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; + sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; + } + sumf += d*(sumi1 + sumi2); + } + *s = sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = _mm256_set1_epi32(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; + idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); + idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); + idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); + idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = _mm256_set_epi32( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = _mm256_set_epi32( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = hsum_float_8(accumf); + +#elif defined(__AVX__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); + const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); + const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); + const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); + + const __m128i idx_mul_0 = _mm_set_epi32(32, 64, 128, 256); + const __m128i idx_mul_1 = _mm_set_epi32(2, 4, 8, 16); + const __m128i idx_mask = _mm_set1_epi32(256); + + typedef union { + __m128i vec[4]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i qs_tmp = _mm_loadu_si128((const __m128i *)qs); + const __m128i idx_l_0 = _mm_cvtepu8_epi16(qs_tmp); + const __m128i idx_l_1 = _mm_cvtepu8_epi16(_mm_srli_si128(qs_tmp, 8)); qs += 16; + idx.vec[0] = _mm_set1_epi32(qh[ib32+0]); + idx.vec[1] = idx.vec[0]; + idx.vec[2] = _mm_set1_epi32(qh[ib32+1]); + idx.vec[3] = idx.vec[2]; + + idx.vec[0] = _mm_and_si128(_mm_mullo_epi32(idx.vec[0], idx_mul_0), idx_mask); + idx.vec[1] = _mm_and_si128(_mm_mullo_epi32(idx.vec[1], idx_mul_1), idx_mask); + idx.vec[2] = _mm_and_si128(_mm_mullo_epi32(idx.vec[2], idx_mul_0), idx_mask); + idx.vec[3] = _mm_and_si128(_mm_mullo_epi32(idx.vec[3], idx_mul_1), idx_mask); + + idx.vec[0] = _mm_or_si128(idx.vec[0], _mm_cvtepi16_epi32(idx_l_0)); + idx.vec[1] = _mm_or_si128(idx.vec[1], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_0, 8))); + idx.vec[2] = _mm_or_si128(idx.vec[2], _mm_cvtepi16_epi32(idx_l_1)); + idx.vec[3] = _mm_or_si128(idx.vec[3], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_1, 8))); + + const __m128i q2_1_0 = _mm_set_epi32(iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]); + const __m128i q2_1_1 = _mm_set_epi32(iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]]); + const __m128i q2_2_0 = _mm_set_epi32(iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[9]], iq3s_grid[idx.index[8]]); + const __m128i q2_2_1 = _mm_set_epi32(iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]]); + + __m128i aux128_0 = _mm_set1_epi32(signs[0] | (signs[1] << 16)); + __m128i aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); + const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); + + aux128_0 = _mm_set1_epi32(signs[2] | (signs[3] << 16)); + aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); + const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); + + signs += 4; + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], + iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; + vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], + iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; + vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], + iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; + vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], + iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; + q3 += 16; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); + vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); + vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); + vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); + vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + sc ++; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + + __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; + idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); + idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); + idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); + idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = lasx_set_w( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = lasx_set_w( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = hsum_float_8(accumf); + +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint8_t * restrict signs = x[i].signs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1; + const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls1; + sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls2; + } + sumf += d * bsum; + } + *s = sumf; +#endif +} + +#if defined(__AVX2__) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = _mm256_sign_epi8(x, x); + const __m256i sy = _mm256_sign_epi8(y, x); + return _mm256_maddubs_epi16(ax, sy); +} +#elif defined(__loongarch_asx) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = __lasx_xvsigncov_b(x, x); + const __m256i sy = __lasx_xvsigncov_b(x, y); + __m256i tmp1, tmp2, tmp3; + tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); + tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); + tmp3 = __lasx_xvadd_h(tmp1, tmp2); + return __lasx_xvsat_h(tmp3, 15); +} +#endif + +void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq1_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + + ggml_int8x16x4_t q1b; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + int sumi1 = 0, sumi2 = 0, sumi3 = 0; + + for (int ib = 0; ib < QK_K/32; ib += 2) { + + q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700))))); + q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700))))); + q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700))))); + q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700))))); + qs += 8; + + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); + + const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + sumi1 += vaddvq_s32(p1) * ls1; + sumi2 += vaddvq_s32(p2) * ls2; + sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1) + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1); + + } + + sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3); + } + + *s = sumf; + +#elif defined __AVX2__ + + __m256 accum = _mm256_setzero_ps(); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = _mm256_setzero_si256(); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], + iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); + const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], + iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); + qs += 8; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2)); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum); + accum1 += d * sumi1; + + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#elif defined __AVX__ + __m256 accum = _mm256_setzero_ps(); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q1b_1_0 = _mm_set_epi64x(iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); + const __m128i q1b_1_1 = _mm_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)]); + const __m128i q1b_2_0 = _mm_set_epi64x(iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); + const __m128i q1b_2_1 = _mm_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)]); + qs += 8; + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); + const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); + const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); + const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(ls1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(ls1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(ls2)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(ls2)); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum); + accum1 += d * sumi1; + + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#elif defined(__POWER9_VECTOR__) + const vector unsigned char v0 = vec_splats((unsigned char)0x0); + const vector unsigned short vsign = vec_splats((unsigned short)0x8000); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi8 = vec_splats((int32_t)0); + + const uint8_t * restrict q1 = x[i].qs; + const uint16_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + const int16_t * restrict qs = y[i].bsums; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q1, 0, 1); + __builtin_prefetch(qh, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; + q1 += 8; + + vector signed char q1x0 = (vector signed char)aux64x2_0; + vector signed char q1x1 = (vector signed char)aux64x2_1; + vector signed char q1x2 = (vector signed char)aux64x2_2; + vector signed char q1x3 = (vector signed char)aux64x2_3; + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); + + const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); + const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + vector signed short vscales = vec_sld(vscales23, vscales01, 8); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + + vector signed short q8ysums = vec_xl_len(qs, 8); + qs += 4; + q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); + + vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); + qh += 2; + vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); + + vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); + + vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + + vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + __m256 accum = (__m256)__lasx_xvldi(0); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = __lasx_xvldi(0); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); + + __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); + + qs += 8; + const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + + __m256i tmp1, tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); + const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); + + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); + const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); + accum1 += d * sumi1; + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#else + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + int sumi = 0, sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + const int ls = 2*((qh[ib] >> 12) & 7) + 1; + const int delta = qh[ib] & 0x8000 ? -1 : 1; + int lsum = 0; + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); + for (int j = 0; j < 8; ++j) { + lsum += q8[j] * grid[j]; + } + q8 += 8; + } + sumi += ls * lsum; + sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]); + qs += 4; + } + + sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1); + } + + *s = sumf; + +#endif +} + +void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq1_m * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + iq1m_scale_t scale; + +#if defined __ARM_NEON + const int32x4_t mask = vdupq_n_s32(0x7); + const int32x4_t mone = vdupq_n_s32(1); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x4_t deltas; + deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1)); + deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1)); + deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1)); + deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1)); + + ggml_int8x16x4_t q1b; + ggml_int8x16x4_t q8b; + + uint32_t aux32; + const uint8_t * aux8 = (const uint8_t *)&aux32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + int32x4_t sumi1 = mzero; + int32x4_t sumi2 = mzero; + + for (int ib = 0; ib < QK_K/32; ib += 2) { + + q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700))))); + q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700))))); + q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700))))); + q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700))))); + + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1])); + const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3])); + const int32x4_t p12 = vpaddq_s32(p1, p2); + + const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that + aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202); + + const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1])); + const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); + const int32x4_t p34 = vpaddq_s32(p3, p4); + + int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); + + scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); + + sumi1 = vmlaq_s32(sumi1, scales_4, p12); + sumi2 = vmlaq_s32(sumi2, scales_4, p34); + + qs += 8; qh += 4; + + } + + sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i mask = _mm256_set1_epi16(0x7); + const __m256i mone = _mm256_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m256i q1b_1 = _mm256_set_epi64x( + iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)], + iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)] + ); + const __m256i q1b_2 = _mm256_set_epi64x( + iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)], + iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)] + ); + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + + const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + + const __m256i dot3 = mul_add_epi8(delta1, q8b_1); + const __m256i dot4 = mul_add_epi8(delta2, q8b_2); + + __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); + __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); + + scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); + scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); + const __m256i p1 = _mm256_madd_epi16(dot1, scale1); + const __m256i p2 = _mm256_madd_epi16(dot2, scale2); + const __m256i p3 = _mm256_madd_epi16(dot3, scale1); + const __m256i p4 = _mm256_madd_epi16(dot4, scale2); + + sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4)); + + qs += 8; qh += 4; + } + + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); + + accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); + accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); + } + + *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); + +#elif defined __AVX__ + const __m128i mask = _mm_set1_epi16(0x7); + const __m128i mone = _mm_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q1b_1_0 = _mm_set_epi64x( + iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]); + const __m128i q1b_1_1 = _mm_set_epi64x( + iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)]); + const __m128i q1b_2_0 = _mm_set_epi64x( + iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]); + const __m128i q1b_2_1 = _mm_set_epi64x( + iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)]); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); + const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); + const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); + const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); + + const __m128i delta1_0 = _mm_set_epi64x(qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta1_1 = _mm_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta2_0 = _mm_set_epi64x(qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta2_1 = _mm_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + + const __m128i dot3_0 = mul_add_epi8_sse(delta1_0, q8b_1_0); + const __m128i dot3_1 = mul_add_epi8_sse(delta1_1, q8b_1_1); + const __m128i dot4_0 = mul_add_epi8_sse(delta2_0, q8b_2_0); + const __m128i dot4_1 = mul_add_epi8_sse(delta2_1, q8b_2_1); + + __m128i scale1_0 = _mm_set1_epi16(sc[ib/2] >> 0); + __m128i scale1_1 = _mm_set1_epi16(sc[ib/2] >> 3); + __m128i scale2_0 = _mm_set1_epi16(sc[ib/2] >> 6); + __m128i scale2_1 = _mm_set1_epi16(sc[ib/2] >> 9); + + scale1_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_0, mask), 1), mone); + scale1_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_1, mask), 1), mone); + scale2_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_0, mask), 1), mone); + scale2_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_1, mask), 1), mone); + const __m128i p1_0 = _mm_madd_epi16(dot1_0, scale1_0); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, scale1_1); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, scale2_0); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, scale2_1); + const __m128i p3_0 = _mm_madd_epi16(dot3_0, scale1_0); + const __m128i p3_1 = _mm_madd_epi16(dot3_1, scale1_1); + const __m128i p4_0 = _mm_madd_epi16(dot4_0, scale2_0); + const __m128i p4_1 = _mm_madd_epi16(dot4_1, scale2_1); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_add_epi32(p3_0, p4_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_add_epi32(p3_1, p4_1)); + + qs += 8; qh += 4; + } + + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); + + accum1 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum1); + accum2 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi2_1, sumi2_0))), accum2); + } + + *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); + +#else + + int sum1[2], sum2[2], delta[4]; + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + delta[0] = qh[0] & 0x08 ? -1 : 1; + delta[1] = qh[0] & 0x80 ? -1 : 1; + delta[2] = qh[1] & 0x08 ? -1 : 1; + delta[3] = qh[1] & 0x80 ? -1 : 1; + sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0; + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700))); + int lsum1 = 0, lsum2 = 0; + for (int j = 0; j < 8; ++j) { + lsum1 += q8[j] * grid[j]; + lsum2 += q8[j]; + } + q8 += 8; + sum1[l/2] += lsum1; + sum2[l/2] += lsum2*delta[l]; + } + + const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; + const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; + + sumi1 += sum1[0] * ls1 + sum1[1] * ls2; + sumi2 += sum2[0] * ls1 + sum2[1] * ls2; + qs += 4; + qh += 2; + } + + sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); + } + + *s = sumf; + +#endif +} + +void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK4_NL == 0); + static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); + + const block_iq4_nl * restrict x = vx; + const block_q8_0 * restrict y = vy; + + const int nb = n / QK4_NL; + + int ib = 0; + float sumf = 0; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + uint8x16x2_t q4bits; + int8x16x4_t q4b; + int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + for (; ib + 1 < nb; ib += 2) { + + q4bits.val[0] = vld1q_u8(x[ib + 0].qs); + q4bits.val[1] = vld1q_u8(x[ib + 1].qs); + q8b.val[0] = vld1q_s8(y[ib + 0].qs); + q8b.val[1] = vld1q_s8(y[ib + 0].qs + 16); + q8b.val[2] = vld1q_s8(y[ib + 1].qs); + q8b.val[3] = vld1q_s8(y[ib + 1].qs + 16); + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + sumf += + GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) + + GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2); + } + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + const __m256i mone = _mm256_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs); + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs); + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); + const __m256i p_2 = _mm256_madd_epi16(p16_2, mone); + accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), + _mm256_cvtepi32_ps(p_1), accum1); + accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), + _mm256_cvtepi32_ps(p_2), accum2); + } + + sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); + +#elif defined __AVX__ + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); + const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); + const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); + const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); + + const __m256 p = mul_sum_i8_quad_float(q4b_1_0, q4b_1_1, q4b_2_0, q4b_2_1, q8b_1_0, q8b_1_1, q8b_2_0, q8b_2_1); + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); + q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + } + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined (__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + const __m256i mone = __lasx_xvreplgr2vr_h(1); + + __m256 accum1 = (__m256)__lasx_xvldi(0); + __m256 accum2 = (__m256)__lasx_xvldi(0); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0); + const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0); + const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0); + const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0); + const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); + const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = lasx_madd_h(p16_1, mone); + const __m256i p_2 = lasx_madd_h(p16_2, mone); + accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), + __lasx_xvffint_s_w(p_1), accum1); + accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), + __lasx_xvffint_s_w(p_2), accum2); + } + + sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); + +#endif + for (; ib < nb; ++ib) { + const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf]; + sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >> 4]; + } + sumf += d * (sumi1 + sumi2); + } + *s = sumf; +} + +void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_K == 0); + + const block_iq4_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + ggml_uint8x16x2_t q4bits; + ggml_int8x16x4_t q4b; + ggml_int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + float sumf = 0; + + for (int ibl = 0; ibl < nb; ++ibl) { + + const int8_t * q8 = y[ibl].qs; + const uint8_t * q4 = x[ibl].qs; + uint16_t h = x[ibl].scales_h; + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/64; ++ib) { + + q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; + int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; + h >>= 4; + sumi1 += vaddvq_s32(prod_1) * ls1; + sumi2 += vaddvq_s32(prod_2) * ls2; + + } + + sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); + const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); + sumi1 = _mm256_add_epi32(p_1, sumi1); + sumi2 = _mm256_add_epi32(p_2, sumi2); + } + accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#elif defined __AVX__ + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)qs); qs += 16; + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); + const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); + const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); + const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); + const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, _mm_set1_epi16(ls1)); + const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, _mm_set1_epi16(ls1)); + const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, _mm_set1_epi16(ls2)); + const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, _mm_set1_epi16(ls2)); + sumi1_0 = _mm_add_epi32(p_1_0, sumi1_0); + sumi1_1 = _mm_add_epi32(p_1_1, sumi1_1); + sumi2_0 = _mm_add_epi32(p_2_0, sumi2_0); + sumi2_1 = _mm_add_epi32(p_2_1, sumi2_1); + } + __m128i sumi12_0 = _mm_add_epi32(sumi1_0, sumi2_0); + __m128i sumi12_1 = _mm_add_epi32(sumi1_1, sumi2_1); + accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(MM256_SET_M128I(sumi12_1, sumi12_0))), accum); + } + + *s = hsum_float_8(accum); + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + + for (int ibl = 0; ibl < nb; ++ibl) { + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); + vector float vyd = vec_splats(y[ibl].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + uint16_t h = x[ibl].scales_h; + + const uint8_t * restrict q4 = x[ibl].qs; + const uint8_t * restrict sc = x[ibl].scales_l; + const int8_t * restrict q8 = y[ibl].qs; + + for (int ib = 0; ib < QK_K/64; ib ++ ) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + q4 += 32; + + vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); + vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); + vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); + vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); + + q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); + q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); + q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); + q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); + + const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); + const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); + h >>= 4; + sc ++; + + vector signed short vscales01 = vec_splats((int16_t)ls0); + vector signed short vscales23 = vec_splats((int16_t)ls1); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + + __m256 accum = (__m256)__lasx_xvldi(0); + __m256i tmp1; + __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; + + mask_8f = __lsx_vreplgr2vr_b(0x8f); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + __m128i zero = __lsx_vldi(0); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); + + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); + + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + __m256i tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); + const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); + const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); + sumi1 = __lasx_xvadd_w(p_1, sumi1); + sumi2 = __lasx_xvadd_w(p_2, sumi2); + } + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#else + float sumf = 0; + for (int ibl = 0; ibl < nb; ++ibl) { + const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; + uint16_t h = x[ibl].scales_h; + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); + const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); + h >>= 4; + const float d1 = d4d8*(ls1 - 32); + const float d2 = d4d8*(ls2 - 32); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d1 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + sumi1 = sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d2 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + } + } + *s = sumf; +#endif +} + +// ============================ 4-bit non-linear quants + +void quantize_row_iq4_nl(const float * restrict x, void * restrict y, int64_t k) { + assert(k % QK4_NL == 0); + quantize_row_iq4_nl_ref(x, y, k); +} + +void quantize_row_iq4_xs(const float * restrict x, void * restrict y, int64_t k) { + assert(k % QK_K == 0); + quantize_iq4_xs(x, y, 1, k, NULL); +} diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.h b/ggml/src/ggml-cpu/ggml-cpu-quants.h new file mode 100644 index 0000000000..e33d9d473e --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.h @@ -0,0 +1,63 @@ +#pragma once + +#define GGML_COMMON_DECL_C +#include "ggml-common.h" + +#include "ggml.h" + +// GGML CPU internal header + +#ifdef __cplusplus +extern "C" { +#endif + +// Quantization +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_tq1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +// Dot product +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c new file mode 100644 index 0000000000..4b58254e7d --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -0,0 +1,13970 @@ +#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows +#define _USE_MATH_DEFINES // For M_PI on MSVC + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-cpu-aarch64.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu.h" +#include "ggml-impl.h" +#include "ggml-quants.h" +#include "ggml-cpu-quants.h" +#include "ggml-threading.h" +#include "ggml.h" + +#if defined(_MSC_VER) || defined(__MINGW32__) +#include // using malloc.h with MSC/MINGW +#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if defined(__gnu_linux__) +#include +#endif + +#ifdef GGML_USE_OPENMP +#include +#endif + +#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) +#undef GGML_USE_LLAMAFILE +#endif + +#ifdef GGML_USE_LLAMAFILE +#include "llamafile/sgemm.h" +#endif + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) + +// disable POSIX deprecation warnings +// these functions are never going away, anyway +#pragma warning(disable: 4996) + +// unreachable code because of multiple instances of code after GGML_ABORT +#pragma warning(disable: 4702) +#endif + +// Note: once we move threading into a separate C++ file +// will use std::hardware_destructive_interference_size instead of hardcoding it here +// and we'll use C++ attribute syntax. +#define GGML_CACHE_LINE 64 + +#if defined(__clang__) || defined(__GNUC__) +#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) +#endif + +#if defined(__has_feature) +#if __has_feature(thread_sanitizer) +#define GGML_TSAN_ENABLED 1 +#endif +#else // __has_feature +#if defined(__SANITIZE_THREAD__) +#define GGML_TSAN_ENABLED 1 +#endif +#endif // __has_feature + +#define UNUSED GGML_UNUSED +#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) + +#if defined(GGML_USE_ACCELERATE) +#include +#endif + +// floating point type used to accumulate sums +typedef double ggml_float; + +#define GGML_GELU_FP16 +#define GGML_GELU_QUICK_FP16 + +#define GGML_SOFT_MAX_UNROLL 4 +#define GGML_VEC_DOT_UNROLL 2 +#define GGML_VEC_MAD_UNROLL 32 + +// +// global data +// + +// precomputed gelu table for f16 (128 KB) +static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; + +// precomputed quick gelu table for f16 (128 KB) +static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; + +#if defined(__ARM_ARCH) +struct ggml_arm_arch_features_type { + int has_neon; + int has_i8mm; + int has_sve; + int sve_cnt; +} ggml_arm_arch_features = {-1, -1, -1, 0}; +#endif + + +#if defined(_WIN32) + +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include + + +#if !defined(__clang__) +#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) + +typedef volatile LONG atomic_int; +typedef atomic_int atomic_bool; +typedef atomic_int atomic_flag; + +#define ATOMIC_FLAG_INIT 0 + +typedef enum { + memory_order_relaxed, + memory_order_consume, + memory_order_acquire, + memory_order_release, + memory_order_acq_rel, + memory_order_seq_cst +} memory_order; + +static void atomic_store(atomic_int * ptr, LONG val) { + InterlockedExchange(ptr, val); +} +static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { + // TODO: add support for explicit memory order + InterlockedExchange(ptr, val); +} +static LONG atomic_load(atomic_int * ptr) { + return InterlockedCompareExchange(ptr, 0, 0); +} +static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { + // TODO: add support for explicit memory order + return InterlockedCompareExchange(ptr, 0, 0); +} +static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { + return InterlockedExchangeAdd(ptr, inc); +} +static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { + // TODO: add support for explicit memory order + return InterlockedExchangeAdd(ptr, inc); +} +static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { + return InterlockedExchange(ptr, 1); +} +static void atomic_flag_clear(atomic_flag * ptr) { + InterlockedExchange(ptr, 0); +} +static void atomic_thread_fence(memory_order mo) { + MemoryBarrier(); +} +#else // clang +#include +#endif + +typedef HANDLE pthread_t; + +typedef DWORD thread_ret_t; +static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { + (void) unused; + HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); + if (handle == NULL) + { + return EAGAIN; + } + + *out = handle; + return 0; +} + +static int pthread_join(pthread_t thread, void * unused) { + (void) unused; + int ret = (int) WaitForSingleObject(thread, INFINITE); + CloseHandle(thread); + return ret; +} + +static int sched_yield (void) { + Sleep (0); + return 0; +} +#else + +#include +#include +#include +#if defined(__FreeBSD__) +#include +#endif + +typedef void * thread_ret_t; + +#include +#include +#include + +#endif + +typedef pthread_t ggml_thread_t; + +#ifdef GGML_USE_CPU_HBM +#include +#endif + +#if defined(__APPLE__) +#include +#include +#include +#endif + +// +// cache line +// + +#if defined(__cpp_lib_hardware_interference_size) +#define CACHE_LINE_SIZE hardware_destructive_interference_size +#else +#if defined(__POWER9_VECTOR__) +#define CACHE_LINE_SIZE 128 +#else +#define CACHE_LINE_SIZE 64 +#endif +#endif + +static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); + + +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); + +static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = { + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, + .vec_dot_type = GGML_TYPE_F32, + .nrows = 1, + }, + [GGML_TYPE_F16] = { + .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, + .vec_dot_type = GGML_TYPE_F16, + .nrows = 1, + }, + [GGML_TYPE_Q4_0] = { + .from_float = quantize_row_q4_0, + .vec_dot = ggml_vec_dot_q4_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q4_1] = { + .from_float = quantize_row_q4_1, + .vec_dot = ggml_vec_dot_q4_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q5_0] = { + .from_float = quantize_row_q5_0, + .vec_dot = ggml_vec_dot_q5_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, + [GGML_TYPE_Q5_1] = { + .from_float = quantize_row_q5_1, + .vec_dot = ggml_vec_dot_q5_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, + }, + [GGML_TYPE_Q8_0] = { + .from_float = quantize_row_q8_0, + .from_float_to_mat = quantize_mat_q8_0, + .vec_dot = ggml_vec_dot_q8_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q8_1] = { + .from_float = quantize_row_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, + }, + [GGML_TYPE_Q2_K] = { + .from_float = quantize_row_q2_K, + .vec_dot = ggml_vec_dot_q2_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q3_K] = { + .from_float = quantize_row_q3_K, + .vec_dot = ggml_vec_dot_q3_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q4_K] = { + .from_float = quantize_row_q4_K, + .vec_dot = ggml_vec_dot_q4_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q5_K] = { + .from_float = quantize_row_q5_K, + .vec_dot = ggml_vec_dot_q5_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q6_K] = { + .from_float = quantize_row_q6_K, + .vec_dot = ggml_vec_dot_q6_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_XXS] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_XS] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq2_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ3_XXS] = { + // NOTE: from_float for iq3 and iq2_s was removed because these quants require initialization in ggml_quantize_init + //.from_float = quantize_row_iq3_xxs, + .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ3_S] = { + //.from_float = quantize_row_iq3_s, + .vec_dot = ggml_vec_dot_iq3_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_S] = { + //.from_float = quantize_row_iq2_s, + .vec_dot = ggml_vec_dot_iq2_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ1_S] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq1_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ1_M] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq1_m_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ4_NL] = { + .from_float = quantize_row_iq4_nl, + .vec_dot = ggml_vec_dot_iq4_nl_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, + [GGML_TYPE_IQ4_XS] = { + .from_float = quantize_row_iq4_xs, + .vec_dot = ggml_vec_dot_iq4_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q8_K] = { + .from_float = quantize_row_q8_K, + }, + [GGML_TYPE_BF16] = { + .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, + .vec_dot_type = GGML_TYPE_BF16, + .nrows = 1, + }, + [GGML_TYPE_Q4_0_4_4] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_q4_0_4x4_q8_0, + .gemm = ggml_gemm_q4_0_4x4_q8_0, + }, + [GGML_TYPE_Q4_0_4_8] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_q4_0_4x8_q8_0, + .gemm = ggml_gemm_q4_0_4x8_q8_0, + }, + [GGML_TYPE_Q4_0_8_8] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 8, + .gemv = ggml_gemv_q4_0_8x8_q8_0, + .gemm = ggml_gemm_q4_0_8x8_q8_0, + }, + [GGML_TYPE_TQ1_0] = { + .from_float = quantize_row_tq1_0, + .vec_dot = ggml_vec_dot_tq1_0_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_TQ2_0] = { + .from_float = quantize_row_tq2_0, + .vec_dot = ggml_vec_dot_tq2_0_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, +}; + +const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) { + return &type_traits_cpu[type]; +} + +// +// simd mappings +// + +// we define a common set of C macros which map to specific intrinsics based on the current architecture +// we then implement the fundamental computation operations below using only these macros +// adding support for new architectures requires to define the corresponding SIMD macros +// +// GGML_F32_STEP / GGML_F16_STEP +// number of elements to process in a single step +// +// GGML_F32_EPR / GGML_F16_EPR +// number of elements to fit in a single register +// + +#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) + +#define GGML_SIMD + +// F32 NEON + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 float32x4_t +#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) +#define GGML_F32x4_SET1(x) vdupq_n_f32(x) +#define GGML_F32x4_LOAD vld1q_f32 +#define GGML_F32x4_STORE vst1q_f32 +#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) +#define GGML_F32x4_ADD vaddq_f32 +#define GGML_F32x4_MUL vmulq_f32 +#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + #define GGML_F16_STEP 32 + #define GGML_F16_EPR 8 + + #define GGML_F16x8 float16x8_t + #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) + #define GGML_F16x8_SET1(x) vdupq_n_f16(x) + #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) + #define GGML_F16x8_STORE vst1q_f16 + #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) + #define GGML_F16x8_ADD vaddq_f16 + #define GGML_F16x8_MUL vmulq_f16 + #define GGML_F16x8_REDUCE(res, x) \ + do { \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ + const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ + (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ + } while (0) + + #define GGML_F16_VEC GGML_F16x8 + #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO + #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) + #define GGML_F16_VEC_FMA GGML_F16x8_FMA + #define GGML_F16_VEC_ADD GGML_F16x8_ADD + #define GGML_F16_VEC_MUL GGML_F16x8_MUL + #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE +#else + // if FP16 vector arithmetic is not supported, we use FP32 instead + // and take advantage of the vcvt_ functions to convert to/from FP16 + + #define GGML_F16_STEP 16 + #define GGML_F16_EPR 4 + + #define GGML_F32Cx4 float32x4_t + #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) + #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) + #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) + #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) + #define GGML_F32Cx4_ADD vaddq_f32 + #define GGML_F32Cx4_MUL vmulq_f32 + #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + + #define GGML_F16_VEC GGML_F32Cx4 + #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO + #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) + #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA + #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD + #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL + #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE +#endif + +#elif defined(__AVX512F__) + +#define GGML_SIMD + +// F32 AVX512 + +#define GGML_F32_STEP 64 +#define GGML_F32_EPR 16 + +#define GGML_F32x16 __m512 +#define GGML_F32x16_ZERO _mm512_setzero_ps() +#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) +#define GGML_F32x16_LOAD _mm512_loadu_ps +#define GGML_F32x16_STORE _mm512_storeu_ps +// _mm512_fmadd_ps is defined in AVX512F so no guard is required +#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32x16_ADD _mm512_add_ps +#define GGML_F32x16_MUL _mm512_mul_ps +#define GGML_F32x16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = _mm512_reduce_add_ps(x[0]); \ +} while (0) + +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x16 +#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD +#define GGML_F32_VEC_STORE GGML_F32x16_STORE +#define GGML_F32_VEC_FMA GGML_F32x16_FMA +#define GGML_F32_VEC_ADD GGML_F32x16_ADD +#define GGML_F32_VEC_MUL GGML_F32x16_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE + +// F16 AVX512 + +// F16 AVX + +#define GGML_F16_STEP 64 +#define GGML_F16_EPR 16 + +// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead + +#define GGML_F32Cx16 __m512 +#define GGML_F32Cx16_ZERO _mm512_setzero_ps() +#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) + +// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F +// so F16C guard isn't required +#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) +#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) + +#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32Cx16_ADD _mm512_add_ps +#define GGML_F32Cx16_MUL _mm512_mul_ps +#define GGML_F32Cx16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = _mm512_reduce_add_ps(x[0]); \ +} while (0) + +#define GGML_F16_VEC GGML_F32Cx16 +#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE + +#elif defined(__AVX__) + +#define GGML_SIMD + +// F32 AVX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO _mm256_setzero_ps() +#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) +#define GGML_F32x8_LOAD _mm256_loadu_ps +#define GGML_F32x8_STORE _mm256_storeu_ps +#if defined(__FMA__) + #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) +#else + #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) +#endif +#define GGML_F32x8_ADD _mm256_add_ps +#define GGML_F32x8_MUL _mm256_mul_ps +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ + _mm256_extractf128_ps(x[0], 1)); \ + const __m128 t1 = _mm_hadd_ps(t0, t0); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 AVX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO _mm256_setzero_ps() +#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) + +#if defined(__F16C__) +// the _mm256_cvt intrinsics require F16C +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) +#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) +#else +static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { + float arr[8]; + + _mm256_storeu_ps(arr, y); + + for (int i = 0; i < 8; i++) + x[i] = GGML_FP32_TO_FP16(arr[i]); +} +#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) +#endif + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD _mm256_add_ps +#define GGML_F32Cx8_MUL _mm256_mul_ps +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__POWER9_VECTOR__) + +#define GGML_SIMD + +// F32 POWER9 + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 vector float +#define GGML_F32x4_ZERO 0.0f +#define GGML_F32x4_SET1 vec_splats +#define GGML_F32x4_LOAD(p) vec_xl(0, p) +#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) +#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) +#define GGML_F32x4_ADD vec_add +#define GGML_F32x4_MUL vec_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + res = vec_extract(x[0], 0) + \ + vec_extract(x[0], 1) + \ + vec_extract(x[0], 2) + \ + vec_extract(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 POWER9 +#define GGML_F16_STEP GGML_F32_STEP +#define GGML_F16_EPR GGML_F32_EPR +#define GGML_F16_VEC GGML_F32x4 +#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE +// Use vec_xl, not vec_ld, in case the load address is not aligned. +#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ + vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ + vec_extract_fp32_from_shortl(vec_xl(0, p)) +#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] +#define GGML_F16_VEC_STORE(p, r, i) \ + if (i & 0x1) \ + vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ + r[i - GGML_ENDIAN_BYTE(0)]), \ + 0, p - GGML_F16_EPR) + +#elif defined(__wasm_simd128__) + +#define GGML_SIMD + +// F32 WASM + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 v128_t +#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F32x4_LOAD wasm_v128_load +#define GGML_F32x4_STORE wasm_v128_store +#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) +#define GGML_F32x4_ADD wasm_f32x4_add +#define GGML_F32x4_MUL wasm_f32x4_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 WASM + +#define GGML_F16_STEP 16 +#define GGML_F16_EPR 4 + +inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(p[0]); + tmp[1] = GGML_FP16_TO_FP32(p[1]); + tmp[2] = GGML_FP16_TO_FP32(p[2]); + tmp[3] = GGML_FP16_TO_FP32(p[3]); + + return wasm_v128_load(tmp); +} + +inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { + float tmp[4]; + + wasm_v128_store(tmp, x); + + p[0] = GGML_FP32_TO_FP16(tmp[0]); + p[1] = GGML_FP32_TO_FP16(tmp[1]); + p[2] = GGML_FP32_TO_FP16(tmp[2]); + p[3] = GGML_FP32_TO_FP16(tmp[3]); +} + +#define GGML_F16x4 v128_t +#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) +#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) +#define GGML_F16x4_FMA GGML_F32x4_FMA +#define GGML_F16x4_ADD wasm_f32x4_add +#define GGML_F16x4_MUL wasm_f32x4_mul +#define GGML_F16x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3); \ +} + +#define GGML_F16_VEC GGML_F16x4 +#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F16x4_FMA +#define GGML_F16_VEC_ADD GGML_F16x4_ADD +#define GGML_F16_VEC_MUL GGML_F16x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE + +#elif defined(__SSE3__) + +#define GGML_SIMD + +// F32 SSE + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO _mm_setzero_ps() +#define GGML_F32x4_SET1(x) _mm_set1_ps(x) +#define GGML_F32x4_LOAD _mm_loadu_ps +#define GGML_F32x4_STORE _mm_storeu_ps +#if defined(__FMA__) + // TODO: Does this work? + #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) +#else + #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) +#endif +#define GGML_F32x4_ADD _mm_add_ps +#define GGML_F32x4_MUL _mm_mul_ps +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ +} +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 SSE + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return _mm_loadu_ps(tmp); +} + +static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { + float arr[4]; + + _mm_storeu_ps(arr, y); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO _mm_setzero_ps() +#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) +#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD _mm_add_ps +#define GGML_F32Cx4_MUL _mm_mul_ps +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#elif defined(__loongarch_asx) + +#define GGML_SIMD + +// F32 LASX +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) +#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) +#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) +#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) +#define GGML_F32x8_ADD __lasx_xvfadd_s +#define GGML_F32x8_MUL __lasx_xvfmul_s +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + float *tmp_p = (float *)&x[0]; \ + res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 LASX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) + +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return (__m256)__lasx_xvld(tmp, 0); +} +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { + float arr[8]; + + __lasx_xvst(y, arr, 0); + + for (int i = 0; i < 8; i++) { + x[i] = GGML_FP32_TO_FP16(arr[i]); + } +} +#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD __lasx_xvfadd_s +#define GGML_F32Cx8_MUL __lasx_xvfmul_s +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__loongarch_sx) + +#define GGML_SIMD + +// F32 LSX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO __lsx_vldi(0) +#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) +#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) +#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) +#define GGML_F32x4_ADD __lsx_vfadd_s +#define GGML_F32x4_MUL __lsx_vfmul_s +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i)t0, 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 LSX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return __lsx_vld(tmp, 0); +} + +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { + float arr[4]; + + __lsx_vst(y, arr, 0); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO __lsx_vldi(0) +#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD __lsx_vfadd_s +#define GGML_F32Cx4_MUL __lsx_vfmul_s +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#endif + +// GGML_F32_ARR / GGML_F16_ARR +// number of registers to use per step +#ifdef GGML_SIMD +#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) +#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) +#endif + +// +// Threading defs +// + +typedef pthread_t ggml_thread_t; + +#if defined(_WIN32) + +typedef CONDITION_VARIABLE ggml_cond_t; +typedef SRWLOCK ggml_mutex_t; + +#define ggml_mutex_init(m) InitializeSRWLock(m) +#define ggml_mutex_destroy(m) +#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) +#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) +#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) +#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) + +#define ggml_cond_init(c) InitializeConditionVariable(c) +#define ggml_cond_destroy(c) +#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) +#define ggml_cond_broadcast(c) WakeAllConditionVariable(c) + +#define ggml_thread_create pthread_create +#define ggml_thread_join pthread_join + +#else + +typedef pthread_cond_t ggml_cond_t; +typedef pthread_mutex_t ggml_mutex_t; + +#define ggml_mutex_init(m) pthread_mutex_init(m, NULL) +#define ggml_mutex_destroy(m) pthread_mutex_destroy(m) +#define ggml_mutex_lock(m) pthread_mutex_lock(m) +#define ggml_mutex_unlock(m) pthread_mutex_unlock(m) +#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) +#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) + +#define ggml_lock_init(x) UNUSED(x) +#define ggml_lock_destroy(x) UNUSED(x) +#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) +#define ggml_lock_lock(x) _mm_pause() +#else +#define ggml_lock_lock(x) UNUSED(x) +#endif +#define ggml_lock_unlock(x) UNUSED(x) + +#define GGML_LOCK_INITIALIZER 0 +#define ggml_cond_init(c) pthread_cond_init(c, NULL) +#define ggml_cond_destroy(c) pthread_cond_destroy(c) +#define ggml_cond_wait(c, m) pthread_cond_wait(c, m) +#define ggml_cond_broadcast(c) pthread_cond_broadcast(c) + +#define ggml_thread_create pthread_create +#define ggml_thread_join pthread_join + +#endif + +// Threadpool def +struct ggml_threadpool { + ggml_mutex_t mutex; // mutex for cond.var + ggml_cond_t cond; // cond.var for waiting for new work + + struct ggml_cgraph * cgraph; + struct ggml_cplan * cplan; + + // synchronization primitives + atomic_int n_graph; // incremented when there is work to be done (i.e each graph) + atomic_int GGML_CACHE_ALIGN n_barrier; + atomic_int GGML_CACHE_ALIGN n_barrier_passed; + atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. + + // these are atomic as an annotation for thread-sanitizer + atomic_bool stop; // Used for stopping the threadpool altogether + atomic_bool pause; // Used for pausing the threadpool or individual threads + atomic_bool abort; // Used for aborting processing of a graph + + struct ggml_compute_state * workers; // per thread state + int n_threads_max; // number of threads in the pool + atomic_int n_threads_cur; // number of threads used in the current graph + + int32_t prio; // Scheduling priority + uint32_t poll; // Polling level (0 - no polling) + + enum ggml_status ec; +}; + +// Per-thread state +struct ggml_compute_state { +#ifndef GGML_USE_OPENMP + ggml_thread_t thrd; + bool cpumask[GGML_MAX_N_THREADS]; + int last_graph; + bool pending; +#endif + struct ggml_threadpool * threadpool; + int ith; +}; + +struct ggml_compute_params { + // ith = thread index, nth = number of threads + int ith, nth; + + // work buffer for all threads + size_t wsize; + void * wdata; + + struct ggml_threadpool * threadpool; +}; + +// +// fundamental operations +// + +inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } +inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } +inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } +inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } +inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } +inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } +inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } +inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } +inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } + +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + +#if defined(GGML_SIMD) + float sumf = 0.0f; + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F32_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += x[i]*y[i]; + } +#else + // scalar + ggml_float sumf = 0.0; + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(x[i]*y[i]); + } +#endif + + *s = sumf; +} + +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + int i = 0; + ggml_float sumf = 0; + +#if defined(__AVX512BF16__) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 64 <= n; i += 64) { + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#elif defined(__AVX512F__) +#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#undef LOAD +#elif defined(__AVX2__) || defined(__AVX__) +#if defined(__AVX2__) +#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) +#else +#define LOAD(p) _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)), (_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_bsrli_si128(_mm_loadu_si128((const __m128i *)(p)), 8)), 16)), 1)) +#endif + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + __m256 c4 = _mm256_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); + c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); + c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); + } + __m128 g; + c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), + _mm256_add_ps(c2, c4)); + g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), + _mm256_castps256_ps128(c1)); + g = _mm_add_ps(g, _mm_movehl_ps(g, g)); + g = _mm_add_ss(g, _mm_movehdup_ps(g)); + sumf += (ggml_float)_mm_cvtss_f32(g); + +#undef LOAD +#endif + + for (; i < n; ++i) { + sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * + GGML_BF16_TO_FP32(y[i])); + } + *s = sumf; +} + +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + ggml_float sumf = 0.0; + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F16_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#else + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#endif + + *s = sumf; +} + +// compute GGML_VEC_DOT_UNROLL dot products at once +// xs - x row stride in bytes +inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { + ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; + + ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); + + sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); + } + } + } + + // reduce sum0..sum3 to sum0 + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + GGML_F16_VEC_REDUCE(sumf[k], sum[k]); + } + + // leftovers + for (int i = np; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#else + for (int i = 0; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#endif + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + s[i] = sumf[i]; + } +} + +inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] += x[i]*v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] += x[i]*v; + } +#endif +} + +inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#endif +} + +// xs and vs are byte strides of x and v +inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { + + const float * restrict x[GGML_VEC_MAD_UNROLL]; + const float * restrict v[GGML_VEC_MAD_UNROLL]; + + for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { + x[i] = (const float *) ((const char *) xv + i*xs); + v[i] = (const float *) ((const char *) vv + i*vs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + vx[k] = GGML_F32_VEC_SET1(v[k][0]); + } + + GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); + } + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = np; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#else + // scalar + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = 0; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#endif +} + +//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } +inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { +#if defined(GGML_USE_ACCELERATE) + vDSP_vsmul(y, 1, &v, y, 1, n); +#elif defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_MUL(ay[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] *= v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] *= v; + } +#endif +} + +inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_MUL(ay[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#endif +} + +inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } +inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } +inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } +inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } +inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } +inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } +inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } +inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } +inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } +inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } +inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } +inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } +inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } +// TODO: optimize performance +inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } + +static const float GELU_COEF_A = 0.044715f; +static const float GELU_QUICK_COEF = -1.702f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + +inline static float ggml_gelu_f32(float x) { + return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + +inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + const uint16_t * i16 = (const uint16_t *) x; + for (int i = 0; i < n; ++i) { + y[i] = ggml_table_gelu_f16[i16[i]]; + } +} + +#ifdef GGML_GELU_FP16 +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + if (x[i] <= -10.0f) { + y[i] = 0.0f; + } else if (x[i] >= 10.0f) { + y[i] = x[i]; + } else { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + } + } +} +#else +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_f32(x[i]); + } +} +#endif + +inline static float ggml_gelu_quick_f32(float x) { + return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); +} + +//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { +// const uint16_t * i16 = (const uint16_t *) x; +// for (int i = 0; i < n; ++i) { +// y[i] = ggml_table_gelu_quick_f16[i16[i]]; +// } +//} + +#ifdef GGML_GELU_QUICK_FP16 +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); + } +} +#else +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]); + } +} +#endif + +// Sigmoid Linear Unit (SiLU) function +inline static float ggml_silu_f32(float x) { + return x/(1.0f + expf(-x)); +} + +#if __FINITE_MATH_ONLY__ +#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" +#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" +#endif + +#if defined(__ARM_NEON) && defined(__aarch64__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON + +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) +#else +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +static void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { + y[i] = ggml_silu_f32(x[i]); + } +} + +static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); +#endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } +#endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} + +static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { + // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) + + int i = 0; + ggml_float sum = 0; + for (; i < n; ++i) { + float val = x[i] - max; + y[i] = val; + sum += (ggml_float)expf(val); + } + return sum = (ggml_float)logf(sum); +} + +inline static float ggml_silu_backward_f32(float x, float dy) { + const float s = 1.0f/(1.0f + expf(-x)); + return dy*s*(1.0f + x*(1.0f - s)); +} + +inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { + for (int i = 0; i < n; ++i) { + dx[i] = ggml_silu_backward_f32(x[i], dy[i]); + } +} + +inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = sum; +#else + vDSP_sve(x, 1, s, n); +#endif +} + +inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = sum; +} + +inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_FP16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_BF16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + float max = -INFINITY; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + } + *s = max; +#else + vDSP_maxv(x, 1, s, n); +#endif +} + +inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { + ggml_vec_norm_f32(n, s, x); + *s = 1.f/(*s); +} + +inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { + float max = -INFINITY; + int idx = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + if (max == x[i]) { idx = i; } + } + *s = idx; +} + +// Helpers for polling loops +#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) +static inline void ggml_thread_cpu_relax(void) { + __asm__ volatile("yield" ::: "memory"); +} +#elif defined(__x86_64__) +static inline void ggml_thread_cpu_relax(void) { + _mm_pause(); +} +#else +static inline void ggml_thread_cpu_relax(void) {;} +#endif + +// +// NUMA support +// + +#define GGML_NUMA_MAX_NODES 8 +#define GGML_NUMA_MAX_CPUS 512 + +struct ggml_numa_node { + uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node + uint32_t n_cpus; +}; + +struct ggml_numa_nodes { + enum ggml_numa_strategy numa_strategy; + struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; + uint32_t n_nodes; + uint32_t total_cpus; // hardware threads on system + uint32_t current_node; // node on which main process is execting +#if defined(__gnu_linux__) + cpu_set_t cpuset; // cpuset from numactl +#else + uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype +#endif +}; + +// +// ggml state +// + +struct ggml_state { + struct ggml_numa_nodes numa; +}; + +static struct ggml_state g_state = {0}; + +static void ggml_barrier(struct ggml_threadpool * tp) { + int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); + if (n_threads == 1) { + return; + } + +#ifdef GGML_USE_OPENMP + #pragma omp barrier +#else + int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); + + // enter barrier (full seq-cst fence) + int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); + + if (n_barrier == (n_threads - 1)) { + // last thread + atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); + + // exit barrier (fill seq-cst fence) + atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); + return; + } + + // wait for other threads + while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { + ggml_thread_cpu_relax(); + } + + // exit barrier (full seq-cst fence) + // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead + #ifdef GGML_TSAN_ENABLED + atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); + #else + atomic_thread_fence(memory_order_seq_cst); + #endif +#endif +} + +#if defined(__gnu_linux__) +static cpu_set_t ggml_get_numa_affinity(void) { + cpu_set_t cpuset; + pthread_t thread; + thread = pthread_self(); + CPU_ZERO(&cpuset); + pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); + return cpuset; +} +#else +static uint32_t ggml_get_numa_affinity(void) { + return 0; // no NUMA support +} +#endif + +void ggml_numa_init(enum ggml_numa_strategy numa_flag) { + if (g_state.numa.n_nodes > 0) { + fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); + + return; + } + +#if defined(__gnu_linux__) + struct stat st; + char path[256]; + int rv; + + // set numa scheme + g_state.numa.numa_strategy = numa_flag; + + GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); + + g_state.numa.cpuset = ggml_get_numa_affinity(); + + // enumerate nodes + while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.n_nodes; + } + + // enumerate CPUs + while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.total_cpus; + } + + GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); + + // figure out which node we're on + uint current_cpu; + int getcpu_ret = 0; +#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 33) || defined(__COSMOPOLITAN__) + getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); +#else + // old glibc doesn't have a wrapper for this call. Fall back on direct syscall +# if !defined(SYS_getcpu) && defined(SYS_get_cpu) +# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name +# endif + getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); +#endif + + if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { + g_state.numa.n_nodes = 0; + return; + } + + GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); + + for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { + struct ggml_numa_node * node = &g_state.numa.nodes[n]; + GGML_PRINT_DEBUG("CPUs on node %u:", n); + node->n_cpus = 0; + for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) == 0) { + node->cpus[node->n_cpus++] = c; + GGML_PRINT_DEBUG(" %u", c); + } + } + GGML_PRINT_DEBUG("\n"); + } + + if (ggml_is_numa()) { + FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); + if (fptr != NULL) { + char buf[42]; + if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { + GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); + } + fclose(fptr); + } + } +#else + UNUSED(numa_flag); + // TODO +#endif +} + +bool ggml_is_numa(void) { + return g_state.numa.n_nodes > 1; +} + +#if defined(__ARM_ARCH) + +#if defined(__linux__) && defined(__aarch64__) +#include +#elif defined(__APPLE__) +#include +#endif + +#if !defined(HWCAP2_I8MM) +#define HWCAP2_I8MM 0 +#endif + +static void ggml_init_arm_arch_features(void) { +#if defined(__linux__) && defined(__aarch64__) + uint32_t hwcap = getauxval(AT_HWCAP); + uint32_t hwcap2 = getauxval(AT_HWCAP2); + + ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); + ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); + ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); +#endif +#elif defined(__APPLE__) + int oldp = 0; + size_t size = sizeof(oldp); + if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_neon = oldp; + + if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_i8mm = oldp; + + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#else +// Run-time CPU feature detection not implemented for this platform, fallback to compile time +#if defined(__ARM_NEON) + ggml_arm_arch_features.has_neon = 1; +#else + ggml_arm_arch_features.has_neon = 0; +#endif + +#if defined(__ARM_FEATURE_MATMUL_INT8) + ggml_arm_arch_features.has_i8mm = 1; +#else + ggml_arm_arch_features.has_i8mm = 0; +#endif + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.has_sve = 1; + ggml_arm_arch_features.sve_cnt = 16; +#else + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#endif +#endif +} +#endif + +struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { + GGML_ASSERT(!ggml_get_no_alloc(ctx)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); + + ggml_set_i32(result, value); + + return result; +} + +struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { + GGML_ASSERT(!ggml_get_no_alloc(ctx)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); + + ggml_set_f32(result, value); + + return result; +} + +struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { + const int n = ggml_nrows(tensor); + const int nc = tensor->ne[0]; + const size_t n1 = tensor->nb[1]; + + char * const data = tensor->data; + + switch (tensor->type) { + case GGML_TYPE_I8: + { + assert(tensor->nb[0] == sizeof(int8_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I16: + { + assert(tensor->nb[0] == sizeof(int16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I32: + { + assert(tensor->nb[0] == sizeof(int32_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_F16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); + } + } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; + case GGML_TYPE_F32: + { + assert(tensor->nb[0] == sizeof(float)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f32(nc, (float *)(data + i*n1), value); + } + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + return tensor; +} + +struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { + const int n = ggml_nrows(tensor); + const int nc = tensor->ne[0]; + const size_t n1 = tensor->nb[1]; + + char * const data = tensor->data; + + switch (tensor->type) { + case GGML_TYPE_I8: + { + assert(tensor->nb[0] == sizeof(int8_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I16: + { + assert(tensor->nb[0] == sizeof(int16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I32: + { + assert(tensor->nb[0] == sizeof(int32_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_F16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); + } + } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_bf16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; + case GGML_TYPE_F32: + { + assert(tensor->nb[0] == sizeof(float)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f32(nc, (float *)(data + i*n1), value); + } + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + return tensor; +} + +int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); + return ((int8_t *)(tensor->data))[i]; + } + case GGML_TYPE_I16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); + return ((int16_t *)(tensor->data))[i]; + } + case GGML_TYPE_I32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); + return ((int32_t *)(tensor->data))[i]; + } + case GGML_TYPE_F16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); + return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); + } + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } + case GGML_TYPE_F32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(float)); + return ((float *)(tensor->data))[i]; + } + default: + { + GGML_ABORT("fatal error"); + } + } +} + +void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); + return; + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); + ((int8_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); + ((int16_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); + ((int32_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_F16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); + ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(float)); + ((float *)(tensor->data))[i] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + return ((int8_t *) data)[0]; + case GGML_TYPE_I16: + return ((int16_t *) data)[0]; + case GGML_TYPE_I32: + return ((int32_t *) data)[0]; + case GGML_TYPE_F16: + return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); + case GGML_TYPE_F32: + return ((float *) data)[0]; + default: + GGML_ABORT("fatal error"); + } +} + +void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(data))[0] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(data))[0] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(data))[0] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(data))[0] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + return ((int8_t *)(tensor->data))[i]; + } + case GGML_TYPE_I16: + { + return ((int16_t *)(tensor->data))[i]; + } + case GGML_TYPE_I32: + { + return ((int32_t *)(tensor->data))[i]; + } + case GGML_TYPE_F16: + { + return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); + } + case GGML_TYPE_BF16: + { + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } + case GGML_TYPE_F32: + { + return ((float *)(tensor->data))[i]; + } + default: + { + GGML_ABORT("fatal error"); + } + } +} + +void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); + return; + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(tensor->data))[i] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + return ((int8_t *) data)[0]; + case GGML_TYPE_I16: + return ((int16_t *) data)[0]; + case GGML_TYPE_I32: + return ((int32_t *) data)[0]; + case GGML_TYPE_F16: + return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); + case GGML_TYPE_F32: + return ((float *) data)[0]; + default: + GGML_ABORT("fatal error"); + } +} + +void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(data))[0] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(data))[0] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(data))[0] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(data))[0] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +//////////////////////////////////////////////////////////////////////////////// + +// ggml_compute_forward_dup + +static void ggml_compute_forward_dup_same_cont( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == dst->type); + + const size_t nb0 = ggml_type_size(src0->type); + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by elements + const int ne = ggml_nelements(dst); + const int dr = (ne + nth - 1) / nth; + const int ie0 = dr * ith; + const int ie1 = MIN(ie0 + dr, ne); + + if (ie0 < ie1) { + memcpy( + ((char *) dst->data + ie0*nb0), + ((char *) src0->data + ie0*nb0), + (ie1 - ie0) * nb0); + } +} + +static void ggml_compute_forward_dup_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_fp16_t)) { + if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_bf16_t)) { + if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + // TODO: simplify + if (nb00 == sizeof(float)) { + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + quantize_row_q(src0_ptr, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(float)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. +static void ggml_compute_forward_dup_bytes( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(src0->type == dst->type); + + GGML_TENSOR_UNARY_OP_LOCALS; + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { + ggml_compute_forward_dup_same_cont(params, dst); + return; + } + + const size_t type_size = ggml_type_size(src0->type); + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == type_size && nb0 == type_size) { + // copy by rows + const size_t rs = ne00 * type_size; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + size_t id = 0; + char * dst_ptr = (char *) dst->data; + const size_t rs = ne00 * type_size; + + if (nb00 == type_size) { + // src0 is contigous on first dimension, copy by rows + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, type_size); + + id += type_size; + } + } + id += rs * (ne01 - ir1); + } + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, type_size); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } +} + +static void ggml_compute_forward_dup( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (src0->type == dst->type) { + ggml_compute_forward_dup_bytes(params, dst); + return; + } + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_dup_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_dup_bf16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_dup_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add + +static void ggml_compute_forward_add_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; + } + } + } +} + +static void ggml_compute_forward_add_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + } + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + if (dst->type == GGML_TYPE_F16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + } + + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + if (dst->type == GGML_TYPE_BF16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_f16_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(ggml_fp16_t)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(ggml_bf16_t)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + const enum ggml_type dtype = dst->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dtype)->from_float; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 indices + const int i03 = ir/(ne02*ne01); + const int i02 = (ir - i03*ne02*ne01)/ne01; + const int i01 = (ir - i03*ne02*ne01 - i02*ne01); + + // src1 and dst are same shape as src0 => same indices + const int i13 = i03; + const int i12 = i02; + const int i11 = i01; + + const int i3 = i03; + const int i2 = i02; + const int i1 = i01; + + void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); + void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + assert(ne00 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne00); + // add src1 + ggml_vec_acc_f32(ne00, wdata, src1_row); + // quantize row to dst + if (quantize_row_q != NULL) { + quantize_row_q(wdata, dst_row, ne00); + } else { + memcpy(dst_row, wdata, ne0*nb0); + } + } +} + +static void ggml_compute_forward_add( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add_f16_f16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_bf16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_add_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add1 + +static void ggml_compute_forward_add1_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_add1_f32); + + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, + (float *) ((char *) src1->data), 0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, + ne0); +#else + ggml_vec_add1_f32(ne0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), + *(float *) src1->data); +#endif + } +} + +static void ggml_compute_forward_add1_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_f16_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(type)->from_float; + + // we don't support permuted src0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(dst->type == src0->type); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); + void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); + + assert(ne0 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne0); + // add src1 + ggml_vec_acc1_f32(ne0, wdata, v); + // quantize row to dst + quantize_row_q(wdata, dst_row, ne0); + } +} + +static void ggml_compute_forward_add1_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add1_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add1_f16_f16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_f16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add1_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_bf16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_add1_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_acc + +static void ggml_compute_forward_acc_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during acc + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during acc + const size_t nb0 = ggml_element_size(src0); + + const size_t nb00 = nb0; + const size_t nb01 = nb1; + const size_t nb02 = nb2; + const size_t nb03 = nb3; + + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + +#ifdef GGML_USE_ACCELERATE + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); +#else + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); +#endif + } +} + +static void ggml_compute_forward_acc( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_acc_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sub + +static void ggml_compute_forward_sub_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; + } + } + } +} + +static void ggml_compute_forward_sub( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sub_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mul + +static void ggml_compute_forward_mul_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (nb10 == sizeof(float)) { + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0 ; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_mul_f32); + + vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne00; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); + } + } + } +} + +static void ggml_compute_forward_mul( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_mul_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_div + +static void ggml_compute_forward_div_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (nb10 == sizeof(float)) { + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_div_f32); + + vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne00; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); + } + } + } +} + +static void ggml_compute_forward_div( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_div_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sqr + +static void ggml_compute_forward_sqr_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sqr_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sqr( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sqr_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sqrt + +static void ggml_compute_forward_sqrt_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sqrt_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sqrt( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sqrt_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_log + +static void ggml_compute_forward_log_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_log_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_log( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_log_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sin + +static void ggml_compute_forward_sin_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sin_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sin( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sin_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cos + +static void ggml_compute_forward_cos_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_cos_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_cos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cos_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum + +static void ggml_compute_forward_sum_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + ggml_float sum = 0; + ggml_float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32_ggf(ne00, + &row_sum, + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + sum += row_sum; + } + } + } + ((float *) dst->data)[0] = sum; +} + +static void ggml_compute_forward_sum_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_fp16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f16_ggf(ne00, + &row_sum, + (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); +} + +static void ggml_compute_forward_sum_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_bf16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_bf16_ggf(ne00, + &row_sum, + (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); +} + +static void ggml_compute_forward_sum( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_sum_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_sum_bf16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum_rows + +static void ggml_compute_forward_sum_rows_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne0 == 1); + GGML_ASSERT(ne1 == ne01); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + for (int64_t i3 = 0; i3 < ne03; i3++) { + for (int64_t i2 = 0; i2 < ne02; i2++) { + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); + float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); + float row_sum = 0; + ggml_vec_sum_f32(ne00, &row_sum, src_row); + dst_row[0] = row_sum; + } + } + } +} + +static void ggml_compute_forward_sum_rows( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mean + +static void ggml_compute_forward_mean_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + assert(ne0 == 1); + assert(ne1 == ne01); + assert(ne2 == ne02); + assert(ne3 == ne03); + + UNUSED(ne0); + UNUSED(ne1); + UNUSED(ne2); + UNUSED(ne3); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32(ne00, + (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + + *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; + } + } + } +} + +static void ggml_compute_forward_mean( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_mean_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argmax + +static void ggml_compute_forward_argmax_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + assert(dst->nb[0] == sizeof(float)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + const size_t nb01 = src0->nb[1]; + const size_t nb0 = dst->nb[0]; + + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src = (float *) ((char *) src0->data + i1*nb01); + int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); + int v = 0; + ggml_vec_argmax_f32(ne00, &v, src); + dst_[0] = v; + } +} + +static void ggml_compute_forward_argmax( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argmax_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_count_equal + +static void ggml_compute_forward_count_equal_i32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + GGML_ASSERT(src0->type == GGML_TYPE_I32); + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_I64); + + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + int64_t * sums = (int64_t *) params->wdata; + int64_t sum_thread = 0; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir / (ne02*ne01); + const int64_t i02 = (ir - i03*ne03) / ne01; + const int64_t i01 = ir - i03*ne03 - i02*ne02; + + const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; + const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; + + for (int64_t i00 = 0; i00 < ne00; ++i00) { + const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); + const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); + + sum_thread += val0 == val1; + } + } + if (ith != 0) { + sums[ith] = sum_thread; + } + ggml_barrier(params->threadpool); + + if (ith != 0) { + return; + } + + for (int ith_other = 1; ith_other < nth; ++ith_other) { + sum_thread += sums[ith_other]; + } + *((int64_t *) dst->data) = sum_thread; +} + +static void ggml_compute_forward_count_equal( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_I32: + { + ggml_compute_forward_count_equal_i32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat + +static void ggml_compute_forward_repeat_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_cpy_f32(ne00, + (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), + (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); + ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); + // ggml_vec_cpy_f16(ne00, y, x) + for (int i = 0; i < ne00; ++i) { + y[i] = x[i]; + } + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_I16: + { + ggml_compute_forward_repeat_f16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_repeat_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat_back + +static void ggml_compute_forward_repeat_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(dst, src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne00/ne0); + const int nr1 = (int)(ne01/ne1); + const int nr2 = (int)(ne02/ne2); + const int nr3 = (int)(ne03/ne3); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (ggml_is_contiguous(dst)) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } else { + for (int k3 = 0; k3 < ne3; k3++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int k1 = 0; k1 < ne1; k1++) { + ggml_vec_set_f32(ne0, + (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), + 0); + } + } + } + } + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne3; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne1; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_acc_f32(ne0, + (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), + (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_repeat_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_concat + +static void ggml_compute_forward_concat_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); + } + + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_concat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_concat_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_abs + +static void ggml_compute_forward_abs_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_abs_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_abs( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_abs_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sgn + +static void ggml_compute_forward_sgn_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_sgn_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sgn( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sgn_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_neg + +static void ggml_compute_forward_neg_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_neg_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_neg( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_neg_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_step + +static void ggml_compute_forward_step_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_step_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_step( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_step_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_tanh + +static void ggml_compute_forward_tanh_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_tanh_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_tanh( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_tanh_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_elu + +static void ggml_compute_forward_elu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_elu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_elu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_elu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_relu + +static void ggml_compute_forward_relu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_relu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_relu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_relu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sigmoid + +static void ggml_compute_forward_sigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_sigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sigmoid_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gelu + +static void ggml_compute_forward_gelu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gelu_quick + +static void ggml_compute_forward_gelu_quick_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_quick_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_quick( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_quick_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu + +static void ggml_compute_forward_silu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} +// ggml_compute_forward_leaky_relu + +static void ggml_compute_forward_leaky_relu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_relu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); + } +} + +static void ggml_compute_forward_leaky_relu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_relu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu_back + +static void ggml_compute_forward_silu_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * grad = dst->src[1]; + + assert(ggml_is_contiguous_1(grad)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + assert(ggml_are_same_shape(src0, grad)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_backward_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1])), + (float *) ((char *) grad->data + i1*(grad->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +static void ggml_compute_forward_hardswish_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_hardswish_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} +static void ggml_compute_forward_hardswish( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_hardswish_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_hardsigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_hardsigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_hardsigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_hardsigmoid_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_exp_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_exp_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_exp( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_exp_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_norm + +static void ggml_compute_forward_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps > 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)x[i00]; + } + + float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + ggml_float sum2 = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sum2 += (ggml_float)(v*v); + } + + float variance = sum2/ne00; + const float scale = 1.0f/sqrtf(variance + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +static void ggml_compute_forward_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_rms_norm + +static void ggml_compute_forward_rms_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps > 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)(x[i00] * x[i00]); + } + + const float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + memcpy(y, x, ne00 * sizeof(float)); + // for (int i00 = 0; i00 < ne00; i00++) { + // y[i00] = x[i00]; + // } + + const float scale = 1.0f/sqrtf(mean + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +static void ggml_compute_forward_rms_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_rms_norm_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + // src1 is same shape as src0 => same indices + const int64_t i11 = i01; + const int64_t i12 = i02; + const int64_t i13 = i03; + + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); + + ggml_float sum_xx = 0.0; + ggml_float sum_xdz = 0.0; + + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum_xx += (ggml_float)(x[i00] * x[i00]); + sum_xdz += (ggml_float)(x[i00] * dz[i00]); + } + + //const float mean = (float)(sum_xx)/ne00; + const float mean_eps = (float)(sum_xx)/ne00 + eps; + const float sum_eps = (float)(sum_xx) + eps*ne00; + //const float mean_xdz = (float)(sum_xdz)/ne00; + // we could cache rms from forward pass to improve performance. + // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. + //const float rms = sqrtf(mean_eps); + const float rrms = 1.0f / sqrtf(mean_eps); + //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) + + { + // z = rms_norm(x) + // + // rms_norm(src0) = + // scale( + // src0, + // div( + // 1, + // sqrt( + // add( + // scale( + // sum( + // sqr( + // src0)), + // (1.0/N)), + // eps)))); + + // postorder: + // ## op args grad + // 00 param src0 grad[#00] + // 01 const 1 + // 02 sqr (#00) grad[#02] + // 03 sum (#02) grad[#03] + // 04 const 1/N + // 05 scale (#03, #04) grad[#05] + // 06 const eps + // 07 add (#05, #06) grad[#07] + // 08 sqrt (#07) grad[#08] + // 09 div (#01,#08) grad[#09] + // 10 scale (#00,#09) grad[#10] + // + // backward pass, given grad[#10] + // #10: scale + // grad[#00] += scale(grad[#10],#09) + // grad[#09] += sum(mul(grad[#10],#00)) + // #09: div + // grad[#08] += neg(mul(grad[#09], div(#09,#08))) + // #08: sqrt + // grad[#07] += mul(grad[#08], div(0.5, #08)) + // #07: add + // grad[#05] += grad[#07] + // #05: scale + // grad[#03] += scale(grad[#05],#04) + // #03: sum + // grad[#02] += repeat(grad[#03], #02) + // #02: + // grad[#00] += scale(mul(#00, grad[#02]), 2.0) + // + // substitute and simplify: + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#02] = repeat(grad[#03], #02) + // grad[#02] = repeat(scale(grad[#05],#04), #02) + // grad[#02] = repeat(scale(grad[#07],#04), #02) + // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) + // a = b*c + d*e + // a = b*c*f/f + d*e*f/f + // a = (b*c*f + d*e*f)*(1/f) + // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) + // a = (b + d*e/c)*c + // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms + // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms + // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms + // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms + // a = (dz + x*div(-mean_xdz,mean_eps))*rrms + // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) + // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + } + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // post-order: + // dx := x + // dx := scale(dx,-mean_xdz/mean_eps) + // dx := add(dx, dz) + // dx := scale(dx, rrms) + float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + ggml_vec_cpy_f32 (ne00, dx, x); + // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); + ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); + ggml_vec_acc_f32 (ne00, dx, dz); + ggml_vec_scale_f32(ne00, dx, rrms); + } + } + } +} + +static void ggml_compute_forward_rms_norm_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_norm + +static void ggml_compute_forward_group_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + // TODO: optimize + + float eps; + memcpy(&eps, dst->op_params + 1, sizeof(float)); + + int n_channels = src0->ne[2]; + int n_groups = dst->op_params[0]; + int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; + for (int i = ith; i < n_groups; i += nth) { + int start = i * n_channels_per_group; + int end = start + n_channels_per_group; + if (end > n_channels) { + end = n_channels; + } + int step = end - start; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + ggml_float sum = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sumr += (ggml_float)x[i00]; + } + sum += sumr; + } + } + const float mean = sum / (ne00 * ne01 * step); + + ggml_float sum2 = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sumr += (ggml_float)(v * v); + } + sum2 += sumr; + } + } + const float variance = sum2 / (ne00 * ne01 * step); + const float scale = 1.0f / sqrtf(variance + eps); + + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_vec_scale_f32(ne00, y, scale); + } + } + } + } +} + +static void ggml_compute_forward_group_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_group_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mul_mat + +static void ggml_compute_forward_mul_mat_one_chunk( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const enum ggml_type type, + const int64_t num_rows_per_vec_dot, + const int64_t ir0_start, + const int64_t ir0_end, + const int64_t ir1_start, + const int64_t ir1_end) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); + + // threads with no work simply yield (not sure if it helps) + if (ir0_start >= ir0_end || ir1_start >= ir1_end) { + return; + } + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + assert(ne12 % ne02 == 0); + assert(ne13 % ne03 == 0); + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + + // attempt to reduce false-sharing (does not seem to make a difference) + // 16 * 2, accounting for mmla kernels + float tmp[32]; + + for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { + for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { + const int64_t i13 = (ir1 / (ne12 * ne1)); + const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; + const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); + + // broadcast src0 into src1 + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; + + const int64_t i1 = i11; + const int64_t i2 = i12; + const int64_t i3 = i13; + + const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char*)wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12 + i13 * nb13)); + float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { + vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); + } + + for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { + memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); + } + } + } + } +} + +static void ggml_compute_forward_mul_mat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + enum ggml_type type = src0->type; + + if (src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { + type = (enum ggml_type)(intptr_t)src0->extra; + } + + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float; + ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat; + int64_t const vec_dot_num_rows = type_traits_cpu[type].nrows; + int64_t const matmul_num_cols = type_traits_cpu[type].ncols; + int64_t const blck_size_interleave = ggml_get_type_traits(type)->blck_size_interleave; + ggml_gemv_t const gemv = type_traits_cpu[type].gemv; + ggml_gemm_t const gemm = type_traits_cpu[type].gemm; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + +#if GGML_USE_LLAMAFILE + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + const bool src1_cont = ggml_is_contiguous(src1); + + if (src1_cont) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(type), + (const char *)src1->data + i12*nb12 + i13*nb13, + nb11/ggml_type_size(src1->type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + type, + src1->type, + dst->type)) + goto UseGgmlGemm1; + return; + } +UseGgmlGemm1:; +#endif + + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + + const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); + const size_t nbw2 = nbw1*ne11; + const size_t nbw3 = nbw2*ne12; + + assert(params->wsize >= ne13*nbw3); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + int64_t i11_processed = 0; + if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { + for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { + from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + 4, ne10, blck_size_interleave); + } + i11_processed = ne11 - ne11 % 4; + } + for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { + from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + ne10); + } + } + } + } + + if (ith == 0) { + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); + } + + ggml_barrier(params->threadpool); + +#if GGML_USE_LLAMAFILE + if (src1->type != vec_dot_type) { + const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(type), + (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, + row_size/ggml_type_size(vec_dot_type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + type, + vec_dot_type, + dst->type)) + goto UseGgmlGemm2; + return; + } +UseGgmlGemm2:; +#endif + + // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) + const int64_t nr0 = ne0; + + // This is the size of the rest of the dimensions of the result + const int64_t nr1 = ne1 * ne2 * ne3; + + // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols + int64_t num_rows_per_vec_dot = vec_dot_num_rows; + // TODO: currently the mmla kernels support only even numbered rows/cols. + // this check can be removed once they are extended to support odd numbered rows/cols too + if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { + num_rows_per_vec_dot = 1; + } + + // Now select a reasonable chunk size. + int chunk_size = 16; + + // We need to step up the size if it's small + if (nr0 == 1 || nr1 == 1) { + chunk_size = 64; + } + + // distribute the work across the inner or outer loop based on which one is larger + // The number of chunks in the 0/1 dim. + // CEIL(nr0/chunk_size) + int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; + int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; + + // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. + // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 + // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. + if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { + // distribute the thread work across the inner or outer loop based on which one is larger + nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + } + + // The number of elements in each chunk + const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; + const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; + + if ((ggml_n_dims(src0) == 2) && gemv) { + const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; + int64_t src0_start = (ith * ne01) / nth; + int64_t src0_end = ((ith + 1) * ne01) / nth; + src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; + src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; + if (src0_start >= src0_end) return; + + // If there are more than three rows in src1, use gemm; otherwise, use gemv. + if (gemm && (ne11 > 3)) { + gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, + (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); + } + for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { + gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, + (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, + src0_end - src0_start); + } + return; + } + + // The first chunk comes from our thread_id, the rest will get auto-assigned. + int current_chunk = ith; + + while (current_chunk < nchunk0 * nchunk1) { + const int64_t ith0 = current_chunk % nchunk0; + const int64_t ith1 = current_chunk / nchunk0; + + const int64_t ir0_start = dr0 * ith0; + const int64_t ir0_end = MIN(ir0_start + dr0, nr0); + + const int64_t ir1_start = dr1 * ith1; + const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + + ggml_compute_forward_mul_mat_one_chunk(params, dst, type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); + + if (nth >= nchunk0 * nchunk1) { + break; + } + + current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); + } +} + +// ggml_compute_forward_mul_mat_id + +static void ggml_compute_forward_mul_mat_id( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * ids = dst->src[2]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float; + int64_t const matmul_num_cols = type_traits_cpu[type].ncols; + ggml_gemv_t const gemv = type_traits_cpu[type].gemv; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // row groups + const int n_ids = ids->ne[0]; // n_expert_used + const int n_as = ne02; // n_expert + + char * wdata_src1_end = (src1->type == vec_dot_type) ? + (char *) params->wdata : + (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); + + struct mmid_row_mapping { + int32_t i1; + int32_t i2; + }; + + int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] + struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] + + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + + const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); + const size_t nbw2 = nbw1*ne11; + const size_t nbw3 = nbw2*ne12; + + assert(params->wsize >= ne13*nbw3); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = ith; i11 < ne11; i11 += nth) { + from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + ne10); + } + } + } + } + +#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] + + if (ith == 0) { + // initialize matrix_row_counts + memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); + + // group rows by src0 matrix + for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + for (int id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); + + assert(i02 >= 0 && i02 < n_as); + + MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; + matrix_row_counts[i02] += 1; + } + } + } + + ggml_barrier(params->threadpool); + + // compute each matrix multiplication in sequence + for (int cur_a = 0; cur_a < n_as; ++cur_a) { + const int64_t cne1 = matrix_row_counts[cur_a]; + + if (cne1 == 0) { + continue; + } + + const char * src0_cur = (const char *) src0->data + cur_a*nb02; + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + const int64_t nr0 = ne01; // src0 rows + const int64_t nr1 = cne1; // src1 rows + + if (((ggml_n_dims(src0) - 1) == 2) && gemv) { + int64_t src0_cur_start = (ith * ne01) / nth; + int64_t src0_cur_end = ((ith + 1) * ne01) / nth; + src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; + src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; + if (src0_cur_start >= src0_cur_end) return; + + for (int ir1 = 0; ir1 < nr1; ir1++) { + struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); + const int id = row_mapping.i1; // selected expert index + + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; // row index in src1 + + const int64_t i1 = id; // selected expert index + const int64_t i2 = i12; // row + + const char * src1_col = (const char *) wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12)); + + gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, + (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); + } + continue; + } + + // distribute the thread work across the inner or outer loop based on which one is larger + + const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + + const int64_t ith0 = ith % nth0; + const int64_t ith1 = ith / nth0; + + const int64_t dr0 = (nr0 + nth0 - 1)/nth0; + const int64_t dr1 = (nr1 + nth1 - 1)/nth1; + + const int64_t ir010 = dr0*ith0; + const int64_t ir011 = MIN(ir010 + dr0, nr0); + + const int64_t ir110 = dr1*ith1; + const int64_t ir111 = MIN(ir110 + dr1, nr1); + + // threads with no work simply yield (not sure if it helps) + //if (ir010 >= ir011 || ir110 >= ir111) { + // sched_yield(); + // continue; + //} + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + // attempt to reduce false-sharing (does not seem to make a difference) + float tmp[16]; + + for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { + for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { + const int64_t _i12 = ir1; // logical row index for this expert + + struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); + const int id = row_mapping.i1; // selected expert index + + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; // row index in src1 + + const int64_t i1 = id; // selected expert index + const int64_t i2 = i12; // row + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char *) wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12*ne11)*row_size + : (i11*nb11 + i12*nb12)); + + float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { + vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); + } + + memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); + } + } + } + } + +#undef MMID_MATRIX_ROW +} + +// ggml_compute_forward_out_prod + +static void ggml_compute_forward_out_prod_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne3 == ne13); + GGML_ASSERT(ne03 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } + ggml_barrier(params->threadpool); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // block-tiling attempt + const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); + const int64_t blck_1 = 16; + + for (int64_t bir = ir0; bir < ir1; bir += blck_1) { + const int64_t bir1 = MIN(bir + blck_1, ir1); + for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { + const int64_t bne01 = MIN(bi01 + blck_0, ne01); + for (int64_t ir = bir; ir < bir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + +#if GGML_VEC_MAD_UNROLL > 2 + const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); + for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); + } + for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#else + for (int64_t i01 = bi01; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#endif + } + } + } +} + +static void ggml_compute_forward_out_prod_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 dim0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst dim0 cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } + ggml_barrier(params->threadpool); + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int64_t ir = ir0; ir < ir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + + for (int64_t i01 = 0; i01 < ne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + dequantize_row_q(s0, wdata, ne0); + ggml_vec_mad_f32(ne0, d, wdata, *s1); + } + } +} + +static void ggml_compute_forward_out_prod( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_out_prod_q_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + GGML_ABORT("fatal error"); // todo + // ggml_compute_forward_out_prod_f16_f32(params, dst); + } + case GGML_TYPE_F32: + { + ggml_compute_forward_out_prod_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_scale + +static void ggml_compute_forward_scale_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + // scale factor + float v; + memcpy(&v, dst->op_params, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const size_t nb01 = src0->nb[1]; + + const size_t nb1 = dst->nb[1]; + + for (int i1 = ir0; i1 < ir1; i1++) { + if (dst->data != src0->data) { + // src0 is same shape as dst => same indices + memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); + } + ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); + } +} + +static void ggml_compute_forward_scale( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_scale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_set + +static void ggml_compute_forward_set_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during set + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during set + const size_t nb0 = ggml_element_size(src0); + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); + } +} + +static void ggml_compute_forward_set( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_set_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cpy + +static void ggml_compute_forward_cpy( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_cont + +static void ggml_compute_forward_cont( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_reshape + +static void ggml_compute_forward_reshape( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_view + +static void ggml_compute_forward_view( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_permute + +static void ggml_compute_forward_permute( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_transpose + +static void ggml_compute_forward_transpose( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_get_rows + +static void ggml_compute_forward_get_rows_q( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == ggml_type_size(type)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + dequantize_row_q( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_fp16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_fp16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_bf16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_bf16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(float)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), + (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); + } +} + +static void ggml_compute_forward_get_rows( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_get_rows_q(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rows_bf16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_get_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_get_rows_back + +static void ggml_compute_forward_get_rows_back_f32_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + for (int j = 0; j < nc; ++j) { + ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; + ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); + } + } +} + +static void ggml_compute_forward_get_rows_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) src0->data + i*src0->nb[1])); + } +} + +static void ggml_compute_forward_get_rows_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_back_f32_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_get_rows_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_diag + +static void ggml_compute_forward_diag_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + // TODO: handle transposed/permuted matrices + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne00 == ne0); + GGML_ASSERT(ne00 == ne1); + GGML_ASSERT(ne01 == 1); + GGML_ASSERT(ne02 == ne2); + GGML_ASSERT(ne03 == ne3); + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb0 == sizeof(float)); + + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = 0; i2 < ne2; i2++) { + for (int i1 = 0; i1 < ne1; i1++) { + float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); + for (int i0 = 0; i0 < i1; i0++) { + d[i0] = 0; + } + d[i1] = s[i1]; + for (int i0 = i1+1; i0 < ne0; i0++) { + d[i0] = 0; + } + } + } + } +} + +static void ggml_compute_forward_diag( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_diag_mask_inf + +static void ggml_compute_forward_diag_mask_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const float value) { + + const struct ggml_tensor * src0 = dst->src[0]; + + const int ith = params->ith; + const int nth = params->nth; + + const int n_past = ((int32_t *) dst->op_params)[0]; + const bool inplace = src0->data == dst->data; + + GGML_ASSERT(n_past >= 0); + + if (!inplace) { + if (ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + // TODO: handle transposed/permuted matrices + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + const int nr = src0->ne[1]; + const int nz = n/nr; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int k = 0; k < nz; k++) { + for (int j = ith; j < nr; j += nth) { + for (int i = n_past; i < nc; i++) { + if (i > n_past + j) { + *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; + } + } + } + } +} + +static void ggml_compute_forward_diag_mask_inf( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_diag_mask_zero( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, 0); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_soft_max + +static void ggml_compute_forward_soft_max_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + assert(ggml_is_contiguous(dst)); + assert(ggml_are_same_shape(src0, dst)); + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + //const int64_t ne11 = src1 ? src1->ne[1] : 1; + + // TODO: is this supposed to be ceil instead of floor? + // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 + const uint32_t n_head = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; + + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); + + for (int i1 = ir0; i1 < ir1; i1++) { + // ALiBi + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); + float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); + + // broadcast the mask across rows + ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + + ggml_vec_cpy_f32 (nc, wp, sp); + ggml_vec_scale_f32(nc, wp, scale); + if (mp_f32) { + if (use_f16) { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); + } + } else { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*mp_f32[i]; + } + } + } + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(wp[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, wp); + + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(nc, dp, sum); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dp[i])); + assert(!isinf(dp[i])); + } +#endif + } +} + +static void ggml_compute_forward_soft_max( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_soft_max_back + +static void ggml_compute_forward_soft_max_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_are_same_shape(src1, dst)); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); + float *y = (float *)((char *) src1->data + i1*src1->nb[1]); + float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(dy[i])); + assert(!isnan(y[i])); + } +#endif + // Jii = yi - yi*yi + // Jij = -yi*yj + // J = diag(y)-y.T*y + // dx = J * dy + // dxk = sum_i(Jki * dyi) + // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*dyk + // dxk = -yk * sum_i(yi * dyi) + yk*dyk + // dxk = -yk * dot(y, dy) + yk*dyk + // dxk = yk * (- dot(y, dy) + dyk) + // dxk = yk * (dyk - dot(y, dy)) + // + // post-order: + // dot_y_dy := dot(y, dy) + // dx := dy + // dx := dx - dot_y_dy + // dx := dx * y + + // linear runtime, no additional memory + float dot_y_dy = 0; + ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); + ggml_vec_cpy_f32 (nc, dx, dy); + ggml_vec_acc1_f32(nc, dx, -dot_y_dy); + ggml_vec_mul_f32 (nc, dx, dx, y); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dx[i])); + assert(!isinf(dx[i])); + } +#endif + } +} + +static void ggml_compute_forward_soft_max_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_clamp + +static void ggml_compute_forward_clamp_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + for (int j = ith; j < n; j += nth) { + float * dst_ptr = (float *) ((char *) dst->data + j*nb1); + float * src0_ptr = (float *) ((char *) src0->data + j*nb01); + + for (int i = 0; i < nc; i++) { + dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); + } + } +} + +static void ggml_compute_forward_clamp( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_clamp_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q8_K: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_I64: + case GGML_TYPE_F64: + case GGML_TYPE_COUNT: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope + +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +static void ggml_rope_cache_init( + float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale) { + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py + float theta = theta_base; + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; + rope_yarn( + theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta *= theta_scale; + } +} + +static void ggml_compute_forward_rope_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb00 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = src[0]; + const float x1 = src[1]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims/2]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } + } + + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } +} + +// TODO: deduplicate f16/f32 code +static void ggml_compute_forward_rope_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[1]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } + + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } +} + +static void ggml_compute_forward_rope( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, true); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, true); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope_back + +static void ggml_compute_forward_rope_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, false); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, false); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_conv_transpose_1d + +static void ggml_compute_forward_conv_transpose_1d_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); + ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // permute source data (src1) from (L x Cin) to (Cin x L) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + ggml_fp16_t * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne02, &v, 0, + (ggml_fp16_t *) wdata_src + i1n, 0, + (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +static void ggml_compute_forward_conv_transpose_1d_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + float * const wdata = (float *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); + float * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // prepare source data (src1) + { + float * const wdata = (float *) params->wdata + nk; + float * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = src[i10]; + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * const wdata = (float *) params->wdata + 0; + float * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + float * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f32(ne02, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +static void ggml_compute_forward_conv_transpose_1d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_conv_transpose_1d_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_f32 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + +// ggml_compute_forward_im2col_f16 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_im2col( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_im2col_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_im2col_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_back_f32 + +static void ggml_compute_forward_im2col_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne3 : ne2; + const int64_t IC = is_2D ? ne2 : ne1; + const int64_t IH = is_2D ? ne1 : 1; + const int64_t IW = ne0; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne12 : 1; + const int64_t OW = ne11; + + int ofs0 = is_2D ? nb3 : nb2; + int ofs1 = is_2D ? nb2 : nb1; + + GGML_ASSERT(nb0 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + for (int64_t iih = 0; iih < IH; iih++) { + for (int64_t iiw = 0; iiw < IW; iiw++) { + + // micro kernel + float grad = 0.0f; + for (int64_t ikh = 0; ikh < KH; ikh++) { + for (int64_t ikw = 0; ikw < KW; ikw++) { + // For s0 > 1 some values were skipped over in the forward pass. + // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. + const int64_t tmpw = (iiw + p0 - ikw*d0); + if (tmpw % s0 != 0) { + continue; + } + const int64_t iow = tmpw / s0; + + // Equivalent logic as above except for s1. + int64_t ioh; + if (is_2D) { + const int64_t tmph = iih + p1 - ikh*d1; + + if (tmph % s1 != 0) { + continue; + } + + ioh = tmph / s1; + } else { + ioh = 0; + } + + if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { + continue; + } + + const float * const src_data = (const float *) src1->data + + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; + } + } + float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] + dst_data[iih*IW + iiw] = grad; + } + } + } + } + } +} + +// ggml_compute_forward_conv_transpose_2d + +static void ggml_compute_forward_conv_transpose_2d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02*ne03; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); + ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; + for (int64_t i01 = 0; i01 < ne01; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; + } + } + } + } + } + + // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + for (int i12 = 0; i12 < ne12; i12++) { + for (int i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); + ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; + for (int i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + } + + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t stride = ggml_get_op_params_i32(dst, 0); + + // total patches in dst + const int np = ne2; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i2 = ip0; i2 < ip1; i2++) { // Cout + float * dst_data = (float *)((char *) dst->data + i2*nb2); + ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; + for (int i11 = 0; i11 < ne11; i11++) { + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i11*ne10*ne12 + i10*ne12; + for (int i01 = 0; i01 < ne01; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne03, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); + dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; + } + } + } + } + } +} + +// ggml_compute_forward_pool_1d_sk_p0 + +static void ggml_compute_forward_pool_1d_sk_p0( + const struct ggml_compute_params * params, + const enum ggml_op_pool op, + const int k, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const char * cdata = (const char *)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + float * drow = (float *)dst->data; + + const int64_t rs = dst->ne[0]; + + while (cdata < data_end) { + const void * srow = (const void *)cdata; + int j = 0; + for (int64_t i = 0; i < rs; ++i) { + switch (op) { + case GGML_OP_POOL_AVG: drow[i] = 0; break; + case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + for (int ki = 0; ki < k; ++ki) { + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: drow[i] += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + ++j; + } + switch (op) { + case GGML_OP_POOL_AVG: drow[i] /= k; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + + cdata += src->nb[1]; + drow += rs; + } +} + +// ggml_compute_forward_pool_1d + +static void ggml_compute_forward_pool_1d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int s0 = opts[2]; + const int p0 = opts[3]; + GGML_ASSERT(p0 == 0); // padding not supported + GGML_ASSERT(k0 == s0); // only s = k supported + + ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); +} + +// ggml_compute_forward_pool_2d + +static void ggml_compute_forward_pool_2d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + const char * cdata = (const char*)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + + const int64_t px = dst->ne[0]; + const int64_t py = dst->ne[1]; + const int64_t pa = px * py; + + float * dplane = (float *)dst->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + float * const drow = dplane + oy * px; + for (int ox = 0; ox < px; ++ox) { + float * const out = drow + ox; + switch (op) { + case GGML_OP_POOL_AVG: *out = 0; break; + case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; + const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: *out += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + switch (op) { + case GGML_OP_POOL_AVG: *out /= ka; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + + cdata += src->nb[2]; + dplane += pa; + } +} + +// ggml_compute_forward_pool_2d_back + +static void ggml_compute_forward_pool_2d_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst + + assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + char * cdata = (char *) dst->data; + const char * cdataf = (const char *) dstf->data; + const char * const data_end = cdata + ggml_nbytes(dst); + + GGML_ASSERT(params->ith == 0); + memset(cdata, 0, ggml_nbytes(dst)); + + const int64_t px = src->ne[0]; + const int64_t py = src->ne[1]; + const int64_t pa = px * py; + + const float * splane = (const float *) src->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + const float * const srow = splane + oy * px; + for (int ox = 0; ox < px; ++ox) { + const float grad0 = srow[ox]; + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + if (op == GGML_OP_POOL_MAX) { + float maxval = -FLT_MAX; + int kxmax = -1; + int kymax = -1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + const float val = dst->type == GGML_TYPE_F32 ? + ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); + if (val <= maxval) { + continue; + } + + maxval = val; + kxmax = kx; + kymax = ky; + } + } + + if (kxmax == -1 || kymax == -1) { + continue; + } + + void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); + const int j = ix + kxmax; + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad0; + } else { + ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); + } + } else if (op == GGML_OP_POOL_AVG) { + const float grad = grad0 / ka; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad; + } else { + ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); + } + } + } + } else { + GGML_ASSERT(false); + } + } + } + + cdata += dst->nb[2]; + cdataf += dst->nb[2]; + splane += pa; + } +} + +// ggml_compute_forward_upscale + +static void ggml_compute_forward_upscale_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; + + // TODO: optimize + + for (int64_t i3 = 0; i3 < ne3; i3++) { + const int64_t i03 = i3 / sf3; + for (int64_t i2 = ith; i2 < ne2; i2 += nth) { + const int64_t i02 = i2 / sf2; + for (int64_t i1 = 0; i1 < ne1; i1++) { + const int64_t i01 = i1 / sf1; + for (int64_t i0 = 0; i0 < ne0; i0++) { + const int64_t i00 = i0 / sf0; + + const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_upscale( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_upscale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_pad + +static void ggml_compute_forward_pad_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT( dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float * dst_ptr = (float *) dst->data; + + // TODO: optimize + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = ith; i1 < ne1; i1 += nth) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + for (int64_t i3 = 0; i3 < ne3; ++i3) { + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + + const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + dst_ptr[dst_idx] = *src_ptr; + } else { + dst_ptr[dst_idx] = 0; + } + } + } + } + } +} + +static void ggml_compute_forward_pad( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_pad_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +static void ggml_compute_forward_arange( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +static void ggml_compute_forward_timestep_embedding( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argsort + +static void ggml_compute_forward_argsort_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(nb0 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); + + for (int64_t i = ith; i < nr; i += nth) { + int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); + const float * src_data = (float *)((char *) src0->data + i*nb01); + + for (int64_t j = 0; j < ne0; j++) { + dst_data[j] = j; + } + + // C doesn't have a functional sort, so we do a bubble sort instead + for (int64_t j = 0; j < ne0; j++) { + for (int64_t k = j + 1; k < ne0; k++) { + if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || + (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { + int32_t tmp = dst_data[j]; + dst_data[j] = dst_data[k]; + dst_data[k] = tmp; + } + } + } + } +} + +static void ggml_compute_forward_argsort( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argsort_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_ext + +static void ggml_compute_forward_flash_attn_ext_f16( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + + GGML_ASSERT(ne0 == D); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nev0 == D); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t rk2 = neq2/nek2; + const int64_t rk3 = neq3/nek3; + + const int64_t rv2 = neq2/nev2; + const int64_t rv3 = neq3/nev3; + + // parallelize by q rows using ggml_vec_dot_f32 + + // total rows in q + const int nr = neq1*neq2*neq3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + enum ggml_type const k_vec_dot_type = type_traits_cpu[k->type].vec_dot_type; + ggml_from_float_t const q_to_vec_dot = type_traits_cpu[k_vec_dot_type].from_float; + ggml_vec_dot_t const kq_vec_dot = type_traits_cpu[k->type].vec_dot; + ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; + + GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); + GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); + + // loop over n_batch and n_head + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int iq3 = ir/(neq2*neq1); + const int iq2 = (ir - iq3*neq2*neq1)/neq1; + const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + + const uint32_t h = iq2; // head index + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value + + float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 + + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, D*sizeof(float)); + } + + const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; + + // k indices + const int ik3 = iq3 / rk3; + const int ik2 = iq2 / rk2; + + // v indices + const int iv3 = iq3 / rv3; + const int iv2 = iq2 / rv2; + + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, D); + + // online softmax / attention + // loop over n_kv and n_head_kv + // ref: https://arxiv.org/pdf/2112.05682.pdf + for (int64_t ic = 0; ic < nek1; ++ic) { + const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + if (mv == -INFINITY) { + continue; + } + + float s; // KQ value + + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); + + s = s*scale; // scale KQ value + + if (logit_softcap != 0.0f) { + s = logit_softcap*tanhf(s); + } + + s += mv; // apply mask + + const float Mold = M; + + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) + + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + + if (v->type == GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + // V += v*expf(s - M) + ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); + } else { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(D, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + v_to_float(v_data, V32, D); + + // V += v*expf(s - M) + ggml_vec_mad_f32(D, VKQ32, V32, vs); + } + + S = S*ms + vs; // scale and increment sum with partial sum + } + + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < D; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } + } + + // V /= S + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(D, VKQ32, S_inv); + + // dst indices + const int i1 = iq1; + const int i2 = iq2; + const int i3 = iq3; + + // original + //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); + + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); + } +} + +static void ggml_compute_forward_flash_attn_ext( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + switch (dst->op_params[3]) { + case GGML_PREC_DEFAULT: + case GGML_PREC_F32: + { + // uses F32 accumulators + ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_back + +static void ggml_compute_forward_flash_attn_back_f32( + const struct ggml_compute_params * params, + const bool masked, + struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * k = dst->src[1]; + const struct ggml_tensor * v = dst->src[2]; + const struct ggml_tensor * d = dst->src[3]; + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ned, d, ne) + GGML_TENSOR_LOCALS(size_t, nbd, d, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + const int64_t P = nek1 - N; + const int64_t M = P + N; + + const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); + const int mxDM = MAX(D, Mup); + + // GGML_ASSERT(ne0 == D); + // GGML_ASSERT(ne1 == N); + GGML_ASSERT(P >= 0); + + GGML_ASSERT(nbq0 == sizeof(float)); + GGML_ASSERT(nbk0 == sizeof(float)); + GGML_ASSERT(nbv0 == sizeof(float)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nek1 == N + P); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned1 == N); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + if (ith == 0) { + memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); + } + ggml_barrier(params->threadpool); + + const int64_t elem_q = ggml_nelements(q); + const int64_t elem_k = ggml_nelements(k); + + enum ggml_type result_type = dst->type; + GGML_ASSERT(ggml_blck_size(result_type) == 1); + const size_t tsize = ggml_type_size(result_type); + + const size_t offs_q = 0; + const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); + const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); + + void * grad_q = (char *) dst->data; + void * grad_k = (char *) dst->data + offs_k; + void * grad_v = (char *) dst->data + offs_v; + + const size_t nbgq1 = nb0*neq0; + const size_t nbgq2 = nb0*neq0*neq1; + const size_t nbgq3 = nb0*neq0*neq1*neq2; + + const size_t nbgk1 = nb0*nek0; + const size_t nbgk2 = nb0*nek0*nek1; + const size_t nbgk3 = nb0*nek0*nek1*neq2; + + const size_t nbgv1 = nb0*nev0; + const size_t nbgv2 = nb0*nev0*nev1; + const size_t nbgv3 = nb0*nev0*nev1*neq2; + + // parallelize by k rows using ggml_vec_dot_f32 + + // total rows in k + const int nr = nek2*nek3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float scale = 1.0f/sqrtf(D); + + //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); + + // how often k2 (and v2) is repeated in q2 + int nrep = neq2/nek2; + + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int ik3 = ir/(nek2); + const int ik2 = ir - ik3*nek2; + + const int iq3 = ik3; + const int id3 = ik3; + const int iv3 = ik3; + const int iv2 = ik2; + + for (int irep = 0; irep < nrep; ++irep) { + const int iq2 = ik2 + irep*nek2; + const int id2 = iq2; + + // (ik2 + irep*nek2) % nek2 == ik2 + for (int iq1 = 0; iq1 < neq1; ++iq1) { + const int id1 = iq1; + + // not sure about CACHE_LINE_SIZE_F32.. + // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? + float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); + float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); + + for (int i = M; i < Mup; ++i) { + S[i] = -INFINITY; + } + + const int64_t masked_begin = masked ? (P + iq1 + 1) : M; + for (int64_t ic = 0; ic < masked_begin; ++ic) { + // k indices + const int ik1 = ic; + + // S indices + const int i1 = ik1; + + ggml_vec_dot_f32(neq0, + S + i1, 0, + (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); + } + + // scale + ggml_vec_scale_f32(masked_begin, S, scale); + + for (int64_t i = masked_begin; i < M; i++) { + S[i] = -INFINITY; + } + + // softmax + // exclude known -INF S[..] values from max and loop + // dont forget to set their SM values to zero + { + float max = -INFINITY; + ggml_vec_max_f32(masked_begin, &max, S); + + ggml_float sum = 0.0; + { +#ifdef GGML_SOFT_MAX_ACCELERATE + max = -max; + vDSP_vsadd(SM, 1, &max, SM, 1, Mup); + vvexpf(SM, SM, &Mup); + ggml_vec_sum_f32(Mup, &sum, SM); +#else + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); +#endif + } + + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(masked_begin, SM, sum); + + } + + // step-by-step explanation + { + // forward-process shape grads from backward process + // parallel_for ik2,ik3: + // for irep: + // iq2 = ik2 + irep*nek2 + // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] + // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] + // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] + // for iq1: + // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur + // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur + // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 + // S0 = -Inf [D,1,1,1] + // ~S1[i] = dot(kcur[:D,i], qcur) + // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale + // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) + // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur + // ~S5[i] = dot(vcur[:,i], S4) + // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] + // ~dst[i,iq1,iq2,iq3] = S5[i] ^ + // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] + // dst backward-/ grad[dst] = d + // + // output gradients with their dependencies: + // + // grad[kcur] = grad[S1].T @ qcur + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S4] = grad[S5] @ vcur + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[qcur] = grad[S1] @ kcur + // grad[vcur] = grad[S5].T @ S4 + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // in post-order: + // + // S1 = qcur @ kcur.T + // S2 = S1 * scale + // S3 = diag_mask_inf(S2, P) + // S4 = softmax(S3) + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[qcur] = grad[S1] @ kcur + // grad[kcur] = grad[S1].T @ qcur + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // using less variables (SM=S4): + // + // S = diag_mask_inf(qcur @ kcur.T * scale, P) + // SM = softmax(S) + // S = d[:D,iq1,iq2,iq3] @ vcur + // dot_SM_gradSM = dot(SM, S) + // S = SM * (S - dot(SM, S)) + // S = diag_mask_zero(S, P) * scale + // + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[k][:D,:M,ik2,ik3] += S.T @ qcur + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + } + + // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // for ic: + // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] + // exclude known future zero S[..] values from operation + ggml_vec_set_f32(masked_begin, S, 0); + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + S, + (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + + // S = SM * (S - dot(SM, S)) + float dot_SM_gradSM = 0; + ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); + ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); + ggml_vec_mul_f32 (masked_begin, S, S, SM); + + // S = diag_mask_zero(S, P) * scale + // already done by above ggml_vec_set_f32 + + // exclude known zero S[..] values from operation + ggml_vec_scale_f32(masked_begin, S, scale); + + // S shape [M,1] + // SM shape [M,1] + // kcur shape [D,M] + // qcur shape [D,1] + // vcur shape [M,D] + + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] + // for ic: + // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), + (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), + S[ic]); + } + + // grad[k][:D,:M,iq2,iq3] += S.T @ qcur + // for ic: + // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] + // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), + S[ic]); + } + + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + // for ic: + // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] + // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] + // exclude known zero SM[..] values from mad + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), + SM, + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + } + } + } +} + +static void ggml_compute_forward_flash_attn_back( + const struct ggml_compute_params * params, + const bool masked, + struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + + switch (q->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_flash_attn_back_f32(params, masked, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_conv + +static void ggml_compute_forward_ssm_conv_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; // conv_x + const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src1->ne[0]; // d_conv + const int ncs = src0->ne[0]; // d_conv - 1 + n_t + const int nr = src0->ne[1]; // d_inner + const int n_t = dst->ne[1]; // tokens per sequence + const int n_s = dst->ne[2]; // number of sequences in the batch + + GGML_ASSERT( dst->ne[0] == nr); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + // {d_conv - 1 + n_t, d_inner, n_seqs} + // sliding window + const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} + const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} + float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} + + // TODO: transpose the output for smaller strides for big batches? + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision + float sumf = 0.0f; + + // d_conv + for (int i0 = 0; i0 < nc; ++i0) { + sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; + } + x[i1] = sumf; + } + } + } +} + +static void ggml_compute_forward_ssm_conv( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_conv_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_scan + +static void ggml_compute_forward_ssm_scan_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; // s + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // dt + const struct ggml_tensor * src3 = dst->src[3]; // A + const struct ggml_tensor * src4 = dst->src[4]; // B + const struct ggml_tensor * src5 = dst->src[5]; // C + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens per sequence + const int64_t n_s = src0->ne[2]; // number of sequences in the batch + + GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(float)); + GGML_ASSERT(src4->nb[0] == sizeof(float)); + GGML_ASSERT(src5->nb[0] == sizeof(float)); + // required for the dot product between s and C + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // required for per-sequence offsets for states + GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[3]) + GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} + const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} + const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} + const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} + float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} + + // use the output as the source for the next token-wise iterations + if (i2 > 0) { s0 = s; } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; + } + } + } +} + +static void ggml_compute_forward_ssm_scan( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_scan_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_part + +static void ggml_compute_forward_win_part_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t w = ((const int32_t *)(dst->op_params))[2]; + + assert(ne00 == ne0); + assert(ne3 == nep0*nep1); + + // TODO: optimize / multi-thread + for (int py = 0; py < nep1; ++py) { + for (int px = 0; px < nep0; ++px) { + const int64_t i3 = py*nep0 + px; + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i02 = py*w + i2; + const int64_t i01 = px*w + i1; + const int64_t i00 = i0; + + const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; + const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; + + if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { + ((float *) dst->data)[i] = 0.0f; + } else { + ((float *) dst->data)[i] = ((float *) src0->data)[j]; + } + } + } + } + } + } +} + +static void ggml_compute_forward_win_part( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_part_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_unpart + +static void ggml_compute_forward_win_unpart_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t w = ((const int32_t *)(dst->op_params))[0]; + + // padding + const int px = (w - ne1%w)%w; + //const int py = (w - ne2%w)%w; + + const int npx = (px + ne1)/w; + //const int npy = (py + ne2)/w; + + assert(ne0 == ne00); + + // TODO: optimize / multi-thread + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int ip2 = i2/w; + const int ip1 = i1/w; + + const int64_t i02 = i2%w; + const int64_t i01 = i1%w; + const int64_t i00 = i0; + + const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; + const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; + + ((float *) dst->data)[j] = ((float *) src0->data)[i]; + } + } + } +} + +static void ggml_compute_forward_win_unpart( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_unpart_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +//gmml_compute_forward_unary + +static void ggml_compute_forward_unary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const enum ggml_unary_op op = ggml_get_unary_op(dst); + + switch (op) { + case GGML_UNARY_OP_ABS: + { + ggml_compute_forward_abs(params, dst); + } break; + case GGML_UNARY_OP_SGN: + { + ggml_compute_forward_sgn(params, dst); + } break; + case GGML_UNARY_OP_NEG: + { + ggml_compute_forward_neg(params, dst); + } break; + case GGML_UNARY_OP_STEP: + { + ggml_compute_forward_step(params, dst); + } break; + case GGML_UNARY_OP_TANH: + { + ggml_compute_forward_tanh(params, dst); + } break; + case GGML_UNARY_OP_ELU: + { + ggml_compute_forward_elu(params, dst); + } break; + case GGML_UNARY_OP_RELU: + { + ggml_compute_forward_relu(params, dst); + } break; + case GGML_UNARY_OP_SIGMOID: + { + ggml_compute_forward_sigmoid(params, dst); + } break; + case GGML_UNARY_OP_GELU: + { + ggml_compute_forward_gelu(params, dst); + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + ggml_compute_forward_gelu_quick(params, dst); + } break; + case GGML_UNARY_OP_SILU: + { + ggml_compute_forward_silu(params, dst); + } break; + case GGML_UNARY_OP_HARDSWISH: + { + ggml_compute_forward_hardswish(params, dst); + } break; + case GGML_UNARY_OP_HARDSIGMOID: + { + ggml_compute_forward_hardsigmoid(params, dst); + } break; + case GGML_UNARY_OP_EXP: + { + ggml_compute_forward_exp(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_get_rel_pos + +static void ggml_compute_forward_get_rel_pos_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 + + GGML_TENSOR_UNARY_OP_LOCALS + + const int64_t w = ne1; + + ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; + ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + const int64_t pos = (w - i1 - 1) + i2; + for (int64_t i0 = 0; i0 < ne0; ++i0) { + dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; + } + } + } +} + +static void ggml_compute_forward_get_rel_pos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rel_pos_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add_rel_pos + +static void ggml_compute_forward_add_rel_pos_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; + if (!inplace) { + if (params->ith == 0) { + memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 + + float * src1_data = (float *) src1->data; + float * src2_data = (float *) src2->data; + float * dst_data = (float *) dst->data; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + + const int ith = params->ith; + const int nth = params->nth; + + // total patches in dst + const int np = ne13; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + for (int64_t i13 = ip0; i13 < ip1; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; + for (int64_t i10 = 0; i10 < ne10; ++i10) { + const int64_t jp0 = jp1 + i10; + const float src1_e = src1_data[jp0]; + const float src2_e = src2_data[jp0]; + + const int64_t jdh = jp0 * ne10; + const int64_t jdw = jdh - (ne10 - 1) * i10; + + for (int64_t j = 0; j < ne10; ++j) { + dst_data[jdh + j ] += src2_e; + dst_data[jdw + j*ne10] += src1_e; + } + } + } + } + } +} + +static void ggml_compute_forward_add_rel_pos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add_rel_pos_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rwkv_wkv6 + +static void ggml_compute_forward_rwkv_wkv6_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const int64_t T = dst->src[1]->ne[3]; + const int64_t C = dst->ne[0]; + const int64_t HEADS = dst->src[1]->ne[2]; + const int64_t n_seqs = dst->src[5]->ne[1]; + const int64_t head_size = C / HEADS; + + float * dst_data = (float *) dst->data; + float * state = ((float *) dst->data) + C * T; + + const int ith = params->ith; + const int nth = params->nth; + + if (ith >= HEADS) { + return; + } + + const int h_start = (HEADS * ith) / nth; + const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? + (HEADS * (ith + 1)) / nth : HEADS; + + float * k = (float *) dst->src[0]->data; + float * v = (float *) dst->src[1]->data; + float * r = (float *) dst->src[2]->data; + float * time_faaaa = (float *) dst->src[3]->data; + float * time_decay = (float *) dst->src[4]->data; + + size_t t_stride = HEADS * head_size; // Same to C + + size_t h_stride = C / HEADS; + GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS + size_t h_stride_2d = head_size * head_size; + + if (ith == 0) { + memset(dst_data, 0, T * C * sizeof(float)); + } + ggml_barrier(params->threadpool); + + + #if defined(__AVX__) && !defined(__AVX512F__) + #define GGML_F32X GGML_F32x8 + #define GGML_F32X_SET1 GGML_F32x8_SET1 + #define GGML_F32X_LOAD GGML_F32x8_LOAD + #define GGML_F32X_STORE GGML_F32x8_STORE + #define GGML_F32X_MUL GGML_F32x8_MUL + #define GGML_F32X_FMA GGML_F32x8_FMA + #define WKV_VECTOR_SIZE 8 + #elif defined(__AVX512F__) + #define GGML_F32X GGML_F32x16 + #define GGML_F32X_SET1 GGML_F32x16_SET1 + #define GGML_F32X_LOAD GGML_F32x16_LOAD + #define GGML_F32X_STORE GGML_F32x16_STORE + #define GGML_F32X_MUL GGML_F32x16_MUL + #define GGML_F32X_FMA GGML_F32x16_FMA + #define WKV_VECTOR_SIZE 16 + #elif defined(__ARM_NEON) && defined(__aarch64__) + #define GGML_F32X GGML_F32x4 + #define GGML_F32X_SET1 GGML_F32x4_SET1 + #define GGML_F32X_LOAD GGML_F32x4_LOAD + #define GGML_F32X_STORE GGML_F32x4_STORE + #define GGML_F32X_MUL GGML_F32x4_MUL + #define GGML_F32X_FMA GGML_F32x4_FMA + #define WKV_VECTOR_SIZE 4 + #endif + + #ifdef WKV_VECTOR_SIZE + const int64_t vec_count = head_size / WKV_VECTOR_SIZE; + + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + float time_decay_val = time_decay[t_h_i_offset]; + + // Broadcast scalar values to vectors + GGML_F32X k_vec = GGML_F32X_SET1(k_val); + GGML_F32X r_vec = GGML_F32X_SET1(r_val); + GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); + GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); + + for (int64_t j = 0; j < vec_count; j++) { + size_t base_j = j * WKV_VECTOR_SIZE; + size_t t_h_j_offset = t_h_offset + base_j; + size_t h_2d_i_j_offset = h_2d_i_offset + base_j; + + // Load x elements at once + GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); + GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); + GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); + + // Compute kv = v * k + GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); + + // Compute temp = kv * time_faaaa + prev_state + GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); + + // Update dst: dst += temp * r + dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); + GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); + + // Update state: state = prev_state * time_decay + kv + GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); + GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); + } + + // Handle remaining elements, this will not be used. + for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + + #else + // basically fused operations: + // dst = r @ (time_faaaa * (k @ v) + state), + // state = time_decay * state + (k @ v), + // recursive through each token + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + // RWKV v6: different time_decay for each token. + float time_decay_val = time_decay[t_h_i_offset]; + + for (int64_t j = 0; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + #endif +} + + +static void ggml_compute_forward_rwkv_wkv6( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rwkv_wkv6_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_unary + +static void ggml_compute_forward_map_unary_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_map_unary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_unary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_binary + +static void ggml_compute_forward_map_binary_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), + (float *) ((char *) src1->data + i*(src1->nb[1]))); + } +} + +static void ggml_compute_forward_map_binary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_binary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom1_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + + if (params->ith != 0) { + return; + } + + fun(dst, a); +} + +// ggml_compute_forward_map_custom2 + +static void ggml_compute_forward_map_custom2_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom2_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b); +} + +// ggml_compute_forward_map_custom3 + +static void ggml_compute_forward_map_custom3_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom3_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b, c); +} + +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + + struct ggml_map_custom1_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom2 + +static void ggml_compute_forward_map_custom2( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + + struct ggml_map_custom2_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom3 + +static void ggml_compute_forward_map_custom3( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[2]; + + struct ggml_map_custom3_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_cross_entropy_loss + +static void ggml_compute_forward_cross_entropy_loss_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0->ne[0]; + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + float * sums = (float *) params->wdata; + float * st = ((float *) params->wdata) + nth + ith*nc; + float sum_thread = 0.0f; + + GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t i1 = ir0; i1 < ir1; ++i1) { + const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); + const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); + assert(sum_softmax >= 0.0); + + ggml_vec_add1_f32(nc, st, st, -sum_softmax); + ggml_vec_mul_f32(nc, st, st, s1); + + float sum_st = 0.0f; + ggml_vec_sum_f32(nc, &sum_st, st); + sum_thread += sum_st; + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(st[i])); + assert(!isinf(st[i])); + } +#endif + } + sums[ith] = sum_thread; + ggml_barrier(params->threadpool); + + if (ith == 0) { + float * dp = (float *) dst->data; + ggml_vec_sum_f32(nth, dp, sums); + dp[0] *= -1.0f / (float) nr; + } +} + +static void ggml_compute_forward_cross_entropy_loss( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cross_entropy_loss_back + +static void ggml_compute_forward_cross_entropy_loss_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * opt0 = dst->src[2]; + + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(opt0)); + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int64_t ith = params->ith; + const int64_t nth = params->nth; + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0->ne[0]; + const int64_t nr = ggml_nrows(src0); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; + + for (int64_t i1 = ir0; i1 < ir1; i1++) { + float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); + float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); + float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + // soft_max + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + ggml_vec_scale_f32(nc, ds0, 1.0/sum); + + // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr + ggml_vec_sub_f32(nc, ds0, ds0, s1); + ggml_vec_scale_f32(nc, ds0, d_by_nr); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(ds0[i])); + assert(!isinf(ds0[i])); + } +#endif + } +} + +static void ggml_compute_forward_cross_entropy_loss_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_opt_step_adamw_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src0_grad = dst->src[1]; + const struct ggml_tensor * src0_grad_m = dst->src[2]; + const struct ggml_tensor * src0_grad_v = dst->src[3]; + const struct ggml_tensor * adamw_params = dst->src[4]; + + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float * adamw_params_ptr = ggml_get_data_f32(adamw_params); + const float alpha = adamw_params_ptr[0]; + const float beta1 = adamw_params_ptr[1]; + const float beta2 = adamw_params_ptr[2]; + const float eps = adamw_params_ptr[3]; + const float wd = adamw_params_ptr[4]; + const float beta1h = adamw_params_ptr[5]; + const float beta2h = adamw_params_ptr[6]; + + for (int ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; + + float * w = (float *) ((char *) src0->data + offset); // weight + const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad + float * m = (float *) ((char *) src0_grad_m->data + offset); + float * v = (float *) ((char *) src0_grad_v->data + offset); + + for (int i00 = 0; i00 < ne00; ++i00) { + m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); + v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); + + const float mh = m[i00]*beta1h; + const float vh = sqrtf(v[i00]*beta2h) + eps; + + // The weight decay is applied independently of the Adam momenta m and v. + // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. + // See: https://arxiv.org/pdf/1711.05101v3.pdf + w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh; + } + } +} + +static void ggml_compute_forward_opt_step_adamw( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_opt_step_adamw_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} +///////////////////////////////// + +static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + GGML_ASSERT(params); + + if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { + return; + } + + switch (tensor->op) { + case GGML_OP_DUP: + { + ggml_compute_forward_dup(params, tensor); + } break; + case GGML_OP_ADD: + { + ggml_compute_forward_add(params, tensor); + } break; + case GGML_OP_ADD1: + { + ggml_compute_forward_add1(params, tensor); + } break; + case GGML_OP_ACC: + { + ggml_compute_forward_acc(params, tensor); + } break; + case GGML_OP_SUB: + { + ggml_compute_forward_sub(params, tensor); + } break; + case GGML_OP_MUL: + { + ggml_compute_forward_mul(params, tensor); + } break; + case GGML_OP_DIV: + { + ggml_compute_forward_div(params, tensor); + } break; + case GGML_OP_SQR: + { + ggml_compute_forward_sqr(params, tensor); + } break; + case GGML_OP_SQRT: + { + ggml_compute_forward_sqrt(params, tensor); + } break; + case GGML_OP_LOG: + { + ggml_compute_forward_log(params, tensor); + } break; + case GGML_OP_SIN: + { + ggml_compute_forward_sin(params, tensor); + } break; + case GGML_OP_COS: + { + ggml_compute_forward_cos(params, tensor); + } break; + case GGML_OP_SUM: + { + ggml_compute_forward_sum(params, tensor); + } break; + case GGML_OP_SUM_ROWS: + { + ggml_compute_forward_sum_rows(params, tensor); + } break; + case GGML_OP_MEAN: + { + ggml_compute_forward_mean(params, tensor); + } break; + case GGML_OP_ARGMAX: + { + ggml_compute_forward_argmax(params, tensor); + } break; + case GGML_OP_COUNT_EQUAL: + { + ggml_compute_forward_count_equal(params, tensor); + } break; + case GGML_OP_REPEAT: + { + ggml_compute_forward_repeat(params, tensor); + } break; + case GGML_OP_REPEAT_BACK: + { + ggml_compute_forward_repeat_back(params, tensor); + } break; + case GGML_OP_CONCAT: + { + ggml_compute_forward_concat(params, tensor); + } break; + case GGML_OP_SILU_BACK: + { + ggml_compute_forward_silu_back(params, tensor); + } break; + case GGML_OP_NORM: + { + ggml_compute_forward_norm(params, tensor); + } break; + case GGML_OP_RMS_NORM: + { + ggml_compute_forward_rms_norm(params, tensor); + } break; + case GGML_OP_RMS_NORM_BACK: + { + ggml_compute_forward_rms_norm_back(params, tensor); + } break; + case GGML_OP_GROUP_NORM: + { + ggml_compute_forward_group_norm(params, tensor); + } break; + case GGML_OP_MUL_MAT: + { + ggml_compute_forward_mul_mat(params, tensor); + } break; + case GGML_OP_MUL_MAT_ID: + { + ggml_compute_forward_mul_mat_id(params, tensor); + } break; + case GGML_OP_OUT_PROD: + { + ggml_compute_forward_out_prod(params, tensor); + } break; + case GGML_OP_SCALE: + { + ggml_compute_forward_scale(params, tensor); + } break; + case GGML_OP_SET: + { + ggml_compute_forward_set(params, tensor); + } break; + case GGML_OP_CPY: + { + ggml_compute_forward_cpy(params, tensor); + } break; + case GGML_OP_CONT: + { + ggml_compute_forward_cont(params, tensor); + } break; + case GGML_OP_RESHAPE: + { + ggml_compute_forward_reshape(params, tensor); + } break; + case GGML_OP_VIEW: + { + ggml_compute_forward_view(params, tensor); + } break; + case GGML_OP_PERMUTE: + { + ggml_compute_forward_permute(params, tensor); + } break; + case GGML_OP_TRANSPOSE: + { + ggml_compute_forward_transpose(params, tensor); + } break; + case GGML_OP_GET_ROWS: + { + ggml_compute_forward_get_rows(params, tensor); + } break; + case GGML_OP_GET_ROWS_BACK: + { + ggml_compute_forward_get_rows_back(params, tensor); + } break; + case GGML_OP_DIAG: + { + ggml_compute_forward_diag(params, tensor); + } break; + case GGML_OP_DIAG_MASK_INF: + { + ggml_compute_forward_diag_mask_inf(params, tensor); + } break; + case GGML_OP_DIAG_MASK_ZERO: + { + ggml_compute_forward_diag_mask_zero(params, tensor); + } break; + case GGML_OP_SOFT_MAX: + { + ggml_compute_forward_soft_max(params, tensor); + } break; + case GGML_OP_SOFT_MAX_BACK: + { + ggml_compute_forward_soft_max_back(params, tensor); + } break; + case GGML_OP_ROPE: + { + ggml_compute_forward_rope(params, tensor); + } break; + case GGML_OP_ROPE_BACK: + { + ggml_compute_forward_rope_back(params, tensor); + } break; + case GGML_OP_CLAMP: + { + ggml_compute_forward_clamp(params, tensor); + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + ggml_compute_forward_conv_transpose_1d(params, tensor); + } break; + case GGML_OP_IM2COL: + { + ggml_compute_forward_im2col(params, tensor); + } break; + case GGML_OP_IM2COL_BACK: + { + ggml_compute_forward_im2col_back_f32(params, tensor); + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + ggml_compute_forward_conv_transpose_2d(params, tensor); + } break; + case GGML_OP_POOL_1D: + { + ggml_compute_forward_pool_1d(params, tensor); + } break; + case GGML_OP_POOL_2D: + { + ggml_compute_forward_pool_2d(params, tensor); + } break; + case GGML_OP_POOL_2D_BACK: + { + ggml_compute_forward_pool_2d_back(params, tensor); + } break; + case GGML_OP_UPSCALE: + { + ggml_compute_forward_upscale(params, tensor); + } break; + case GGML_OP_PAD: + { + ggml_compute_forward_pad(params, tensor); + } break; + case GGML_OP_ARANGE: + { + ggml_compute_forward_arange(params, tensor); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + ggml_compute_forward_timestep_embedding(params, tensor); + } break; + case GGML_OP_ARGSORT: + { + ggml_compute_forward_argsort(params, tensor); + } break; + case GGML_OP_LEAKY_RELU: + { + ggml_compute_forward_leaky_relu(params, tensor); + } break; + case GGML_OP_FLASH_ATTN_EXT: + { + ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + int32_t t = ggml_get_op_params_i32(tensor, 0); + GGML_ASSERT(t == 0 || t == 1); + bool masked = t != 0; + ggml_compute_forward_flash_attn_back(params, masked, tensor); + } break; + case GGML_OP_SSM_CONV: + { + ggml_compute_forward_ssm_conv(params, tensor); + } break; + case GGML_OP_SSM_SCAN: + { + ggml_compute_forward_ssm_scan(params, tensor); + } break; + case GGML_OP_WIN_PART: + { + ggml_compute_forward_win_part(params, tensor); + } break; + case GGML_OP_WIN_UNPART: + { + ggml_compute_forward_win_unpart(params, tensor); + } break; + case GGML_OP_UNARY: + { + ggml_compute_forward_unary(params, tensor); + } break; + case GGML_OP_GET_REL_POS: + { + ggml_compute_forward_get_rel_pos(params, tensor); + } break; + case GGML_OP_ADD_REL_POS: + { + ggml_compute_forward_add_rel_pos(params, tensor); + } break; + case GGML_OP_RWKV_WKV6: + { + ggml_compute_forward_rwkv_wkv6(params, tensor); + } break; + case GGML_OP_MAP_UNARY: + { + ggml_unary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_unary(params, tensor, fun); + } + break; + case GGML_OP_MAP_BINARY: + { + ggml_binary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_binary(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM1_F32: + { + ggml_custom1_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom1_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM2_F32: + { + ggml_custom2_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom2_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM3_F32: + { + ggml_custom3_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom3_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM1: + { + ggml_compute_forward_map_custom1(params, tensor); + } + break; + case GGML_OP_MAP_CUSTOM2: + { + ggml_compute_forward_map_custom2(params, tensor); + } + break; + case GGML_OP_MAP_CUSTOM3: + { + ggml_compute_forward_map_custom3(params, tensor); + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS: + { + ggml_compute_forward_cross_entropy_loss(params, tensor); + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + { + ggml_compute_forward_cross_entropy_loss_back(params, tensor); + } + break; + case GGML_OP_OPT_STEP_ADAMW: + { + ggml_compute_forward_opt_step_adamw(params, tensor); + } + break; + case GGML_OP_NONE: + { + // nop + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + } +} + +// Android's libc implementation "bionic" does not support setting affinity +#if defined(__gnu_linux__) +static void set_numa_thread_affinity(int thread_n) { + if (!ggml_is_numa()) { + return; + } + + int node_num; + int rv; + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + switch(g_state.numa.numa_strategy) { + case GGML_NUMA_STRATEGY_DISTRIBUTE: + // run thread on node_num thread_n / (threads per node) + node_num = thread_n % g_state.numa.n_nodes; + break; + case GGML_NUMA_STRATEGY_ISOLATE: + // run thread on current_node + node_num = g_state.numa.current_node; + break; + case GGML_NUMA_STRATEGY_NUMACTL: + // use the cpuset that numactl gave us + rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); + } + return; + default: + return; + } + + struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (size_t i = 0; i < node->n_cpus; ++i) { + CPU_SET_S(node->cpus[i], setsize, cpus); + } + + rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); + } + + CPU_FREE(cpus); +} + +static void clear_numa_thread_affinity(void) { + if (!ggml_is_numa()) { + return; + } + + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { + CPU_SET_S(i, setsize, cpus); + } + + int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); + } + + CPU_FREE(cpus); +} +#else +// TODO: Windows etc. +// (the linux implementation may also work on BSD, someone should test) +static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } +static void clear_numa_thread_affinity(void) {} +#endif + +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { + int n_tasks = 0; + + if (ggml_is_empty(node)) { + // no need to multi-thread a no-op + n_tasks = 1; + return n_tasks; + } + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + case GGML_OP_CONT: + case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_ACC: + { + n_tasks = n_threads; + } break; + case GGML_OP_SUB: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_LOG: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT_EQUAL: + { + n_tasks = n_threads; + } break; + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + case GGML_OP_LEAKY_RELU: + { + n_tasks = 1; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_EXP: + { + n_tasks = 1; + } break; + + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + { + n_tasks = n_threads; + } break; + default: + GGML_ABORT("fatal error"); + } + break; + case GGML_OP_SILU_BACK: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_GROUP_NORM: + case GGML_OP_CONCAT: + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + case GGML_OP_OUT_PROD: + { + n_tasks = n_threads; + } break; + case GGML_OP_GET_ROWS: + { + // FIXME: get_rows can use additional threads, but the cost of launching additional threads + // decreases performance with GPU offloading + //n_tasks = n_threads; + n_tasks = 1; + } break; + case GGML_OP_SCALE: + case GGML_OP_SET: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_GET_ROWS_BACK: + case GGML_OP_DIAG: + { + n_tasks = 1; + } break; + case GGML_OP_DIAG_MASK_ZERO: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + case GGML_OP_ADD_REL_POS: + { + n_tasks = n_threads; + } break; + case GGML_OP_CLAMP: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_SOFT_MAX: + { + n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); + } break; + case GGML_OP_IM2COL: + case GGML_OP_IM2COL_BACK: + case GGML_OP_CONV_TRANSPOSE_1D: + case GGML_OP_CONV_TRANSPOSE_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + case GGML_OP_POOL_2D_BACK: + { + n_tasks = 1; + } break; + case GGML_OP_UPSCALE: + case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_ARGSORT: + case GGML_OP_FLASH_ATTN_EXT: + case GGML_OP_FLASH_ATTN_BACK: + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + n_tasks = n_threads; + } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_GET_REL_POS: + case GGML_OP_RWKV_WKV6: + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: + { + n_tasks = 1; + } break; + case GGML_OP_MAP_CUSTOM1: + { + struct ggml_map_custom1_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM2: + { + struct ggml_map_custom2_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM3: + { + struct ggml_map_custom3_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + case GGML_OP_OPT_STEP_ADAMW: + { + n_tasks = n_threads; + } break; + case GGML_OP_NONE: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + default: + { + fprintf(stderr, "%s: op not implemented: ", __func__); + if (node->op < GGML_OP_COUNT) { + fprintf(stderr, "%s\n", ggml_op_name(node->op)); + } else { + fprintf(stderr, "%d\n", node->op); + } + GGML_ABORT("fatal error"); + } + } + + assert(n_tasks > 0); + + return n_tasks; +} + +static thread_ret_t ggml_graph_compute_secondary_thread(void* data); + +#if defined(_WIN32) +#include "windows.h" + +// TODO: support > 64 CPUs +bool ggml_thread_apply_affinity(bool * mask) { + HANDLE h = GetCurrentThread(); + uint64_t bitmask = 0ULL; + + assert(GGML_MAX_N_THREADS >= 64); + + for (int32_t i = 0; i < 8; i++) { + int32_t idx = i * 8; + uint8_t val = 0; + val |= mask[idx + 0] << 0; + val |= mask[idx + 1] << 1; + val |= mask[idx + 2] << 2; + val |= mask[idx + 3] << 3; + val |= mask[idx + 4] << 4; + val |= mask[idx + 5] << 5; + val |= mask[idx + 6] << 6; + val |= mask[idx + 7] << 7; + bitmask |= (uint64_t)val << idx; + } + + for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { + fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); + break; + } + } + + DWORD_PTR m = (DWORD_PTR)bitmask; + + m = SetThreadAffinityMask(h, m); + + return m != 0; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. + // This is up to the applications. + DWORD p = THREAD_PRIORITY_NORMAL; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; + case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; + case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; + case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + if (!SetThreadPriority(GetCurrentThread(), p)) { + fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); + return false; + } + + return true; +} + +#elif defined(__APPLE__) +#include +#include + +static bool ggml_thread_apply_affinity(const bool * mask) { + // Not supported on Apple platforms + UNUSED(mask); + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + struct sched_param p; + int32_t policy = SCHED_OTHER; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; + case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; + case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; + case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + int32_t err = pthread_setschedparam(pthread_self(), policy, &p); + if (err != 0) { + fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); + return false; + } + + return true; +} + +#elif defined(__gnu_linux__) +// TODO: this may not work on BSD, to be verified + +static bool ggml_thread_apply_affinity(const bool * mask) { + cpu_set_t cpuset; + int err; + + CPU_ZERO(&cpuset); + + for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { + GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); + CPU_SET(i, &cpuset); + } + } + +#ifdef __ANDROID__ + err = sched_setaffinity(0, sizeof(cpuset), &cpuset); + if (err < 0) { + err = errno; + } +#else + err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); +#endif + if (err != 0) { + fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); + return false; + } + + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + struct sched_param p; + int32_t policy = SCHED_OTHER; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; + case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; + case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; + case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + int32_t err = pthread_setschedparam(pthread_self(), policy, &p); + if (err != 0) { + fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); + return false; + } + + return true; +} + +#else // unsupported platforms + +static bool ggml_thread_apply_affinity(const bool * mask) { + UNUSED(mask); + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + UNUSED(prio); + return true; +} + +#endif + +static bool ggml_thread_cpumask_is_valid(const bool * mask) { + for (int i = 0; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { return true; } + } + return false; +} + +static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { + if (!strict) { + memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); + return; + } else { + memset(local_mask, 0, GGML_MAX_N_THREADS); + int32_t base_idx = *iter; + for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + int32_t idx = base_idx + i; + if (idx >= GGML_MAX_N_THREADS) { + // Just a cheaper modulo + idx -= GGML_MAX_N_THREADS; + } + if (global_mask[idx]) { + local_mask[idx] = 1; + *iter = idx + 1; + return; + } + } + } +} + +void ggml_threadpool_free(struct ggml_threadpool* threadpool) { + if (!threadpool) return; + + const int n_threads = threadpool->n_threads_max; + +#ifndef GGML_USE_OPENMP + struct ggml_compute_state* workers = threadpool->workers; + + ggml_mutex_lock(&threadpool->mutex); + + threadpool->stop = true; + threadpool->pause = false; + + ggml_cond_broadcast(&threadpool->cond); + ggml_mutex_unlock(&threadpool->mutex); + + for (int j = 1; j < n_threads; j++) { + int32_t rc = ggml_thread_join(workers[j].thrd, NULL); + GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); + UNUSED(rc); + } + + ggml_mutex_destroy(&threadpool->mutex); + ggml_cond_destroy(&threadpool->cond); +#endif // GGML_USE_OPENMP + + const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; + ggml_aligned_free(threadpool->workers, workers_size); + ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); +} + +#ifndef GGML_USE_OPENMP +// pause/resume must be called under mutex +static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { + GGML_PRINT_DEBUG("Pausing threadpool\n"); + threadpool->pause = true; + ggml_cond_broadcast(&threadpool->cond); +} + +static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { + GGML_PRINT_DEBUG("Resuming threadpool\n"); + threadpool->pause = false; + ggml_cond_broadcast(&threadpool->cond); +} +#endif + +void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { +#ifndef GGML_USE_OPENMP + ggml_mutex_lock(&threadpool->mutex); + if (!threadpool->pause) { + ggml_threadpool_pause_locked(threadpool); + } + ggml_mutex_unlock(&threadpool->mutex); +#else + UNUSED(threadpool); +#endif +} + +void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { +#ifndef GGML_USE_OPENMP + ggml_mutex_lock(&threadpool->mutex); + if (threadpool->pause) { + ggml_threadpool_resume_locked(threadpool); + } + ggml_mutex_unlock(&threadpool->mutex); +#else + UNUSED(threadpool); +#endif +} + +struct ggml_cplan ggml_graph_plan( + const struct ggml_cgraph * cgraph, + int n_threads, + struct ggml_threadpool * threadpool) { + + if (threadpool == NULL) { + //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); + } + if (n_threads <= 0) { + n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; + } + + size_t work_size = 0; + + struct ggml_cplan cplan; + memset(&cplan, 0, sizeof(struct ggml_cplan)); + + int max_tasks = 1; + + // thread scheduling for the different operations + work buffer size estimation + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + const int n_tasks = ggml_get_n_tasks(node, n_threads); + + max_tasks = MAX(max_tasks, n_tasks); + + size_t cur = 0; + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + { + if (ggml_is_quantized(node->type) || + // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 + (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || + (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; + } + } break; + case GGML_OP_ADD: + case GGML_OP_ADD1: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; + } + } break; + case GGML_OP_ACC: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; + } + } break; + case GGML_OP_COUNT_EQUAL: + { + cur = ggml_type_size(node->type)*n_tasks; + } break; + case GGML_OP_MUL_MAT: + { + const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type; + + if (node->src[1]->type != vec_dot_type) { + cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); + } + } break; + case GGML_OP_MUL_MAT_ID: + { + cur = 0; + const struct ggml_tensor * src0 = node->src[0]; + const struct ggml_tensor * src1 = node->src[1]; + const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type; + if (src1->type != vec_dot_type) { + cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); + } + const int n_as = src0->ne[2]; + cur += GGML_PAD(cur, sizeof(int64_t)); // align + cur += n_as * sizeof(int64_t); // matrix_row_counts + cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows + } break; + case GGML_OP_OUT_PROD: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; + } + } break; + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + { + cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + GGML_ASSERT(node->src[0]->ne[3] == 1); + GGML_ASSERT(node->src[1]->ne[2] == 1); + GGML_ASSERT(node->src[1]->ne[3] == 1); + + const int64_t ne00 = node->src[0]->ne[0]; // K + const int64_t ne01 = node->src[0]->ne[1]; // Cout + const int64_t ne02 = node->src[0]->ne[2]; // Cin + + const int64_t ne10 = node->src[1]->ne[0]; // L + const int64_t ne11 = node->src[1]->ne[1]; // Cin + + if ((node->src[0]->type == GGML_TYPE_F16 || + node->src[0]->type == GGML_TYPE_BF16) && + node->src[1]->type == GGML_TYPE_F32) { + cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; + cur += sizeof(ggml_fp16_t)*ne10*ne11; + } else if (node->src[0]->type == GGML_TYPE_F32 && + node->src[1]->type == GGML_TYPE_F32) { + cur += sizeof(float)*ne00*ne01*ne02; + cur += sizeof(float)*ne10*ne11; + } else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + const int64_t ne00 = node->src[0]->ne[0]; // W + const int64_t ne01 = node->src[0]->ne[1]; // H + const int64_t ne02 = node->src[0]->ne[2]; // Channels Out + const int64_t ne03 = node->src[0]->ne[3]; // Channels In + + const int64_t ne10 = node->src[1]->ne[0]; // W + const int64_t ne11 = node->src[1]->ne[1]; // H + const int64_t ne12 = node->src[1]->ne[2]; // Channels In + + cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; + cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; + } break; + case GGML_OP_FLASH_ATTN_EXT: + { + const int64_t ne00 = node->src[0]->ne[0]; // D + + cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + const int64_t D = node->src[0]->ne[0]; + const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); + const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back + if (node->src[1]->type == GGML_TYPE_F32) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_F16) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_BF16) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } + } break; + + case GGML_OP_CROSS_ENTROPY_LOSS: + { + cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + default: + break; + } + + work_size = MAX(work_size, cur); + } + + if (work_size > 0) { + work_size += CACHE_LINE_SIZE*(n_threads); + } + + cplan.threadpool = threadpool; + cplan.n_threads = MIN(max_tasks, n_threads); + cplan.work_size = work_size; + cplan.work_data = NULL; + + return cplan; +} + +static thread_ret_t ggml_graph_compute_thread(void * data) { + struct ggml_compute_state * state = (struct ggml_compute_state *) data; + struct ggml_threadpool * tp = state->threadpool; + + const struct ggml_cgraph * cgraph = tp->cgraph; + const struct ggml_cplan * cplan = tp->cplan; + + set_numa_thread_affinity(state->ith); + + struct ggml_compute_params params = { + /*.ith =*/ state->ith, + /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + /*.threadpool=*/ tp, + }; + + for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { + struct ggml_tensor * node = cgraph->nodes[node_n]; + + ggml_compute_forward(¶ms, node); + + if (state->ith == 0 && cplan->abort_callback && + cplan->abort_callback(cplan->abort_callback_data)) { + tp->abort = true; + tp->ec = GGML_STATUS_ABORTED; + } + + ggml_barrier(state->threadpool); + } + + return 0; +} + +#ifndef GGML_USE_OPENMP + +// check if thread is active +static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); + return (state->ith < n_threads); +} + +// check if thread is ready to proceed (exit from polling or sleeping) +static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + if (state->pending || threadpool->stop || threadpool->pause) { return true; } + + // check for new graph/work + int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); + if (new_graph != state->last_graph) { + state->pending = ggml_graph_compute_thread_active(state); + state->last_graph = new_graph; + } + + return state->pending; +} + +// sync thread state after polling +static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { + // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead + #ifdef GGML_TSAN_ENABLED + atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); + #else + atomic_thread_fence(memory_order_seq_cst); + #endif + UNUSED(state); +} + +static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + // Skip polling for unused threads + if (!ggml_graph_compute_thread_active(state)) { + return state->pending; + } + + // This seems to make 0 ... 100 a decent range for polling level across modern processors. + // Perhaps, we can adjust it dynamically based on load and things. + const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; + + for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { + // No new work. Keep polling. + ggml_thread_cpu_relax(); + } + + return state->pending; +} + +static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + if (ggml_graph_compute_poll_for_work(state)) { + ggml_graph_compute_thread_sync(state); + return state->pending; + } + + ggml_mutex_lock_shared(&threadpool->mutex); + while (!ggml_graph_compute_thread_ready(state)) { + // No new work. Wait for the signal. + GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); + ggml_cond_wait(&threadpool->cond, &threadpool->mutex); + } + ggml_mutex_unlock_shared(&threadpool->mutex); + + return state->pending; +} + +static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { + struct ggml_compute_state * state = (struct ggml_compute_state *) data; + struct ggml_threadpool * threadpool = state->threadpool; + + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(state->cpumask)) { + ggml_thread_apply_affinity(state->cpumask); + } + + while (true) { + // Check if we need to sleep + while (threadpool->pause) { + GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); + ggml_mutex_lock_shared(&threadpool->mutex); + if (threadpool->pause) { + ggml_cond_wait(&threadpool->cond, &threadpool->mutex); + } + GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); + ggml_mutex_unlock_shared(&threadpool->mutex); + } + + // This needs to be checked for after the cond_wait + if (threadpool->stop) break; + + // Check if there is new work + // The main thread is the only one that can dispatch new work + + ggml_graph_compute_check_for_work(state); + if (state->pending) { + state->pending = false; + + ggml_graph_compute_thread(state); + } + } + + return (thread_ret_t) 0; +} + +// Start processing new graph +static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) +{ + // Always take the mutex here because the worker threads are doing hybrid poll/wait + + ggml_mutex_lock(&threadpool->mutex); + + GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); + + // Update the number of active threads + atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + + // Indicate the graph is ready to be processed + // We need the full seq-cst fence here because of the polling threads (used in thread_sync) + atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); + + if (threadpool->pause) { + // Update main thread prio and affinity to match the threadpool settings + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { + ggml_thread_apply_affinity(threadpool->workers[0].cpumask); + } + + // resume does cond broadcast + ggml_threadpool_resume_locked(threadpool); + } else { + ggml_cond_broadcast(&threadpool->cond); + } + + ggml_mutex_unlock(&threadpool->mutex); +} + +#endif // GGML_USE_OPENMP + +void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { + p->n_threads = n_threads; + p->prio = 0; // default priority (usually means normal or inherited) + p->poll = 50; // hybrid-polling enabled + p->strict_cpu = false; // no strict placement (all threads share same cpumask) + p->paused = false; // threads are ready to go + memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) +} + +struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { + struct ggml_threadpool_params p; + ggml_threadpool_params_init(&p, n_threads); + return p; +} + +bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { + if (p0->n_threads != p1->n_threads ) return false; + if (p0->prio != p1->prio ) return false; + if (p0->poll != p1->poll ) return false; + if (p0->strict_cpu != p1->strict_cpu ) return false; + return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; +} + +static struct ggml_threadpool * ggml_threadpool_new_impl( + struct ggml_threadpool_params * tpp, + struct ggml_cgraph * cgraph, + struct ggml_cplan * cplan) { + + struct ggml_threadpool * threadpool = + ggml_aligned_malloc(sizeof(struct ggml_threadpool)); + { + threadpool->cgraph = cgraph; + threadpool->cplan = cplan; + threadpool->n_graph = 0; + threadpool->n_barrier = 0; + threadpool->n_barrier_passed = 0; + threadpool->current_chunk = 0; + threadpool->stop = false; + threadpool->pause = tpp->paused; + threadpool->abort = false; + threadpool->workers = NULL; + threadpool->n_threads_max = tpp->n_threads; + threadpool->n_threads_cur = tpp->n_threads; + threadpool->poll = tpp->poll; + threadpool->prio = tpp->prio; + threadpool->ec = GGML_STATUS_SUCCESS; + } + + // Allocate and init workers state + const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; + struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); + + memset(workers, 0, workers_size); + for (int j = 0; j < tpp->n_threads; j++) { + workers[j].threadpool = threadpool; + workers[j].ith = j; + } + + threadpool->workers = workers; + +#ifndef GGML_USE_OPENMP + ggml_mutex_init(&threadpool->mutex); + ggml_cond_init(&threadpool->cond); + + // Spin the threads for all workers, and update CPU placements. + // Place the main thread last (towards the higher numbered CPU cores). + + int32_t cpumask_iter = 0; + + for (int j = 1; j < tpp->n_threads; j++) { + ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); + + int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); + GGML_ASSERT(rc == 0); + } + + ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); + + if (!threadpool->pause) { + // Update main thread prio and affinity at the start, otherwise we'll do it in resume + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { + ggml_thread_apply_affinity(threadpool->workers[0].cpumask); + } + } +#endif // GGML_USE_OPENMP + + return threadpool; +} + +struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { + return ggml_threadpool_new_impl(tpp, NULL, NULL); +} + +enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { + ggml_cpu_init(); + + GGML_ASSERT(cplan); + GGML_ASSERT(cplan->n_threads > 0); + GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); + + int n_threads = cplan->n_threads; + struct ggml_threadpool * threadpool = cplan->threadpool; + + bool disposable_threadpool = false; + + if (threadpool == NULL) { + //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); + disposable_threadpool = true; + + struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); + threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); + } else { + // Reset some of the parameters that need resetting + // No worker threads should be accessing the parameters below at this stage + threadpool->cgraph = cgraph; + threadpool->cplan = cplan; + threadpool->current_chunk = 0; + threadpool->abort = false; + threadpool->ec = GGML_STATUS_SUCCESS; + } + +#ifdef GGML_USE_OPENMP + if (n_threads > 1) { + #pragma omp parallel num_threads(n_threads) + { + #pragma omp single + { + // update the number of threads from the actual number of threads that we got from OpenMP + n_threads = omp_get_num_threads(); + atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + } + + ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); + } + } else { + atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); + ggml_graph_compute_thread(&threadpool->workers[0]); + } +#else + if (n_threads > threadpool->n_threads_max) { + GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); + n_threads = threadpool->n_threads_max; + } + + // Kick all threads to start the new graph + ggml_graph_compute_kickoff(threadpool, n_threads); + + // This is a work thread too + ggml_graph_compute_thread(&threadpool->workers[0]); +#endif + + // don't leave affinity set on the main thread + clear_numa_thread_affinity(); + + enum ggml_status ret = threadpool->ec; + + if (disposable_threadpool) { + ggml_threadpool_free(threadpool); + } + + return ret; +} + +enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { + struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); + + cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size); + + return ggml_graph_compute(cgraph, &cplan); +} + + +int ggml_cpu_has_avx(void) { +#if defined(__AVX__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx_vnni(void) { +#if defined(__AVXVNNI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx2(void) { +#if defined(__AVX2__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512(void) { +#if defined(__AVX512F__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_vbmi(void) { +#if defined(__AVX512VBMI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_vnni(void) { +#if defined(__AVX512VNNI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_bf16(void) { +#if defined(__AVX512BF16__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_amx_int8(void) { +#if defined(__AMX_INT8__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_fma(void) { +#if defined(__FMA__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_arm_fma(void) { +#if defined(__ARM_FEATURE_FMA) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_riscv_v(void) { +#if defined(__riscv_v_intrinsic) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_f16c(void) { +#if defined(__F16C__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_fp16_va(void) { +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_wasm_simd(void) { +#if defined(__wasm_simd128__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_llamafile(void) { +#if defined(GGML_USE_LLAMAFILE) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_sse3(void) { +#if defined(__SSE3__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_ssse3(void) { +#if defined(__SSSE3__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_vsx(void) { +#if defined(__POWER9_VECTOR__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_neon(void) { +#if defined(__ARM_ARCH) && defined(__ARM_NEON) + return ggml_arm_arch_features.has_neon; +#else + return 0; +#endif +} + +int ggml_cpu_has_sve(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) + return ggml_arm_arch_features.has_sve; +#else + return 0; +#endif +} + +int ggml_cpu_has_matmul_int8(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8) + return ggml_arm_arch_features.has_i8mm; +#else + return 0; +#endif +} + +int ggml_cpu_get_sve_cnt(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) + return ggml_arm_arch_features.sve_cnt; +#else + return 0; +#endif +} + +void ggml_cpu_init(void) { + // needed to initialize f16 tables + { + struct ggml_init_params params = { 0, NULL, false }; + struct ggml_context * ctx = ggml_init(params); + ggml_free(ctx); + } + + ggml_critical_section_start(); + + static bool is_first_call = true; + + if (is_first_call) { + // initialize GELU, Quick GELU, SILU and EXP F32 tables + { + const uint64_t t_start = ggml_time_us(); UNUSED(t_start); + + for (int i = 0; i < (1 << 16); ++i) { + union { + uint16_t u16; + ggml_fp16_t fp16; + } u = {i}; + float f = GGML_FP16_TO_FP32(u.fp16); + ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); + ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); + } + + const uint64_t t_end = ggml_time_us(); UNUSED(t_end); + + GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0); + } + +#if defined(__ARM_ARCH) + ggml_init_arm_arch_features(); +#endif + + is_first_call = false; + } + + ggml_critical_section_end(); +} diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp new file mode 100644 index 0000000000..573b7c5b9b --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -0,0 +1,663 @@ +#include "ggml-backend.h" +#include "ggml-backend-impl.h" +#include "ggml-cpu.h" +#include "ggml-cpu-aarch64.h" +#include "ggml-impl.h" +#include +#include +#include + +#if defined(__APPLE__) +#include +#include +#endif + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include +#endif + +// ggml-backend interface + +#ifdef GGML_USE_CPU_HBM + +// buffer type HBM + +#include + +static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_HBM"; + + GGML_UNUSED(buft); +} + +static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { + hbw_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr; + int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); + if (result != 0) { + GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size); + return NULL; + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_hbm; +} +#endif + +// buffer type AARCH64 + +static void ggml_backend_cpu_aarch64_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + tensor->extra = (void *)ggml_aarch64_get_optimal_repack_type(tensor); // NOLINT + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_aarch64_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + enum ggml_type repack_type = (enum ggml_type)(intptr_t)tensor->extra; + + ggml_aarch64_repack_tensor(tensor, repack_type, data, size); + + GGML_UNUSED(buffer); +} + +static const char * ggml_backend_cpu_aarch64_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_AARCH64"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cpu_aarch64_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + auto * buffer = ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + + if (buffer == NULL) { + return NULL; + } + + buffer->buft = buft; + buffer->iface.init_tensor = ggml_backend_cpu_aarch64_buffer_init_tensor; + buffer->iface.set_tensor = ggml_backend_cpu_aarch64_buffer_set_tensor; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_aarch64 = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_aarch64_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_aarch64_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ NULL, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_aarch64; +} + +bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft) { + return buft == ggml_backend_cpu_aarch64_buffer_type(); +} + +static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) { + static std::vector bufts = []() { + std::vector bufts; + +#ifdef GGML_USE_CPU_HBM + bufts.push_back(ggml_backend_cpu_hbm_buffer_type()); +#endif + +#ifdef GGML_USE_CPU_AARCH64 + bufts.push_back(ggml_backend_cpu_aarch64_buffer_type()); +#endif + + bufts.push_back(NULL); + + return bufts; + }(); + + return bufts.data(); + + GGML_UNUSED(device); +} + +// CPU backend - backend (stream) + +struct ggml_backend_cpu_context { + int n_threads; + ggml_threadpool_t threadpool; + + uint8_t * work_data; + size_t work_size; + + ggml_abort_callback abort_callback; + void * abort_callback_data; +}; + +static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { + return "CPU"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_cpu_free(ggml_backend_t backend) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + delete[] cpu_ctx->work_data; + delete cpu_ctx; + delete backend; +} + +struct ggml_backend_plan_cpu { + struct ggml_cplan cplan; + struct ggml_cgraph cgraph; +}; + +static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_backend_plan_cpu * cpu_plan = new ggml_backend_plan_cpu; + + cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); + cpu_plan->cgraph = *cgraph; // FIXME: deep copy + + if (cpu_plan->cplan.work_size > 0) { + cpu_plan->cplan.work_data = new uint8_t[cpu_plan->cplan.work_size]; + if (cpu_plan->cplan.work_data == NULL) { + delete cpu_plan; + return NULL; + } + } + + cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; + cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return cpu_plan; +} + +static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + delete[] cpu_plan->cplan.work_data; + delete cpu_plan; + + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); + + if (cpu_ctx->work_size < cplan.work_size) { + delete[] cpu_ctx->work_data; + cpu_ctx->work_data = new uint8_t[cplan.work_size]; + if (cpu_ctx->work_data == NULL) { + cpu_ctx->work_size = 0; + return GGML_STATUS_ALLOC_FAILED; + } + cpu_ctx->work_size = cplan.work_size; + } + cplan.work_data = (uint8_t *)cpu_ctx->work_data; + + cplan.abort_callback = cpu_ctx->abort_callback; + cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return ggml_graph_compute(cgraph, &cplan); +} + +static const struct ggml_backend_i ggml_backend_cpu_i = { + /* .get_name = */ ggml_backend_cpu_get_name, + /* .free = */ ggml_backend_cpu_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, + /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, + /* .graph_compute = */ ggml_backend_cpu_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; + +static ggml_guid_t ggml_backend_cpu_guid(void) { + static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; + return &guid; +} + +ggml_backend_t ggml_backend_cpu_init(void) { + // initialize CPU backend now to avoid slowing the first graph computation + ggml_cpu_init(); + + struct ggml_backend_cpu_context * ctx = new ggml_backend_cpu_context; + if (ctx == NULL) { + return NULL; + } + + ctx->n_threads = GGML_DEFAULT_N_THREADS; + ctx->threadpool = NULL; + ctx->work_data = NULL; + ctx->work_size = 0; + ctx->abort_callback = NULL; + ctx->abort_callback_data = NULL; + + ggml_backend_t cpu_backend = new ggml_backend { + /* .guid = */ ggml_backend_cpu_guid(), + /* .interface = */ ggml_backend_cpu_i, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ ctx, + }; + + if (cpu_backend == NULL) { + delete ctx; + return NULL; + } + + return cpu_backend; +} + +bool ggml_backend_is_cpu(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); +} + +void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->n_threads = n_threads; +} + +void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + + if (ctx->threadpool && ctx->threadpool != threadpool) { + // already had a different threadpool, pause/suspend it before switching + ggml_threadpool_pause(ctx->threadpool); + } + ctx->threadpool = threadpool; +} + +void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + +// CPU backend - device + +struct ggml_backend_cpu_device_context { + std::string description = "CPU"; + + ggml_backend_cpu_device_context() { +#ifdef __APPLE__ + size_t len = 0; + if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) { + description.resize(len); + sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT + } +#elif defined(__linux__) + FILE * f = fopen("/proc/cpuinfo", "r"); + if (f) { + char buf[1024]; + while (fgets(buf, sizeof(buf), f)) { + if (strncmp(buf, "model name", 10) == 0) { + char * p = strchr(buf, ':'); + if (p) { + p++; + while (std::isspace(*p)) { + p++; + } + while (std::isspace(p[strlen(p) - 1])) { + p[strlen(p) - 1] = '\0'; + } + description = p; + break; + } + } + } + fclose(f); + } +#elif defined(_WIN32) + HKEY hKey; + if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, + TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), + 0, + KEY_READ, + &hKey) == ERROR_SUCCESS) { + DWORD cpu_brand_size = 0; + if (RegQueryValueExA(hKey, + TEXT("ProcessorNameString"), + NULL, + NULL, + NULL, + &cpu_brand_size) == ERROR_SUCCESS) { + description.resize(cpu_brand_size); + if (RegQueryValueExA(hKey, + TEXT("ProcessorNameString"), + NULL, + NULL, + (LPBYTE)&description[0], // NOLINT + &cpu_brand_size) == ERROR_SUCCESS) { + if (description.find('\0') != std::string::npos) { + description.resize(description.find('\0')); + } + } + } + RegCloseKey(hKey); + } +#endif + } +}; + +static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) { + return "CPU"; + + GGML_UNUSED(dev); +} + +static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) { + struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context; + + return ctx->description.c_str(); +} + +static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + // TODO + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_CPU; + + GGML_UNUSED(dev); +} + +static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_cpu_device_get_name(dev); + props->description = ggml_backend_cpu_device_get_description(dev); + props->type = ggml_backend_cpu_device_get_type(dev); + ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ true, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) { + return ggml_backend_cpu_init(); + + GGML_UNUSED(dev); + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(dev); +} + +static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + return ggml_backend_cpu_buffer_from_ptr(ptr, size); + + GGML_UNUSED(dev); + GGML_UNUSED(max_tensor_size); +} + +static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + if (src0 && src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { + if (op->op != GGML_OP_MUL_MAT || src0->type != GGML_TYPE_Q4_0 || ggml_aarch64_get_optimal_repack_type(src0) == GGML_TYPE_Q4_0) { + return false; + } + } + + for (int i = 1; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_cpu_buft_is_aarch64(op->src[i]->buffer->buft)) { + return false; + } + } + + switch (op->op) { + case GGML_OP_CPY: + return + op->type != GGML_TYPE_IQ2_XXS && + op->type != GGML_TYPE_IQ2_XS && + op->type != GGML_TYPE_IQ1_S && + op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float + case GGML_OP_MUL_MAT: + return src1->type == GGML_TYPE_F32 || src1->type == ggml_get_type_traits_cpu(src0->type)->vec_dot_type; + case GGML_OP_ROPE_BACK: + return op->src[2] == NULL && (op->op_params[2] & 4) == 0; + case GGML_OP_IM2COL_BACK: + return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32; + case GGML_OP_OUT_PROD: + return (src0->type == GGML_TYPE_F32 || ggml_is_quantized(src0->type)) && src1->type == GGML_TYPE_F32; + default: + return true; + } + + GGML_UNUSED(dev); +} + +static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return ggml_backend_buft_is_host(buft) || ggml_backend_cpu_buft_is_aarch64(buft); + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { + /* .get_name = */ ggml_backend_cpu_device_get_name, + /* .get_description = */ ggml_backend_cpu_device_get_description, + /* .get_memory = */ ggml_backend_cpu_device_get_memory, + /* .get_type = */ ggml_backend_cpu_device_get_type, + /* .get_props = */ ggml_backend_cpu_device_get_props, + /* .init_backend = */ ggml_backend_cpu_device_init_backend, + /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_cpu_device_supports_op, + /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// CPU backend - backend (reg) + +static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { + return "CPU"; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) { + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + static ggml_backend_cpu_device_context ctx; + static ggml_backend_device ggml_backend_cpu_device = { + /* .iface = */ ggml_backend_cpu_device_i, + /* .reg = */ reg, + /* .context = */ &ctx, + }; + + return &ggml_backend_cpu_device; +} + +struct ggml_backend_feature { + const char * name; + const char * value; +}; + +// Not used yet +// This is intended to replace the the ggml_cpu_has_* functions when loading the CPU backend dynamically, +// and additionally to allow other backends to expose their own list of features that applications can query using the same API. +static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t reg) { + static std::vector features = []() { + std::vector features; + if (ggml_cpu_has_sse3()) { + features.push_back({ "SSE3", "1" }); + } + if (ggml_cpu_has_ssse3()) { + features.push_back({ "SSSE3", "1" }); + } + if (ggml_cpu_has_avx()) { + features.push_back({ "AVX", "1" }); + } + if (ggml_cpu_has_avx2()) { + features.push_back({ "AVX2", "1" }); + } + if (ggml_cpu_has_f16c()) { + features.push_back({ "F16C", "1" }); + } + if (ggml_cpu_has_fma()) { + features.push_back({ "FMA", "1" }); + } + if (ggml_cpu_has_avx_vnni()) { + features.push_back({ "AVX_VNNI", "1" }); + } + if (ggml_cpu_has_avx512()) { + features.push_back({ "AVX512", "1" }); + } + if (ggml_cpu_has_avx512_vbmi()) { + features.push_back({ "AVX512_VBMI", "1" }); + } + if (ggml_cpu_has_avx512_vnni()) { + features.push_back({ "AVX512_VNNI", "1" }); + } + if (ggml_cpu_has_avx512_bf16()) { + features.push_back({ "AVX512_BF16", "1" }); + } + if (ggml_cpu_has_amx_int8()) { + features.push_back({ "AMX_INT8", "1" }); + } + if (ggml_cpu_has_neon()) { + features.push_back({ "NEON", "1" }); + } + if (ggml_cpu_has_arm_fma()) { + features.push_back({ "ARM_FMA", "1" }); + } + if (ggml_cpu_has_fp16_va()) { + features.push_back({ "FP16_VA", "1" }); + } + if (ggml_cpu_has_matmul_int8()) { + features.push_back({ "MATMUL_INT8", "1" }); + } + if (ggml_cpu_has_sve()) { + features.push_back({ "SVE", "1" }); + } + if (ggml_cpu_get_sve_cnt() > 0) { + static std::string sve_cnt = std::to_string(ggml_cpu_get_sve_cnt()); + features.push_back({ "SVE_CNT", sve_cnt.c_str() }); + } + if (ggml_cpu_has_riscv_v()) { + features.push_back({ "RISCV_V", "1" }); + } + if (ggml_cpu_has_vsx()) { + features.push_back({ "VSX", "1" }); + } + if (ggml_cpu_has_wasm_simd()) { + features.push_back({ "WASM_SIMD", "1" }); + } + if (ggml_cpu_has_llamafile()) { + features.push_back({ "LLAMAFILE", "1" }); + } + + features.push_back({ nullptr, nullptr }); + + return features; + }(); + + return features.data(); + + GGML_UNUSED(reg); +} + +static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (strcmp(name, "ggml_backend_set_n_threads") == 0) { + return (void *)ggml_backend_cpu_set_n_threads; + } + if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { + return (void *)ggml_backend_cpu_get_extra_bufts; + } + + return NULL; + + GGML_UNUSED(reg); +} + +static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = { + /* .get_name = */ ggml_backend_cpu_reg_get_name, + /* .get_device_count = */ ggml_backend_cpu_reg_get_device_count, + /* .get_device = */ ggml_backend_cpu_reg_get_device, + /* .get_proc_address = */ ggml_backend_cpu_get_proc_address, +}; + +ggml_backend_reg_t ggml_backend_cpu_reg(void) { + // init CPU feature detection + ggml_cpu_init(); + + static struct ggml_backend_reg ggml_backend_cpu_reg = { + /* .iface = */ ggml_backend_cpu_reg_i, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_reg; +} diff --git a/ggml/src/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp similarity index 60% rename from ggml/src/llamafile/sgemm.cpp rename to ggml/src/ggml-cpu/llamafile/sgemm.cpp index 0193a463ae..b2ce2e6649 100644 --- a/ggml/src/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -50,7 +50,8 @@ #include "sgemm.h" #include "ggml-impl.h" -#include "ggml-cpu-impl.h" +// hack until moved into the CPU backend +#include "../ggml-cpu-impl.h" #include "ggml-quants.h" #ifdef _MSC_VER @@ -106,6 +107,10 @@ inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#if defined(__MMA__) +typedef vector unsigned char vec_t; +typedef __vector_quad acc_t; +#endif //////////////////////////////////////////////////////////////////////////////////////////////////// // VECTORIZED FUSED MULTIPLY ADD @@ -942,6 +947,36 @@ class tinyBLAS_Q0_AVX { return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8)); } + inline __m256i load(const block_q5_0 *b) { + return _mm256_or_si256(denibble(b->qs), bittobyte(b->qh)); + } + + inline __m128i load0(const block_q5_0* b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + uint32_t x32; + memcpy(&x32, b->qh, sizeof(uint32_t)); + __m128i qxl = _mm_and_si128(_mm_set1_epi8(15), x); + __m128i bytesl = _mm_cmpeq_epi8(_mm_set1_epi64x(-1), + _mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm_shuffle_epi8(_mm_set1_epi32(x32), + _mm_set_epi64x(0x0101010101010101, 0x0000000000000000)))); + bytesl = _mm_andnot_si128(bytesl, _mm_set1_epi8((char)0xF0)); + return _mm_or_si128(qxl, bytesl); + } + + inline __m128i load1(const block_q5_0* b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + uint32_t x32; + memcpy(&x32, b->qh, sizeof(uint32_t)); + __m128i qxh = _mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)); + __m128i bytesh = _mm_cmpeq_epi8(_mm_set1_epi64x(-1), + _mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm_shuffle_epi8(_mm_set1_epi32(x32), + _mm_set_epi64x(0x0303030303030303, 0x0202020202020202)))); + bytesh = _mm_andnot_si128(bytesh, _mm_set1_epi8((char)0xF0)); + return _mm_or_si128(qxh, bytesh); + } + inline __m256i load(const block_iq4_nl *b) { return MM256_SET_M128I(load1(b), load0(b)); } @@ -973,6 +1008,17 @@ class tinyBLAS_Q0_AVX { _mm_srli_epi16(x, 4), 1)); } + static inline __m256i bittobyte(const uint8_t *p) { + uint32_t x32; + memcpy(&x32, p, sizeof(uint32_t)); + __m256i bytes = _mm256_cmpeq_epi8(_mm256_set1_epi64x(-1), + _mm256_or_si256(_mm256_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm256_shuffle_epi8(_mm256_set1_epi32(x32), + _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000)))); + return _mm256_andnot_si256(bytes, _mm256_set1_epi8((char)0xF0)); + } + const TA *const A; const TB *const B; TC *const C; @@ -985,6 +1031,600 @@ class tinyBLAS_Q0_AVX { }; #endif // __AVX__ +//PPC Implementation +#if defined(__MMA__) + +#define SAVE_ACC(ACC, ii, jj) \ + __builtin_mma_disassemble_acc(vec_C, ACC); \ + for (int I = 0; I < 4; I++) { \ + for (int J = 0; J < 4; J++) { \ + *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); \ + } \ + } \ + +template +class tinyBLAS_PPC { + public: + tinyBLAS_PPC(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n) { + mnpack(0, m, 0, n); + } + + private: + + void (tinyBLAS_PPC::*kernel)(int64_t, int64_t); + + void READ_BLOCK(const float* a, int64_t lda, int rows, int cols, float* vec) { + int64_t i, j; + float *aoffset = NULL, *boffset = NULL; + float *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL; + float *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL; + + aoffset = const_cast(a); + boffset = vec; + j = (rows >> 3); + if (j > 0) { + do { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + aoffset4 = aoffset3 + lda; + aoffset5 = aoffset4 + lda; + aoffset6 = aoffset5 + lda; + aoffset7 = aoffset6 + lda; + aoffset8 = aoffset7 + lda; + aoffset += 8 * lda; + i = (cols >> 3); + if (i > 0) { + __vector_pair C1, C2, C3, C4, C5, C6, C7, C8; + vector float c1[2], c2[2], c3[2], c4[2], c5[2], c6[2], c7[2], c8[2]; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + do { + C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1); + C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2); + C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3); + C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4); + C5 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset5); + C6 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset6); + C7 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset7); + C8 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset8); + __builtin_vsx_disassemble_pair(c1, &C1); + __builtin_vsx_disassemble_pair(c2, &C2); + __builtin_vsx_disassemble_pair(c3, &C3); + __builtin_vsx_disassemble_pair(c4, &C4); + __builtin_vsx_disassemble_pair(c5, &C5); + __builtin_vsx_disassemble_pair(c6, &C6); + __builtin_vsx_disassemble_pair(c7, &C7); + __builtin_vsx_disassemble_pair(c8, &C8); + + t1 = vec_mergeh(c1[0], c2[0]); + t2 = vec_mergeh(c3[0], c4[0]); + t3 = vec_mergeh(c5[0], c6[0]); + t4 = vec_mergeh(c7[0], c8[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergel(c1[0], c2[0]); + t2 = vec_mergel(c3[0], c4[0]); + t3 = vec_mergel(c5[0], c6[0]); + t4 = vec_mergel(c7[0], c8[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + + t1 = vec_mergeh(c1[1], c2[1]); + t2 = vec_mergeh(c3[1], c4[1]); + t3 = vec_mergeh(c5[1], c6[1]); + t4 = vec_mergeh(c7[1], c8[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+32); + vec_xst(t6, 0, boffset+36); + vec_xst(t7, 0, boffset+40); + vec_xst(t8, 0, boffset+44); + + t1 = vec_mergel(c1[1], c2[1]); + t2 = vec_mergel(c3[1], c4[1]); + t3 = vec_mergel(c5[1], c6[1]); + t4 = vec_mergel(c7[1], c8[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+48); + vec_xst(t6, 0, boffset+52); + vec_xst(t7, 0, boffset+56); + vec_xst(t8, 0, boffset+60); + + aoffset1 += 8*lda; + aoffset2 += 8*lda; + aoffset3 += 8*lda; + aoffset4 += 8*lda; + boffset += 64; + i--; + } while(i > 0); + } + if (cols & 4) { + vector float c1, c2, c3, c4, c5, c6, c7, c8; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + c4 = vec_xl(0, aoffset4); + c5 = vec_xl(0, aoffset5); + c6 = vec_xl(0, aoffset6); + c7 = vec_xl(0, aoffset7); + c8 = vec_xl(0, aoffset8); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_mergeh(c5, c6); + t4 = vec_mergeh(c7, c8); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_mergel(c5, c6); + t4 = vec_mergel(c7, c8); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + } + j--; + } while(j > 0); + } + + if (rows & 4) { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + aoffset4 = aoffset3 + lda; + aoffset += 4 * lda; + i = (cols >> 3); + if (i > 0) { + __vector_pair C1, C2, C3, C4; + vector float c1[2], c2[2], c3[2], c4[2]; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + do { + C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1); + C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2); + C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3); + C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4); + __builtin_vsx_disassemble_pair(c1, &C1); + __builtin_vsx_disassemble_pair(c2, &C2); + __builtin_vsx_disassemble_pair(c3, &C3); + __builtin_vsx_disassemble_pair(c4, &C4); + + t1 = vec_mergeh(c1[0], c2[0]); + t2 = vec_mergeh(c3[0], c4[0]); + t3 = vec_mergel(c1[0], c2[0]); + t4 = vec_mergel(c3[0], c4[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t1, t2, 3); + t7 = vec_xxpermdi(t3, t4, 0); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergeh(c1[1], c2[1]); + t2 = vec_mergeh(c3[1], c4[1]); + t3 = vec_mergel(c1[1], c2[1]); + t4 = vec_mergel(c3[1], c4[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t1, t2, 3); + t7 = vec_xxpermdi(t3, t4, 0); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + + aoffset1 += 8*lda; + aoffset2 += 8*lda; + aoffset3 += 8*lda; + aoffset4 += 8*lda; + boffset += 32; + i--; + } while(i > 0); + } + + if (cols & 4) { + vector float c1, c2, c3, c4; + vector float t1, t2, t3, t4; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + c4 = vec_xl(0, aoffset4); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset); + vec_xst(t4, 0, boffset+4); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset+8); + vec_xst(t4, 0, boffset+12); + } + } + if (rows & 3) { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + if (cols & 4) { + vector float c1, c2, c3, c4 = {0}; + vector float t1, t2, t3, t4; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset); + vec_xst(t4, 0, boffset+4); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset+8); + vec_xst(t4, 0, boffset+12); + } + } + } + + void KERNEL_4x4(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[4], vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + for (int l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]); + } + SAVE_ACC(&acc_0, ii, jj); + } + + void KERNEL_4x8(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[8], vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int64_t l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], (vec_t)vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[0], (vec_t)vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], (vec_t)vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[1], (vec_t)vec_B[3]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], (vec_t)vec_B[4]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[2], (vec_t)vec_B[5]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], (vec_t)vec_B[6]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[3], (vec_t)vec_B[7]); + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + } + + void KERNEL_8x4(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[4], vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int64_t l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 8, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[1], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[2], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[3], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[4], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[5], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[6], vec_B[3]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[7], vec_B[3]); + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii+4, jj); + } + + void KERNEL_8x8(int64_t ii, int64_t jj) { + vec_t vec_A[16], vec_B[16], vec_C[4]; + acc_t acc_0, acc_1, acc_2, acc_3; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + __builtin_mma_xxsetaccz(&acc_2); + __builtin_mma_xxsetaccz(&acc_3); + for (int l = 0; l < k; l+=8) { + READ_BLOCK(A+(ii*lda)+l, lda, 8, 8, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 8, (float*)vec_B); + for(int x = 0; x < 16; x+=2) { + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[x], vec_B[x]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x+1]); + __builtin_mma_xvf32gerpp(&acc_2, (vec_t)vec_A[x+1], vec_B[x]); + __builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x+1], vec_B[x+1]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + SAVE_ACC(&acc_2, ii+4, jj); + SAVE_ACC(&acc_3, ii+4, jj+4); + } + + void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + int m_rem = MIN(m - m0, 16); + int n_rem = MIN(n - n0, 16); + if (m_rem >= 16 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if(m_rem >= 8 && n_rem >= 16) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 8 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 8) { + mc = 4; + nc = 8; + gemm<4,8>(m0, m, n0, n); + } else if (m_rem >= 8 && n_rem >= 4) { + mc = 8; + nc = 4; + gemm<8,4>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 4) { + mc = 4; + nc = 4; + gemm<4,4>(m0, m, n0, n); + } else if ((m_rem < 4) && (n_rem > 4)) { + nc = 4; + switch(m_rem) { + case 1: + mc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 2: + mc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 3: + mc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } else if ((m_rem > 4) && (n_rem < 4)) { + mc = 4; + switch(n_rem) { + case 1: + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 2: + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 3: + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } else { + switch((m_rem << 4) | n_rem) { + case 0x43: + mc = 4; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x42: + mc = 4; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x41: + mc = 4; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x34: + mc = 3; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x33: + mc = 3; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x32: + mc = 3; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x31: + mc = 3; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x24: + mc = 2; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x23: + mc = 2; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x22: + mc = 2; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x21: + mc = 2; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x14: + mc = 1; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x13: + mc = 1; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x12: + mc = 1; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x11: + mc = 1; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n, int RM, int RN) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + vec_t vec_A[4], vec_B[4]; + for (int l=0; l= 4 && RM == 1) { + float* a = const_cast(A+(ii)*lda+l); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + vec_A[0] = (vec_t)vec_xl(0,a); + vec_A[1] = (vec_t)vec_splats(*((float*)&vec_A+1)); + vec_A[2] = (vec_t)vec_splats(*((float*)&vec_A+2)); + vec_A[3] = (vec_t)vec_splats(*((float*)&vec_A+3)); + } else { + READ_BLOCK(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B); + } + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]); + } + __builtin_mma_disassemble_acc(vec_C, &acc_0); + for (int I = 0; I < RM; I++) { + for (int J = 0; J < RN; J++) { + *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); + } + } + } + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (RM == 4 && RN == 4) { + kernel = &tinyBLAS_PPC::KERNEL_4x4; + } else if (RM == 4 && RN == 8) { + kernel = &tinyBLAS_PPC::KERNEL_4x8; + } else if (RM == 8 && RN == 4) { + kernel = &tinyBLAS_PPC::KERNEL_8x4; + } else if (RM == 8 && RN == 8) { + kernel = &tinyBLAS_PPC::KERNEL_8x8; + } + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + (this->*kernel)(ii, jj); + } + } + + const TA *const A; + const TB *const B; + TC *C; + TA *At; + TB *Bt; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; +#endif } // namespace /** @@ -1073,6 +1713,16 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda ith, nth}; tb.matmul(m, n); return true; +#elif defined(__MMA__) + if (k % 8) + return false; + tinyBLAS_PPC tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n); + return true; #else return false; #endif @@ -1182,6 +1832,22 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda #endif } + case GGML_TYPE_Q5_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) + tinyBLAS_Q0_AVX tb{ + k, (const block_q5_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n); + return true; +#else + return false; +#endif + } + case GGML_TYPE_IQ4_NL: { if (Btype != GGML_TYPE_Q8_0) return false; diff --git a/ggml/src/llamafile/sgemm.h b/ggml/src/ggml-cpu/llamafile/sgemm.h similarity index 100% rename from ggml/src/llamafile/sgemm.h rename to ggml/src/ggml-cpu/llamafile/sgemm.h diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt new file mode 100644 index 0000000000..e1482a269d --- /dev/null +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -0,0 +1,155 @@ +cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES + +find_package(CUDAToolkit) + +if (CUDAToolkit_FOUND) + message(STATUS "CUDA Toolkit found") + + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + # native == GPUs available at build time + # 52 == Maxwell, lowest CUDA 12 standard + # 60 == P100, FP16 CUDA intrinsics + # 61 == Pascal, __dp4a instruction (per-byte integer dot product) + # 70 == V100, FP16 tensor cores + # 75 == Turing, int8 tensor cores + if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24") + set(CMAKE_CUDA_ARCHITECTURES "native") + elseif(GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75") + else() + set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75") + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + + enable_language(CUDA) + + file(GLOB GGML_HEADERS_CUDA "*.cuh") + list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_CUDA "*.cu") + file(GLOB SRCS "template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() + + add_library(ggml-cuda + ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_CUDA} + ) + + target_link_libraries(ggml-cuda PRIVATE ggml-base) + target_include_directories(ggml-cuda PRIVATE . ..) + + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + if (WIN32) + # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt) + else () + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) + endif() + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cuda_driver) + endif() + + set(CUDA_CXX_FLAGS "") + + set(CUDA_FLAGS -use_fast_math) + + if (GGML_FATAL_WARNINGS) + list(APPEND CUDA_FLAGS -Werror all-warnings) + endif() + + if (GGML_ALL_WARNINGS AND NOT MSVC) + set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) + if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") + list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) + endif() + + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler --version + OUTPUT_VARIABLE CUDA_CCFULLVER + ERROR_QUIET + ) + + if (NOT CUDA_CCFULLVER MATCHES clang) + set(CUDA_CCID "GNU") + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" + OUTPUT_VARIABLE CUDA_CCVER + ERROR_QUIET + ) + else() + if (CUDA_CCFULLVER MATCHES Apple) + set(CUDA_CCID "AppleClang") + else() + set(CUDA_CCID "Clang") + endif() + string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) + endif() + + message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") + + get_flags(${CUDA_CCID} ${CUDA_CCVER}) + list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later + endif() + + if (NOT MSVC) + list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) + endif() + + list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument + + if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") + list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) + endif() + + target_compile_options(ggml-cuda PRIVATE "$<$:${CUDA_FLAGS}>") +else() + message(FATAL_ERROR "CUDA Toolkit not found") +endif() diff --git a/ggml/src/ggml-cuda/argmax.cu b/ggml/src/ggml-cuda/argmax.cu index aab04eca7a..5340eedc08 100644 --- a/ggml/src/ggml-cuda/argmax.cu +++ b/ggml/src/ggml-cuda/argmax.cu @@ -1,57 +1,69 @@ -#include "common.cuh" -#include "argmax.cuh" -#include "sum.cuh" - +#include #include -static __global__ void argmax_f32( - const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) { +#include "argmax.cuh" +#include "common.cuh" +#include "sum.cuh" - int argmax_thread = 0; - const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE; +static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __restrict__ dst, const int64_t ncols) { + const int64_t row = blockIdx.x; -#pragma unroll - for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) { - const int64_t row = row0 + row1; + float maxval = -FLT_MAX; + int argmax = -1; + const float * rowx = x + row * ncols; - if (row >= nrows) { - break; + for (int32_t col = threadIdx.x; col < ncols; col += blockDim.x) { + const float val = rowx[col]; + if (val > maxval) { + maxval = val; + argmax = col; } - - float maxval = -FLT_MAX; - int argmax = -1; - - for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) { - const float val = x[row*ncols + col]; - const int bigger = val > maxval; - const int not_bigger = bigger ^ 0x00000001; - - maxval = maxval*not_bigger + val*bigger; - argmax = argmax*not_bigger + col*bigger; - } - -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE); - const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE); - const int bigger = val > maxval; - const int not_bigger = bigger ^ 0x00000001; - - maxval = maxval*not_bigger + val*bigger; - argmax = argmax*not_bigger + col*bigger; - } - - const int store = row1 == threadIdx.x; - argmax_thread += store*argmax; } - const int row = row0 + threadIdx.x; - - if (row >= nrows) { - return; +#pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) { + const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE); + const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE); + if (val > maxval) { + maxval = val; + argmax = col; + } } - dst[row] = argmax_thread; + const int n_warps = blockDim.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + if (n_warps > 1) { + constexpr int max_warps = 1024 / WARP_SIZE; + __shared__ float shared_maxval[max_warps]; + __shared__ int shared_argmax[max_warps]; + if (lane_id == 0) { + shared_maxval[warp_id] = maxval; + shared_argmax[warp_id] = argmax; + } + + __syncthreads(); + + if (warp_id == 0) { + if (lane_id < n_warps) { + maxval = shared_maxval[lane_id]; + argmax = shared_argmax[lane_id]; + } +#pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) { + const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE); + const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE); + if (val > maxval) { + maxval = val; + argmax = col; + } + } + } + } + + if (warp_id == 0 && lane_id == 0) { + dst[row] = argmax; + } } void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -70,10 +82,10 @@ void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { cudaStream_t stream = ctx.stream(); - const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE; - - const dim3 blocks_dim(WARP_SIZE, 1, 1); + const int64_t num_blocks = nrows; + const int64_t num_threads = std::min(1024, (ne00 + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE); + const dim3 blocks_dim(num_threads, 1, 1); const dim3 blocks_num(num_blocks, 1, 1); - argmax_f32<<>>(src0_d, dst_d, ne00, nrows); + argmax_f32<<>>(src0_d, dst_d, ne00); } diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index dd203fcded..b0dd16066b 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -6,7 +6,7 @@ #include #include -#if defined(GGML_USE_HIPBLAS) +#if defined(GGML_USE_HIP) #define GGML_COMMON_DECL_HIP #define GGML_COMMON_IMPL_HIP #else @@ -26,13 +26,13 @@ #include #include -#if defined(GGML_USE_HIPBLAS) +#if defined(GGML_USE_HIP) #include "vendors/hip.h" #elif defined(GGML_USE_MUSA) #include "vendors/musa.h" #else #include "vendors/cuda.h" -#endif // defined(GGML_USE_HIPBLAS) +#endif // defined(GGML_USE_HIP) #define STRINGIZE_IMPL(...) #__VA_ARGS__ #define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__) @@ -97,7 +97,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in #define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str) -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIP) static const char * cu_get_error_str(CUresult err) { const char * err_str; cuGetErrorString(err, &err_str); @@ -120,21 +120,21 @@ typedef float dfloat; // dequantize float typedef float2 dfloat2; #endif // GGML_CUDA_F16 -#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #define FP16_AVAILABLE -#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 #define FAST_FP16_AVAILABLE #endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA #define FP16_MMA_AVAILABLE -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING #define INT8_MMA_AVAILABLE -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING #if !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= CC_QY1) #define FLASH_ATTN_AVAILABLE @@ -156,14 +156,14 @@ static constexpr bool int8_mma_available(const int cc) { static __device__ void no_device_code( const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", file_name, line, function_name, arch); GGML_UNUSED(arch_list); #else printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", file_name, line, function_name, arch, arch_list); -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) __trap(); GGML_UNUSED(no_device_code); // suppress unused function warning @@ -176,30 +176,30 @@ static __device__ void no_device_code( #endif // __CUDA_ARCH__ static __device__ __forceinline__ int warp_reduce_sum(int x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE return __reduce_add_sync(0xffffffff, x); #else #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, offset, 32); } return x; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE } static __device__ __forceinline__ float warp_reduce_sum(float x) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, offset, 32); } return x; } static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); - a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32); } return a; } @@ -207,21 +207,21 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { #ifdef FP16_AVAILABLE -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32); reinterpret_cast(a.x) += __low2half(a_other); reinterpret_cast(a.y) += __high2half(a_other); } return a; #else #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32)); } return a; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #else NO_DEVICE_CODE; @@ -231,8 +231,8 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32)); } return x; } @@ -240,11 +240,11 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { #ifdef FP16_AVAILABLE -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX return __float2half(fmaxf(__half2float(a), __half2float(b))); #else return __hmax(a, b); -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX #else NO_DEVICE_CODE; @@ -254,7 +254,7 @@ static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b } static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) #if CUDART_VERSION >= CUDART_HMAX return __hmax2(a, b); @@ -269,20 +269,20 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal GGML_UNUSED(a); GGML_UNUSED(b); NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) } static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32)); } return x; #else GGML_UNUSED(x); NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } #if CUDART_VERSION < CUDART_HMASK @@ -294,7 +294,7 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half #endif // CUDART_VERSION < CUDART_HMASK static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(RDNA2) c = __builtin_amdgcn_sdot4(a, b, c, false); #elif defined(RDNA3) @@ -320,7 +320,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i #endif return c; -#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if __CUDA_ARCH__ >= MIN_CC_DP4A return __dp4a(a, b, c); @@ -330,7 +330,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3]; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } // TODO: move to ggml-common.h diff --git a/ggml/src/ggml-cuda/count-equal.cu b/ggml/src/ggml-cuda/count-equal.cu index ffb053b101..08898115da 100644 --- a/ggml/src/ggml-cuda/count-equal.cu +++ b/ggml/src/ggml-cuda/count-equal.cu @@ -44,7 +44,7 @@ void ggml_cuda_count_equal(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int"); - const int64_t dne = GGML_PAD(ne / (4*nsm), CUDA_COUNT_EQUAL_CHUNK_SIZE); + const int64_t dne = GGML_PAD((ne + 4*nsm - 1) / (4*nsm), CUDA_COUNT_EQUAL_CHUNK_SIZE); CUDA_CHECK(cudaMemsetAsync(dst_d, 0, ggml_nbytes(dst), stream)); diff --git a/ggml/src/ggml-cuda/cpy.cuh b/ggml/src/ggml-cuda/cpy.cuh index 7961674266..28b06cddaa 100644 --- a/ggml/src/ggml-cuda/cpy.cuh +++ b/ggml/src/ggml-cuda/cpy.cuh @@ -1,6 +1,6 @@ #include "common.cuh" -#define CUDA_CPY_BLOCK_SIZE 32 +#define CUDA_CPY_BLOCK_SIZE 64 void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1); diff --git a/ggml/src/ggml-cuda/dmmv.cu b/ggml/src/ggml-cuda/dmmv.cu deleted file mode 100644 index 96a5adef5b..0000000000 --- a/ggml/src/ggml-cuda/dmmv.cu +++ /dev/null @@ -1,683 +0,0 @@ -#include "dmmv.cuh" -#include "dequantize.cuh" -#include "convert.cuh" - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 2 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q2_K * x = (const block_q2_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 16/K_QUANTS_PER_ITERATION; - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int s_offset = 8*im; - const int y_offset = 128*im + l0; - - uint32_t aux[4]; - const uint8_t * d = (const uint8_t *)aux; - const uint8_t * m = (const uint8_t *)(aux + 2); - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset); - aux[0] = a[0] & 0x0f0f0f0f; - aux[1] = a[1] & 0x0f0f0f0f; - aux[2] = (a[0] >> 4) & 0x0f0f0f0f; - aux[3] = (a[1] >> 4) & 0x0f0f0f0f; - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) - + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) - + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) - + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) - + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) - + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) - + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) - +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); - sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] - + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - - } - tmp += dall * sum1 - dmin * sum2; - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q3_K * x = (const block_q3_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop - const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0....15 or 0...7 - - const uint8_t m = 1 << (4*im); - - const int l0 = n*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int y_offset = 128*im + l0; - - uint16_t utmp[4]; - const int8_t * s = (const int8_t *)utmp; - - const uint16_t s_shift = 4*im; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; - const uint8_t * h = x[i].hmask + l0; - - const uint16_t * a = (const uint16_t *)x[i].scales; - utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); - utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); - utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); - utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); - - const float d = x[i].d; - - float sum = 0; - for (int l = 0; l < n; ++l) { - sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) - + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) - + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) - + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); - sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) - + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) - + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) - + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); - } - tmp += d * sum; - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q4_K * x = (const block_q4_K *)vx + ib0; - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const int il = tid/step; // 0...3 - const int ir = tid - step*il; // 0...7 or 0...3 - const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - -#if K_QUANTS_PER_ITERATION == 2 - uint32_t q32[4]; - const uint8_t * q4 = (const uint8_t *)q32; -#else - uint16_t q16[4]; - const uint8_t * q4 = (const uint8_t *)q16; -#endif - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - -#if K_QUANTS_PER_ITERATION == 2 - const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset); - const uint32_t * q2 = q1 + 16; - - q32[0] = q1[0] & 0x0f0f0f0f; - q32[1] = q1[0] & 0xf0f0f0f0; - q32[2] = q2[0] & 0x0f0f0f0f; - q32[3] = q2[0] & 0xf0f0f0f0; - - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 4; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4]; - s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#else - const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset); - const uint16_t * q2 = q1 + 32; - - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[0] & 0xf0f0; - q16[2] = q2[0] & 0x0f0f; - q16[3] = q2[0] & 0xf0f0; - - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 2; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; - s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#endif - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) { - - const int row = blockIdx.x; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q5_K * x = (const block_q5_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int tid = threadIdx.x/2; // 0...15 - const int ix = threadIdx.x%2; - - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 2; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - uint16_t q16[8]; - const uint8_t * q4 = (const uint8_t *)q16; - - for (int i = ix; i < num_blocks_per_row; i += 2) { - - const uint8_t * ql1 = x[i].qs + q_offset; - const uint8_t * qh = x[i].qh + l0; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 sum = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - const uint16_t * q1 = (const uint16_t *)ql1; - const uint16_t * q2 = q1 + 32; - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[8] & 0x0f0f; - q16[2] = (q1[0] >> 4) & 0x0f0f; - q16[3] = (q1[8] >> 4) & 0x0f0f; - q16[4] = q2[0] & 0x0f0f; - q16[5] = q2[8] & 0x0f0f; - q16[6] = (q2[0] >> 4) & 0x0f0f; - q16[7] = (q2[8] >> 4) & 0x0f0f; - for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) - + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) - + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) - + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) - + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; - } - tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q6_K * x = (const block_q6_K *)vx + ib0; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 - const int is = 0; -#else - const int l0 = 4 * in; // 0, 4, 8, ..., 28 - const int is = in / 4; -#endif - const int ql_offset = 64*im + l0; - const int qh_offset = 32*im + l0; - const int s_offset = 8*im + is; - const int y_offset = 128*im + l0; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * ql = x[i].ql + ql_offset; - const uint8_t * qh = x[i].qh + qh_offset; - const int8_t * s = x[i].scales + s_offset; - - const float d = x[i].d; - -#if K_QUANTS_PER_ITERATION == 1 - float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) - + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) - + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) - + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) - + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) - + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) - + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) - +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); - tmp += sum; -#else - float sum = 0; - for (int l = 0; l < 4; ++l) { - sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) - + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) - + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) - + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); - } - tmp += sum; -#endif - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { - dst[row] = tmp; - } -} - -static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ - const half * x = (const half *) vx; - - // automatic half -> float type cast if dfloat == float - v.x = x[ib + iqs + 0]; - v.y = x[ib + iqs + 1]; -} - -static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) { - return type == GGML_TYPE_Q4_0 ? dequantize_q4_0 : - type == GGML_TYPE_Q4_1 ? dequantize_q4_1 : - type == GGML_TYPE_Q5_0 ? dequantize_q5_0 : - type == GGML_TYPE_Q5_1 ? dequantize_q5_1 : - type == GGML_TYPE_Q8_0 ? dequantize_q8_0 : - type == GGML_TYPE_F16 ? convert_f16 : - nullptr; -} - -template -static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { - constexpr int qk = ggml_cuda_type_traits::qk; // quantized weights per x block - constexpr int qr = ggml_cuda_type_traits::qr; // number of quantized weights per data value in x block - constexpr dequantize_kernel_t dequantize_kernel = get_dequantize_kernel(type); - - const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y; - - if (row >= nrows) { - return; - } - - const int tid = threadIdx.x; - - const int iter_stride = 2*GGML_CUDA_DMMV_X; - const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter - const int y_offset = qr == 1 ? 1 : qk/2; - -// partial sum for each thread -#ifdef GGML_CUDA_F16 - half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics -#else - float tmp = 0.0f; -#endif // GGML_CUDA_F16 - - for (int i = 0; i < ncols; i += iter_stride) { - const int col = i + vals_per_iter*tid; - const int64_t ib = ((int64_t)row*ncols + col)/qk; // x block index - const int iqs = (col%qk)/qr; // x quant index - const int iybs = col - col%qk; // y block start index - -// processing >2 values per i iter is faster for fast GPUs -#pragma unroll - for (int j = 0; j < vals_per_iter; j += 2) { - // process 2 vals per j iter - - // dequantize - // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val - dfloat2 v; - dequantize_kernel(vx, ib, iqs + j/qr, v); - - // matrix multiplication - // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 -#ifdef GGML_CUDA_F16 - tmp += __hmul2(v, { - y[iybs + iqs + j/qr + 0], - y[iybs + iqs + j/qr + y_offset] - }); -#else - tmp += v.x * y[iybs + iqs + j/qr + 0]; - tmp += v.y * y[iybs + iqs + j/qr + y_offset]; -#endif // GGML_CUDA_F16 - } - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { -#ifdef GGML_CUDA_F16 - dst[row] = tmp.x + tmp.y; -#else - dst[row] = tmp; -#endif // GGML_CUDA_F16 - } -} - -static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const dim3 block_dims(32, 1, 1); - dequantize_mul_mat_vec_q5_k<<>>(vx, y, dst, ncols); -} - -static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); -} - -static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -void ggml_cuda_op_dequantize_mul_mat_vec( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { - GGML_UNUSED(ctx); - const int64_t ne00 = src0->ne[0]; - const int64_t row_diff = row_high - row_low; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics -#ifdef GGML_CUDA_F16 - ggml_cuda_pool_alloc src1_dfloat_a(ctx.pool()); - half * src1_dfloat = nullptr; // dfloat == half - - bool src1_convert_f16 = - src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || - src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || - src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; - - if (src1_convert_f16) { - src1_dfloat = src1_dfloat_a.alloc(ne00); - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); - } -#else - const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion -#endif // GGML_CUDA_F16 - - switch (src0->type) { - case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q2_K: - dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q3_K: - dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_K: - dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_K: - dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q6_K: - dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - default: - GGML_ABORT("fatal error"); - break; - } - - GGML_UNUSED(src1); - GGML_UNUSED(dst); - GGML_UNUSED(src1_ddq_i); - GGML_UNUSED(src1_ncols); - GGML_UNUSED(src1_padded_row_size); -} - -bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) { - return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 || - src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 || - src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K || - src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K || - src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K || - src0_type == GGML_TYPE_F16; -} diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 1fb5c09c3b..ee9752da6a 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -517,9 +517,9 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) { } template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_combine_results( const float * __restrict__ VKQ_parts, const float2 * __restrict__ VKQ_meta, diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu index 5af02c7ecb..4d314dacb1 100644 --- a/ggml/src/ggml-cuda/fattn-tile-f16.cu +++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu @@ -5,9 +5,9 @@ #define FATTN_KQ_STRIDE_TILE_F16 64 template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_tile_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu index f402195ce0..bb33604470 100644 --- a/ggml/src/ggml-cuda/fattn-tile-f32.cu +++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu @@ -5,9 +5,9 @@ #define FATTN_KQ_STRIDE_TILE_F32 32 template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_tile_ext_f32( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh index 2ed6509acb..5ec3b91ae2 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh @@ -2,9 +2,9 @@ #include "fattn-common.cuh" template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_vec_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh index bf51259025..3d93f4a8ac 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh @@ -2,9 +2,9 @@ #include "fattn-common.cuh" template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_vec_ext_f32( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh index b10d19d932..860d0e6dc2 100644 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh @@ -7,9 +7,9 @@ // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 83e5589a1c..0e7ebbc539 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -13,9 +13,9 @@ static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, g const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - const int32_t precision = KQV->op_params[3]; + const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); - if (precision != GGML_PREC_DEFAULT) { + if (prec != GGML_PREC_DEFAULT) { if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; switch (Q->ne[0]) { @@ -301,11 +301,11 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst ggml_cuda_set_device(ctx.device); const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const int32_t precision = KQV->op_params[3]; + const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); // On AMD the tile kernels perform poorly, use the vec kernel instead: if (cc >= CC_OFFSET_AMD) { - if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { + if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); @@ -332,7 +332,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - if (precision == GGML_PREC_DEFAULT) { + if (prec == GGML_PREC_DEFAULT) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } else if(Q->ne[0] <= 128) { diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu similarity index 90% rename from ggml/src/ggml-cuda.cu rename to ggml/src/ggml-cuda/ggml-cuda.cu index 1338bd4583..dd94ab03d5 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -16,11 +16,11 @@ #include "ggml-cuda/cpy.cuh" #include "ggml-cuda/cross-entropy-loss.cuh" #include "ggml-cuda/diagmask.cuh" -#include "ggml-cuda/dmmv.cuh" #include "ggml-cuda/fattn.cuh" #include "ggml-cuda/getrows.cuh" #include "ggml-cuda/im2col.cuh" #include "ggml-cuda/mmq.cuh" +#include "ggml-cuda/mmv.cuh" #include "ggml-cuda/mmvq.cuh" #include "ggml-cuda/norm.cuh" #include "ggml-cuda/opt-step-adamw.cuh" @@ -36,7 +36,7 @@ #include "ggml-cuda/tsembd.cuh" #include "ggml-cuda/unary.cuh" #include "ggml-cuda/upscale.cuh" -#include "ggml-cuda/rwkv-wkv.cuh" +#include "ggml-cuda/wkv6.cuh" #include #include @@ -91,7 +91,7 @@ int ggml_cuda_get_device() { static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { ggml_cuda_set_device(device); -#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) +#if defined(GGML_USE_HIP) && defined(GGML_HIP_UMA) auto res = hipMallocManaged(ptr, size); if (res == hipSuccess) { // if error we "need" to know why... @@ -100,7 +100,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) return res; #else -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIP) cudaError_t err; if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) { @@ -113,7 +113,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) return err; #else return cudaMalloc(ptr, size); -#endif // !defined(GGML_USE_HIPBLAS) +#endif // !defined(GGML_USE_HIP) #endif } @@ -151,7 +151,7 @@ static ggml_cuda_device_info ggml_cuda_init() { for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) CUdevice device; CU_CHECK(cuDeviceGet(&device, id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); @@ -163,7 +163,7 @@ static ggml_cuda_device_info ggml_cuda_init() { alloc_prop.location.id = id; CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) info.devices[id].vmm = !!device_vmm; cudaDeviceProp prop; @@ -175,13 +175,13 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].nsm = prop.multiProcessorCount; info.devices[id].smpb = prop.sharedMemPerBlock; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) info.devices[id].smpbo = prop.sharedMemPerBlock; info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } for (int id = 0; id < info.device_count; ++id) { @@ -299,7 +299,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { }; // pool with virtual memory -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB @@ -393,14 +393,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { GGML_ASSERT(ptr == (void *) (pool_addr + pool_used)); } }; -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(int device) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) if (ggml_cuda_info().devices[device].vmm) { return std::unique_ptr(new ggml_cuda_pool_vmm(device)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) return std::unique_ptr(new ggml_cuda_pool_leg(device)); } @@ -421,20 +421,15 @@ struct ggml_backend_cuda_buffer_context { } }; -static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - -static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; -} - static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; delete ctx; } +static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { + return buffer->iface.free_buffer == ggml_backend_cuda_buffer_free_buffer; +} + static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; @@ -515,7 +510,6 @@ static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, @@ -548,8 +542,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac ggml_cuda_set_device(buft_ctx->device); - size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 - void * dev_ptr; cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); if (err != cudaSuccess) { @@ -657,7 +649,9 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl } struct ggml_backend_cuda_split_buffer_type_context { + int main_device; std::array tensor_split; + std::string name; }; struct ggml_backend_cuda_split_buffer_context { @@ -680,16 +674,6 @@ struct ggml_backend_cuda_split_buffer_context { std::vector tensor_extras; }; -static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Split"; - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; - GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds -} static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; @@ -833,7 +817,6 @@ static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, u } static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, @@ -848,9 +831,9 @@ static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { // cuda split buffer type static const char * ggml_backend_cuda_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return GGML_CUDA_NAME "_Split"; + ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; - GGML_UNUSED(buft); + return ctx->name.c_str(); } static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) { @@ -915,11 +898,11 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_inte /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, }; -ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { +ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) { static std::mutex mutex; std::lock_guard lock(mutex); - static std::map, struct ggml_backend_buffer_type> buft_map; + static std::map>, struct ggml_backend_buffer_type> buft_map; std::array tensor_split_arr = {}; @@ -937,18 +920,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten } } - auto it = buft_map.find(tensor_split_arr); + auto it = buft_map.find({main_device, tensor_split_arr}); if (it != buft_map.end()) { return &it->second; } + auto * ctx = new ggml_backend_cuda_split_buffer_type_context{ + main_device, + tensor_split_arr, + GGML_CUDA_NAME + std::to_string(main_device) + "_Split", + }; struct ggml_backend_buffer_type buft { /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), 0), - /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), main_device), + /* .context = */ ctx, }; - auto result = buft_map.emplace(tensor_split_arr, buft); + auto result = buft_map.emplace(std::make_pair(main_device, tensor_split_arr), buft); return &result.first->second; } @@ -960,12 +948,6 @@ static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_ GGML_UNUSED(buft); } -static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Host"; - - GGML_UNUSED(buffer); -} - static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { CUDA_CHECK(cudaFreeHost(buffer->context)); } @@ -998,7 +980,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; return buffer; @@ -1039,120 +1020,12 @@ typedef void (*ggml_cuda_op_mul_mat_t)( #define MUL_MAT_SRC1_COL_STRIDE 128 -static __global__ void mul_mat_p021_f16_f32( - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / (nchannels_y / nchannels_x); - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - // x is transposed and permuted - const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; - const float xi = __half2float(x[ix]); - - const int row_y = col_x; - - // y is not transposed but permuted - const int iy = channel*nrows_y + row_y; - - tmp += xi * y[iy]; - } - - // dst is not transposed and not permuted - const int idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / channel_x_divisor; - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - const int idst = channel*nrows_dst + row_dst; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - const int row_y = col_x; - - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const int iy = channel*nrows_y + row_y; - - const float xi = __half2float(x[ix]); - - tmp += xi * y[iy]; - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static void ggml_mul_mat_p021_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, - const int nchannels_x, const int nchannels_y, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_p021_f16_f32<<>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y); -} - -static void ggml_mul_mat_vec_nc_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, - const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_vec_nc_f16_f32<<>> - (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); -} - static cudaError_t ggml_cuda_cpy_tensor_2d( void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer)); - char * src_ptr = (char *) src->data; - char * dst_ptr = (char *) dst; + const char * src_ptr = (const char *) src->data; + char * dst_ptr = (char *) dst; const int64_t ne0 = src->ne[0]; const int64_t nb0 = src->nb[0]; @@ -1162,7 +1035,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( const enum ggml_type type = src->type; const int64_t ts = ggml_type_size(type); const int64_t bs = ggml_blck_size(type); - int64_t i1_diff = i1_high - i1_low; + const int64_t i1_diff = i1_high - i1_low; const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; if (nb0 == ts && nb1 == ts*ne0/bs) { @@ -1316,11 +1189,17 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); if (err != cudaErrorPeerAccessAlreadyEnabled) { CUDA_CHECK(err); + } else { + // reset the error + cudaGetLastError(); } } else { cudaError_t err = cudaDeviceDisablePeerAccess(id_other); if (err != cudaErrorPeerAccessNotEnabled) { CUDA_CHECK(err); + } else { + // reset the error + cudaGetLastError(); } } } @@ -1338,7 +1217,7 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { static cudaError_t ggml_cuda_Memcpy2DPeerAsync( void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices cudaMemcpy3DPeerParms p = {}; p.dstDevice = dstDevice; @@ -1352,7 +1231,7 @@ static cudaError_t ggml_cuda_Memcpy2DPeerAsync( GGML_UNUSED(dstDevice); GGML_UNUSED(srcDevice); return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream); -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) } static void ggml_cuda_op_mul_mat( @@ -1400,7 +1279,7 @@ static void ggml_cuda_op_mul_mat( const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); + const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); @@ -1479,14 +1358,24 @@ static void ggml_cuda_op_mul_mat( if (src0_is_contiguous) { dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data; } else { - dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0)); + // If src0 is not contiguous it will be copied to a temporary buffer. + // This buffer needs to be cleared entirely because multiple regions will function as padding. + const size_t nbytes_data = ggml_nbytes(src0); + const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); + dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding); + // TODO: remove this for MUSA once the Guilty Lockup issue is resolved +#ifndef GGML_USE_MUSA + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream)); +#else // GGML_USE_MUSA + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); +#endif // !GGML_USE_MUSA } - // If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared: + // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) { - const int64_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); - const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream)); + const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); + const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); } if (src1_on_device && src1_is_contiguous) { @@ -1657,58 +1546,6 @@ static void ggml_cuda_op_mul_mat( } } -static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); -} - -static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - const int64_t row_stride_x = nb01 / sizeof(half); - const int64_t channel_stride_x = nb02 / sizeof(half); - - ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); -} - static __global__ void k_compute_batched_ptrs( const half * src0_as_f16, const half * src1_as_f16, char * dst, const void ** ptrs_src, void ** ptrs_dst, @@ -1880,23 +1717,19 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co } static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); + const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); - bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type) + bool use_mul_mat_vec = src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1; - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src0->ne[0] % 2 == 0 && src1->ne[1] == 1; + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - bool use_mul_mat_q = ggml_is_quantized(src0->type) + bool use_mul_mat_q = ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - // if mmvq is available it's a better choice than dmmv: -#ifndef GGML_CUDA_FORCE_DMMV - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; -#endif // GGML_CUDA_FORCE_DMMV - - bool any_gpus_with_slow_fp16 = false; + bool any_gpus_with_slow_fp16 = false; + bool any_gpus_without_fp16_mma = false; if (split) { ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; @@ -1907,14 +1740,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor continue; } - const int cc = ggml_cuda_info().devices[id].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + const int cc = ggml_cuda_info().devices[id].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_available(cc); } } else { - const int cc = ggml_cuda_info().devices[ctx.device].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + const int cc = ggml_cuda_info().devices[ctx.device].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_available(cc); } // debug helpers @@ -1925,18 +1760,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // FP32 precision KQ single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst); - } else if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // FP32 precision KQV single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst); + if (!split && use_mul_mat_vec && dst->ne[3] == 1 && (src0->ne[1] < MMV_MAX_ROWS || any_gpus_without_fp16_mma)) { + // the custom F16 vector kernel can be used over batched cuBLAS GEMM + // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention) + ggml_cuda_mul_mat_vec(ctx, src0, src1, dst); } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { - // KQ + KQV multi-batch without FlashAttention + // general KQ + KQV multi-batch without FlashAttention ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); - } else if (use_dequantize_mul_mat_vec) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr); + } else if (use_mul_mat_vec) { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec, nullptr); } else if (use_mul_mat_vec_q) { ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda); } else if (use_mul_mat_q) { @@ -2007,7 +1840,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * GGML_TENSOR_BINARY_OP_LOCALS - GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers"); + GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers"); cudaStream_t stream = ctx.stream(); @@ -2140,7 +1973,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) { // why is this here instead of mul_mat? - if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) { + if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) { ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device); } @@ -2322,8 +2155,8 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_CROSS_ENTROPY_LOSS: ggml_cuda_cross_entropy_loss(ctx, dst); break; - case GGML_OP_RWKV_WKV: - ggml_cuda_op_rwkv_wkv(ctx, dst); + case GGML_OP_RWKV_WKV6: + ggml_cuda_op_rwkv_wkv6(ctx, dst); break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: ggml_cuda_cross_entropy_loss_back(ctx, dst); @@ -2361,12 +2194,6 @@ static void ggml_backend_cuda_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - return ggml_backend_cuda_buffer_type(cuda_ctx->device); -} - static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; @@ -2572,7 +2399,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, continue; } - if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { + if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) { use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__); @@ -2659,7 +2486,8 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { assert(node->src[j]->buffer); - assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); + assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || + ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft)); } } #endif @@ -2752,7 +2580,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); if (stat == cudaErrorGraphExecUpdateFailure) { #ifndef NDEBUG - GGML_LOG_ERROR("%s: CUDA graph update failed\n", __func__); + GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__); #endif // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate @@ -2801,7 +2629,6 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev static const ggml_backend_i ggml_backend_cuda_interface = { /* .get_name = */ ggml_backend_cuda_get_name, /* .free = */ ggml_backend_cuda_free, - /* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async, /* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async, @@ -2811,9 +2638,6 @@ static const ggml_backend_i ggml_backend_cuda_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, - /* .supports_op = */ NULL, // moved to device - /* .supports_buft = */ NULL, // moved to device - /* .offload_op = */ NULL, // moved to device /* .event_record = */ ggml_backend_cuda_event_record, /* .event_wait = */ ggml_backend_cuda_event_wait, }; @@ -2903,7 +2727,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) { GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; } static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { @@ -2927,7 +2751,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back }; } -static ggml_backend_t ggml_backend_cuda_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_cuda_device_init_backend(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; return ggml_backend_cuda_init(ctx->device); @@ -2943,18 +2767,29 @@ static ggml_backend_buffer_type_t ggml_backend_cuda_device_get_host_buffer_type( return ggml_backend_cuda_host_buffer_type(); } -static ggml_backend_buffer_t ggml_backend_cuda_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - GGML_UNUSED(dev); - GGML_UNUSED(ptr); - GGML_UNUSED(size); - GGML_UNUSED(max_tensor_size); - return nullptr; -} - // TODO: move these functions here static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context; + // split buffers can only be used with GGML_OP_MUL_MAT + if (op->op != GGML_OP_MUL_MAT) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda_split(op->src[i]->buffer->buft)) { + return false; + } + } + } + + // check if all the sources are allocated on this device + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda(op->src[i]->buffer->buft)) { + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)op->src[i]->buffer->buft->context; + if (buft_ctx->device != dev_ctx->device) { + return false; + } + } + } + switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -2979,6 +2814,17 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g { struct ggml_tensor * a = op->src[0]; struct ggml_tensor * b = op->src[1]; + // for small weight matrices the active device can end up without any rows, don't use row split in those cases + // this avoids some edge cases (and the performance would not be good anyways) + if (a->buffer && ggml_backend_buft_is_cuda_split(a->buffer->buft)) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) a->buffer->buft->context; + int64_t row_low; + int64_t row_high; + get_row_split(&row_low, &row_high, a, buft_ctx->tensor_split, dev_ctx->device); + if (row_low == row_high) { + return false; + } + } if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) { return false; } @@ -3114,18 +2960,20 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } return false; } break; + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0; + break; case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: case GGML_OP_ADD: case GGML_OP_ADD1: case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: - case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SQRT: @@ -3141,7 +2989,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ROPE: return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: - return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_2D: case GGML_OP_SUM: case GGML_OP_SUM_ROWS: @@ -3153,12 +3000,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: - case GGML_OP_RWKV_WKV: + case GGML_OP_RWKV_WKV6: return true; case GGML_OP_FLASH_ATTN_EXT: { #ifndef FLASH_ATTN_AVAILABLE return false; #endif + if (op->src[1]->type == GGML_TYPE_BF16 || op->src[2]->type == GGML_TYPE_BF16) { + return false; + } if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { return true; } @@ -3181,24 +3031,27 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } static bool ggml_backend_cuda_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (ggml_backend_buft_is_cuda_split(buft)) { - return true; - } + return (ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev; +} - if (ggml_backend_buft_is_cuda(buft)) { - ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context; - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - return buft_ctx->device == dev_ctx->device; +static int64_t get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return 0; + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + return op->ne[2]; + default: + return ggml_nrows(op); } - - return false; } static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; - return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || - (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + return get_op_batch_size(op) >= min_batch_size; GGML_UNUSED(dev); } @@ -3239,10 +3092,10 @@ static const ggml_backend_device_i ggml_backend_cuda_device_interface = { /* .get_memory = */ ggml_backend_cuda_device_get_memory, /* .get_type = */ ggml_backend_cuda_device_get_type, /* .get_props = */ ggml_backend_cuda_device_get_props, - /* .init_backend = */ ggml_backend_cuda_device_init, + /* .init_backend = */ ggml_backend_cuda_device_init_backend, /* .get_buffer_type = */ ggml_backend_cuda_device_get_buffer_type, /* .get_host_buffer_type = */ ggml_backend_cuda_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ ggml_backend_cuda_device_buffer_from_host_ptr, + /* .buffer_from_host_ptr = */ NULL, /* .supports_op = */ ggml_backend_cuda_device_supports_op, /* .supports_buft = */ ggml_backend_cuda_device_supports_buft, /* .offload_op = */ ggml_backend_cuda_device_offload_op, diff --git a/ggml/src/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu index 16463ab0fb..86a54e42bb 100644 --- a/ggml/src/ggml-cuda/im2col.cu +++ b/ggml/src/ggml-cuda/im2col.cu @@ -91,9 +91,9 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t OH = is_2D ? dst->ne[2] : 1; const int64_t OW = dst->ne[1]; - const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const int64_t batch = src1->ne[3]; - const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 + const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[is_2D ? 3 : 2]; + const size_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 if(dst->type == GGML_TYPE_F16) { im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream); diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 4935f88186..ae5c68ab35 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -8,8 +8,6 @@ void ggml_cuda_op_mul_mat_q( const int64_t ne00 = src0->ne[0]; - const int64_t nb01 = src0->nb[1]; - const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; GGML_ASSERT(ne10 % QK8_1 == 0); @@ -17,7 +15,7 @@ void ggml_cuda_op_mul_mat_q( const int64_t ne0 = dst->ne[0]; const int64_t row_diff = row_high - row_low; - const int64_t stride00 = nb01 / ggml_type_size(src0->type); + const int64_t stride00 = ne00 / ggml_blck_size(src0->type); int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 021a25682c..425acb20da 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -100,9 +100,9 @@ static constexpr __device__ int get_mmq_x_max_device() { return 128; #else // INT8_MMA_AVAILABLE -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) return 128; -#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if __CUDA_ARCH__ >= CC_VOLTA #ifdef GGML_CUDA_FORCE_MMQ @@ -115,7 +115,7 @@ static constexpr __device__ int get_mmq_x_max_device() { return 64; #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #endif // INT8_MMA_AVAILABLE } @@ -124,7 +124,7 @@ static constexpr int get_mmq_y_host(const int cc) { } static constexpr __device__ int get_mmq_y_device() { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA1) return 64; #else @@ -136,7 +136,7 @@ static constexpr __device__ int get_mmq_y_device() { #else return 64; #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } #define MMQ_DP4A_TXS_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0} @@ -2569,7 +2569,7 @@ static __device__ void mul_mat_q_process_tile( // The mul_mat_q kernel implements "stream-k" work partitioning as described in https://arxiv.org/abs/2301.03598 template -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) __launch_bounds__(WARP_SIZE*nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) @@ -2579,7 +2579,7 @@ template #else __launch_bounds__(WARP_SIZE*nwarps, 2) #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) static __global__ void mul_mat_q( const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup, const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) { @@ -2594,7 +2594,7 @@ static __global__ void mul_mat_q( constexpr int mmq_y = get_mmq_y_device(); // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead: -#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA +#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA { constexpr bool fixup = false; mul_mat_q_process_tile @@ -2602,7 +2602,7 @@ static __global__ void mul_mat_q( blockIdx.x, blockIdx.y, 0, ne00/qk); return; } -#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA +#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA const int64_t blocks_per_ne00 = ne00 / qk; constexpr int blocks_per_iter = MMQ_ITER_K / qk; @@ -2765,14 +2765,14 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a const int shmem = mmq_get_shmem(mmq_x, mmq_y, cc); -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; if (!shmem_limit_raised[id]) { CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); shmem_limit_raised[id] = true; } -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) const int nty = (args.ne01 + mmq_y - 1) / mmq_y; const int ntx = (args.ne11 + mmq_x - 1) / mmq_x; diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu new file mode 100644 index 0000000000..cfe91f4283 --- /dev/null +++ b/ggml/src/ggml-cuda/mmv.cu @@ -0,0 +1,223 @@ +#include "common.cuh" +#include "mmv.cuh" + +template +static __global__ void mul_mat_vec( + const half * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row, + const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) { + const int64_t row = blockIdx.x; + const int64_t channel = blockIdx.z; + const int tid = threadIdx.x; + + x += (channel/channel_ratio)*stride_channel_x + row*stride_row; + y += channel *stride_channel_y; + dst += channel *stride_channel_dst; + + const half2 * x2 = (const half2 *) x; + const float2 * y2 = (const float2 *) y; + + extern __shared__ char data_mmv[]; + float * buf_iw = (float *) data_mmv; + + if (block_size > WARP_SIZE) { + if (tid < WARP_SIZE) { + buf_iw[tid] = 0.0f; + } + __syncthreads(); + } + + float sumf; + + if (std::is_same::value) { + sumf = 0.0f; + + for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + const float2 tmpx = __half22float2(x2[col2]); + const float2 tmpy = y2[col2]; + sumf += tmpx.x * tmpy.x; + sumf += tmpx.y * tmpy.y; + } + } else { +#ifdef FP16_AVAILABLE + half2 sumh2 = make_half2(0.0f, 0.0f); + + for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + const float2 tmp = y2[col2]; + sumh2 += x2[col2] * make_half2(tmp.x, tmp.y); + } + + sumf = __low2float(sumh2) + __high2float(sumh2); +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE + } + + sumf = warp_reduce_sum(sumf); + + if (block_size > WARP_SIZE) { + buf_iw[tid/WARP_SIZE] = sumf; + __syncthreads(); + if (tid > WARP_SIZE) { + return; + } + sumf = buf_iw[tid]; + sumf = warp_reduce_sum(sumf); + } + + if (tid != 0) { + return; + } + + dst[row] = sumf; +} + +template +static void launch_mul_mat_vec_cuda( + const half * x, const float * y, float * dst, + const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, + const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, + cudaStream_t stream) { + GGML_ASSERT(ncols % 2 == 0); + GGML_ASSERT(stride_row % 2 == 0); + GGML_ASSERT(nchannels_y % nchannels_x == 0); + const int64_t channel_ratio = nchannels_y / nchannels_x; + + int64_t block_size_best = WARP_SIZE; + int64_t niter_best = (ncols + 2*WARP_SIZE - 1) / (2*WARP_SIZE); + for (int64_t block_size = 2*WARP_SIZE; block_size <= 256; block_size += WARP_SIZE) { + const int64_t niter = (ncols + 2*block_size - 1) / (2*block_size); + if (niter < niter_best) { + niter_best = niter; + block_size_best = block_size; + } + } + + const int smem = WARP_SIZE*sizeof(float); + const dim3 block_nums(nrows, 1, nchannels_y); + const dim3 block_dims(block_size_best, 1, 1); + switch (block_size_best) { + case 32: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 64: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 96: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 128: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 160: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 192: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 224: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 256: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + default: { + GGML_ABORT("fatal error"); + } break; + } +} + +static void mul_mat_vec_cuda( + const half * x, const float * y, float * dst, + const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, + const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, + enum ggml_prec prec, cudaStream_t stream) { + switch (prec) { + case GGML_PREC_DEFAULT: { + launch_mul_mat_vec_cuda(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, + stride_channel_x, stride_channel_y, stride_channel_dst, stream); + } break; + case GGML_PREC_F32: { + launch_mul_mat_vec_cuda(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, + stride_channel_x, stride_channel_y, stride_channel_dst, stream); + } break; + } +} + +void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + GGML_ASSERT(src1->ne[1] == 1); + + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32; + + const half * src0_d = (const half *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + + const int64_t ne02 = src0->ne[2]; + const int64_t ne12 = src1->ne[2]; + GGML_ASSERT(dst->ne[2] == ne12); + + GGML_ASSERT(src0->ne[3] == 1); + GGML_ASSERT(src1->ne[3] == 1); + GGML_ASSERT( dst->ne[3] == 1); + + const int64_t stride_row = src0->nb[1] / ggml_type_size(src0->type); + const int64_t channel_stride_x = src0->nb[2] / ggml_type_size(src0->type); + const int64_t channel_stride_y = src1->nb[2] / ggml_type_size(src1->type); + const int64_t channel_stride_dst = dst->nb[2] / ggml_type_size( dst->type); + + mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12, channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream()); +} + +void ggml_cuda_op_mul_mat_vec( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t row_diff = row_high - row_low; + + GGML_ASSERT(src1_ncols == 1); + + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32; + + + // ggml_cuda_op provides single, contiguous matrices + const int64_t stride_row = ne00; + const int64_t nchannels_x = 1; + const int64_t nchannels_y = 1; + const int64_t channel_stride_x = 0; + const int64_t channel_stride_y = 0; + const int64_t channel_stride_dst = 0; + + mul_mat_vec_cuda((const half *) src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row, + nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream); + + GGML_UNUSED(ctx); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddq_i); + GGML_UNUSED(src1_ncols); + GGML_UNUSED(src1_padded_row_size); +} diff --git a/ggml/src/ggml-cuda/dmmv.cuh b/ggml/src/ggml-cuda/mmv.cuh similarity index 55% rename from ggml/src/ggml-cuda/dmmv.cuh rename to ggml/src/ggml-cuda/mmv.cuh index e727eb97f6..78a1cd4a69 100644 --- a/ggml/src/ggml-cuda/dmmv.cuh +++ b/ggml/src/ggml-cuda/mmv.cuh @@ -1,20 +1,12 @@ #include "common.cuh" -// dmmv = dequantize_mul_mat_vec +// maximum number of src0 rows with which to use mul_mat_vec over cuBLAS if FP16 tensor cores are available +#define MMV_MAX_ROWS 512 -// TODO: remove this? -#ifndef GGML_CUDA_DMMV_X -#define GGML_CUDA_DMMV_X 32 -#endif +void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); -#ifndef GGML_CUDA_MMV_Y -#define GGML_CUDA_MMV_Y 1 -#endif - -void ggml_cuda_op_dequantize_mul_mat_vec( +void ggml_cuda_op_mul_mat_vec( ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream); - -bool ggml_cuda_dmmv_type_supported(ggml_type src0_type); diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 7dbbc99390..735975c160 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -48,10 +48,10 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) { } template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) // tell the compiler to use as many registers as it wants, see nwarps definition below __launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { @@ -62,13 +62,13 @@ static __global__ void mul_mat_vec_q( constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) constexpr int nwarps = 1; constexpr int rows_per_cuda_block = 1; #else constexpr int nwarps = ncols_y <= 4 ? 4 : 2; constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; const int row0 = rows_per_cuda_block*blockIdx.x; diff --git a/ggml/src/ggml-cuda/opt-step-adamw.cu b/ggml/src/ggml-cuda/opt-step-adamw.cu index d6f13a9c62..35154f2996 100644 --- a/ggml/src/ggml-cuda/opt-step-adamw.cu +++ b/ggml/src/ggml-cuda/opt-step-adamw.cu @@ -1,11 +1,11 @@ +#include "ggml-impl.h" #include "opt-step-adamw.cuh" #include static __global__ void opt_step_adamw_f32( - float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, const int64_t k, - const float alpha, const float beta1, const float beta2, const float eps, const float wd, - const float beta1h, const float beta2h) { + float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, + const float * __restrict__ pars, const int64_t k) { const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x; @@ -13,6 +13,14 @@ static __global__ void opt_step_adamw_f32( return; } + const float alpha = pars[0]; + const float beta1 = pars[1]; + const float beta2 = pars[2]; + const float eps = pars[3]; + const float wd = pars[4]; + const float beta1h = pars[5]; + const float beta2h = pars[6]; + const float gi = g[i]; const float gmi = g_m[i]*beta1 + gi*(1.0f - beta1); const float gvi = g_v[i]*beta2 + gi*gi*(1.0f - beta2); @@ -23,58 +31,48 @@ static __global__ void opt_step_adamw_f32( const float mh = gmi*beta1h; const float vh = sqrtf(gvi*beta2h) + eps; - x[i] = x[i]*(1.0f - alpha*wd) - mh/vh; + x[i] = x[i]*(1.0f - alpha*wd) - alpha*mh/vh; } static void opt_step_adamw_f32_cuda( - float * x, const float * g, float * g_m, float * g_v, const int64_t k, - const float alpha, const float beta1, const float beta2, const float eps, const float wd, - const float beta1h, const float beta2h, cudaStream_t stream) { + float * x, const float * g, float * g_m, float * g_v, const float * pars, const int64_t k, cudaStream_t stream) { const dim3 block_dims(CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1); const dim3 block_nums((k + CUDA_OPT_STEP_ADAMW_BLOCK_SIZE - 1) / CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1); - opt_step_adamw_f32<<>>(x, g, g_m, g_v, k, alpha, beta1, beta2, eps, wd, beta1h, beta2h); + opt_step_adamw_f32<<>>(x, g, g_m, g_v, pars, k); } void ggml_cuda_opt_step_adamw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const ggml_tensor * src0_grad = dst->src[1]; - const ggml_tensor * src0_grad_m = dst->src[2]; - const ggml_tensor * src0_grad_v = dst->src[3]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src0_grad = dst->src[1]; + const ggml_tensor * src0_grad_m = dst->src[2]; + const ggml_tensor * src0_grad_v = dst->src[3]; + const ggml_tensor * adamw_params = dst->src[4]; - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32); + GGML_ASSERT(adamw_params->type == GGML_TYPE_F32); GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src0_grad)); GGML_ASSERT(ggml_is_contiguous(src0_grad_m)); GGML_ASSERT(ggml_is_contiguous(src0_grad_v)); + GGML_ASSERT(ggml_is_contiguous(adamw_params)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); - float * src0_d = (float *) src0->data; - const float * src0_grad_d = (const float *) src0_grad->data; - float * src0_grad_m_d = (float *) src0_grad_m->data; - float * src0_grad_v_d = (float *) src0_grad_v->data; + float * src0_d = (float *) src0->data; + const float * src0_grad_d = (const float *) src0_grad->data; + float * src0_grad_m_d = (float *) src0_grad_m->data; + float * src0_grad_v_d = (float *) src0_grad_v->data; + const float * adamw_params_d = (const float *) adamw_params->data; cudaStream_t stream = ctx.stream(); const int64_t ne = ggml_nelements(src0); - int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); - float alpha; memcpy(&alpha, &dst->op_params[2], sizeof(float)); - float beta1; memcpy(&beta1, &dst->op_params[3], sizeof(float)); - float beta2; memcpy(&beta2, &dst->op_params[4], sizeof(float)); - float eps; memcpy(&eps, &dst->op_params[5], sizeof(float)); - float wd; memcpy(&wd, &dst->op_params[6], sizeof(float)); - - const float beta1h = alpha/(1.0f - powf(beta1, iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); - - opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, ne, alpha, beta1, beta2, eps, wd, beta1h, beta2h, stream); - - iter++; - memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); + opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, adamw_params_d, ne, stream); } diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 45408ce868..1702e4ce2f 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -69,8 +69,8 @@ static __global__ void quantize_mmq_q8_1( // Exchange max. abs. value between vals_per_scale/4 threads. #pragma unroll - for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) { - amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE)); + for (int offset = vals_per_scale/8; offset > 0; offset >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE)); } float sum; @@ -79,8 +79,8 @@ static __global__ void quantize_mmq_q8_1( // Exchange calculate sum across vals_per_sum/4 threads. #pragma unroll - for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) { - sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE); + for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) { + sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE); } } diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cuh b/ggml/src/ggml-cuda/rwkv-wkv.cuh deleted file mode 100644 index 13795247fb..0000000000 --- a/ggml/src/ggml-cuda/rwkv-wkv.cuh +++ /dev/null @@ -1,5 +0,0 @@ -#include "common.cuh" - -#define CUDA_WKV_BLOCK_SIZE 64 - -void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/sum.cu b/ggml/src/ggml-cuda/sum.cu index 0583e4fe0c..31cfe53941 100644 --- a/ggml/src/ggml-cuda/sum.cu +++ b/ggml/src/ggml-cuda/sum.cu @@ -1,6 +1,6 @@ -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 #define USE_CUB -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 #ifdef USE_CUB // On Windows CUB uses libraries with variables called CC_PASCAL which conflict with the define in common.cuh. diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cu b/ggml/src/ggml-cuda/wkv6.cu similarity index 93% rename from ggml/src/ggml-cuda/rwkv-wkv.cu rename to ggml/src/ggml-cuda/wkv6.cu index 098e92d352..42578341a3 100644 --- a/ggml/src/ggml-cuda/rwkv-wkv.cu +++ b/ggml/src/ggml-cuda/wkv6.cu @@ -1,5 +1,5 @@ #include "common.cuh" -#include "rwkv-wkv.cuh" +#include "wkv6.cuh" static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const int H, const float * k, const float * v, const float * r, const float * tf, const float * td, const float * s, float * dst) { const int tid = threadIdx.x; @@ -64,7 +64,7 @@ static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const } } -void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { +void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const float * k_d = (const float *)dst->src[0]->data; const float * v_d = (const float *)dst->src[1]->data; const float * r_d = (const float *)dst->src[2]->data; @@ -83,7 +83,7 @@ void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32); GGML_ASSERT(C % H == 0); - GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); + GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); // The current cuda kernel is designed for RWKV6, HEAD_SIZE == 64 rwkv_wkv_f32<<>>(B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d); } diff --git a/ggml/src/ggml-cuda/wkv6.cuh b/ggml/src/ggml-cuda/wkv6.cuh new file mode 100644 index 0000000000..a7124ee517 --- /dev/null +++ b/ggml/src/ggml-cuda/wkv6.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +#define CUDA_WKV_BLOCK_SIZE 64 + +void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt new file mode 100644 index 0000000000..fccf8eb844 --- /dev/null +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -0,0 +1,106 @@ +if (NOT EXISTS $ENV{ROCM_PATH}) + if (NOT EXISTS /opt/rocm) + set(ROCM_PATH /usr) + else() + set(ROCM_PATH /opt/rocm) + endif() +else() + set(ROCM_PATH $ENV{ROCM_PATH}) +endif() + +list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) +list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") + +# CMake on Windows doesn't support the HIP language yet +if (WIN32) + set(CXX_IS_HIPCC TRUE) +else() + string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") +endif() + +if (CXX_IS_HIPCC) + if (LINUX) + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") + message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") + endif() + + message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." + " Prefer setting the HIP compiler directly. See README for details.") + endif() +else() + # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. + if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) + endif() + cmake_minimum_required(VERSION 3.21) + enable_language(HIP) +endif() + +find_package(hip REQUIRED) +find_package(hipblas REQUIRED) +find_package(rocblas REQUIRED) + +message(STATUS "HIP and hipBLAS found") + +file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh") +list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h") + +file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu") +file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) +file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) + +if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) +else() + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) +endif() + +add_library(ggml-hip + ${GGML_HEADERS_ROCM} + ${GGML_SOURCES_ROCM}) + +target_link_libraries(ggml-hip PRIVATE ggml-base) +target_include_directories(ggml-hip PRIVATE . ..) + +# TODO: do not use CUDA definitions for HIP +target_compile_definitions(ggml PUBLIC GGML_USE_CUDA) + +add_compile_definitions(GGML_USE_HIP) + +if (GGML_HIP_UMA) + add_compile_definitions(GGML_HIP_UMA) +endif() + +if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) +endif() + +if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) +endif() + +if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) +endif() + +if (CXX_IS_HIPCC) + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + target_link_libraries(ggml-hip PRIVATE hip::device) +else() + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) +endif() + +if (GGML_STATIC) + message(FATAL_ERROR "Static linking not supported for HIP/ROCm") +endif() + +target_link_libraries(ggml-hip PRIVATE ggml-base hip::host roc::rocblas roc::hipblas) diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index d3f4bad8c0..3965be7875 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -3,11 +3,28 @@ // GGML internal header #include "ggml.h" - #include +#include #include // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/ #include #include +#include + +#ifdef __ARM_FEATURE_SVE +#include +#endif // __ARM_FEATURE_SVE + +#if defined(__ARM_NEON) +// if YCM cannot find , make a symbolic link to it, for example: +// +// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ +// +#include +#endif + +#if defined(__F16C__) +#include +#endif #ifdef __cplusplus extern "C" { @@ -19,20 +36,37 @@ extern "C" { #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +// required for mmap as gguf only guarantees 32-byte alignment +#define TENSOR_ALIGNMENT 32 + // static_assert should be a #define, but if it's not, // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 #ifndef __cplusplus -#ifndef static_assert -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) -#define static_assert(cond, msg) _Static_assert(cond, msg) -#else -#define static_assert(cond, msg) struct global_scope_noop_trick -#endif -#endif + #ifndef static_assert + #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) + #define static_assert(cond, msg) _Static_assert(cond, msg) + #else + #define static_assert(cond, msg) struct global_scope_noop_trick + #endif + #endif #endif +static inline int ggml_up32(int n) { + return (n + 31) & ~31; +} + +//static inline int ggml_up64(int n) { +// return (n + 63) & ~63; +//} + +static inline int ggml_up(int n, int m) { + // assert m is a power of 2 + GGML_ASSERT((m & (m - 1)) == 0); + return (n + m - 1) & ~(m - 1); +} + // // logging // @@ -48,6 +82,72 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi #define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__) #define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__) +#define GGML_DEBUG 0 + +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#if (GGML_DEBUG >= 5) +#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_5(...) +#endif + +#if (GGML_DEBUG >= 10) +#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_10(...) +#endif + +// tensor params + +static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { + GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings + assert(params_size <= GGML_MAX_OP_PARAMS); + memcpy(tensor->op_params, params, params_size); +} + +static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + return ((const int32_t *)(tensor->op_params))[i]; +} + +static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + return ((const float *)(tensor->op_params))[i]; +} + +static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + ((int32_t *)(tensor->op_params))[i] = value; +} + +static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + ((float *)(tensor->op_params))[i] = value; +} + +struct ggml_map_custom1_op_params { + ggml_custom1_op_t fun; + int n_tasks; + void * userdata; +}; + +struct ggml_map_custom2_op_params { + ggml_custom2_op_t fun; + int n_tasks; + void * userdata; +}; + +struct ggml_map_custom3_op_params { + ggml_custom3_op_t fun; + int n_tasks; + void * userdata; +}; + // bitset typedef uint32_t ggml_bitset_t; @@ -96,7 +196,7 @@ void ggml_hash_set_reset(struct ggml_hash_set * hash_set); static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key); // returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted -static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key); +static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key); // returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key); @@ -110,7 +210,7 @@ static inline size_t ggml_hash(const struct ggml_tensor * p) { return (size_t)(uintptr_t)p >> 4; } -static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) { +static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key) { size_t h = ggml_hash(key) % hash_set->size; // linear probing @@ -181,21 +281,274 @@ enum ggml_cgraph_eval_order { }; struct ggml_cgraph { - int size; - int n_nodes; - int n_leafs; + int size; // maximum number of nodes/leafs/grads/grad_accs + int n_nodes; // number of nodes currently in use + int n_leafs; // number of leafs currently in use - struct ggml_tensor ** nodes; - struct ggml_tensor ** grads; - struct ggml_tensor ** leafs; + struct ggml_tensor ** nodes; // tensors with data that can change if the graph is evaluated + struct ggml_tensor ** grads; // the outputs of these tensors are the gradients of the nodes + struct ggml_tensor ** grad_accs; // accumulators for node gradients + struct ggml_tensor ** leafs; // tensors with constant data struct ggml_hash_set visited_hash_set; enum ggml_cgraph_eval_order order; }; +// returns a slice of cgraph with nodes [i0, i1) +// the slice does not have leafs or gradients +// if you need the gradients, get them from the original graph struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1); +// Memory allocation + +void * ggml_aligned_malloc(size_t size); +void ggml_aligned_free(void * ptr, size_t size); + +// FP16 to FP32 conversion + +#if defined(__ARM_NEON) + #ifdef _MSC_VER + typedef uint16_t ggml_fp16_internal_t; + #else + typedef __fp16 ggml_fp16_internal_t; + #endif +#endif + +#if defined(__ARM_NEON) && !defined(_MSC_VER) + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + + #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + ggml_fp16_internal_t tmp; + memcpy(&tmp, &h, sizeof(ggml_fp16_t)); + return (float)tmp; + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + ggml_fp16_t res; + ggml_fp16_internal_t tmp = f; + memcpy(&res, &tmp, sizeof(ggml_fp16_t)); + return res; + } + +#elif defined(__F16C__) + + #ifdef _MSC_VER + #define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) + #define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) + #else + #define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) + #endif + +#elif defined(__POWER9_VECTOR__) + + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + /* the inline asm below is about 12% faster than the lookup method */ + #define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) + #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + register float f; + register double d; + __asm__( + "mtfprd %0,%2\n" + "xscvhpdp %0,%0\n" + "frsp %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=f"(f): + /* in */ "r"(h)); + return f; + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + register double d; + register ggml_fp16_t r; + __asm__( /* xscvdphp can work on double or single precision */ + "xscvdphp %0,%2\n" + "mffprd %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=r"(r): + /* in */ "f"(f)); + return r; + } + +#else + + // FP16 <-> FP32 + // ref: https://github.com/Maratyszcza/FP16 + + static inline float fp32_from_bits(uint32_t w) { + union { + uint32_t as_bits; + float as_value; + } fp32; + fp32.as_bits = w; + return fp32.as_value; + } + + static inline uint32_t fp32_to_bits(float f) { + union { + float as_value; + uint32_t as_bits; + } fp32; + fp32.as_value = f; + return fp32.as_bits; + } + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + const uint32_t w = (uint32_t) h << 16; + const uint32_t sign = w & UINT32_C(0x80000000); + const uint32_t two_w = w + w; + + const uint32_t exp_offset = UINT32_C(0xE0) << 23; + #if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L) + const float exp_scale = 0x1.0p-112f; + #else + const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); + #endif + const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; + + const uint32_t magic_mask = UINT32_C(126) << 23; + const float magic_bias = 0.5f; + const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; + + const uint32_t denormalized_cutoff = UINT32_C(1) << 27; + const uint32_t result = sign | + (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); + return fp32_from_bits(result); + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + #if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L) + const float scale_to_inf = 0x1.0p+112f; + const float scale_to_zero = 0x1.0p-110f; + #else + const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); + const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); + #endif + float base = (fabsf(f) * scale_to_inf) * scale_to_zero; + + const uint32_t w = fp32_to_bits(f); + const uint32_t shl1_w = w + w; + const uint32_t sign = w & UINT32_C(0x80000000); + uint32_t bias = shl1_w & UINT32_C(0xFF000000); + if (bias < UINT32_C(0x71000000)) { + bias = UINT32_C(0x71000000); + } + + base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; + const uint32_t bits = fp32_to_bits(base); + const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); + const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); + const uint32_t nonsign = exp_bits + mantissa_bits; + return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); + } + + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + +#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) + +// precomputed f32 table for f16 (256 KB) +// defined in ggml.c, initialized in ggml_init() +GGML_API float ggml_table_f32_f16[1 << 16]; + +// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, +// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. +// This is also true for POWER9. +#if !defined(GGML_FP16_TO_FP32) +inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { + uint16_t s; + memcpy(&s, &f, sizeof(uint16_t)); + return ggml_table_f32_f16[s]; +} + +#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) +#endif + +#if !defined(GGML_FP32_TO_FP16) +#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) +#endif + +/** + * Converts brain16 to float32. + * + * The bfloat16 floating point format has the following structure: + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───┐ + * 0b0000000000000000 brain16 + * + * Since bf16 has the same number of exponent bits as a 32bit float, + * encoding and decoding numbers becomes relatively straightforward. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───────────────────┐ + * 0b00000000000000000000000000000000 IEEE binary32 + * + * For comparison, the standard fp16 format has fewer exponent bits. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌─┴─┐┌─┴──────┐ + * 0b0000000000000000 IEEE binary16 + * + * @see IEEE 754-2008 + */ +static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { + union { + float f; + uint32_t i; + } u; + u.i = (uint32_t)h.bits << 16; + return u.f; +} + +/** + * Converts float32 to brain16. + * + * This is binary identical with Google Brain float conversion. + * Floats shall round to nearest even, and NANs shall be quiet. + * Subnormals aren't flushed to zero, except perhaps when used. + * This code should vectorize nicely if using modern compilers. + */ +static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { + ggml_bf16_t h; + union { + float f; + uint32_t i; + } u; + u.f = s; + if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ + h.bits = (u.i >> 16) | 64; /* force to quiet */ + return h; + } + h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; + return h; +} + +#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) +#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-kompute/CMakeLists.txt b/ggml/src/ggml-kompute/CMakeLists.txt new file mode 100644 index 0000000000..0bd027c7f5 --- /dev/null +++ b/ggml/src/ggml-kompute/CMakeLists.txt @@ -0,0 +1,162 @@ + +find_package(Vulkan COMPONENTS glslc REQUIRED) +find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) + +if (NOT glslc_executable) + message(FATAL_ERROR "glslc not found") +endif() + +add_library(ggml-kompute + ggml-kompute.cpp + ../../include/ggml-kompute.h + ) + +target_link_libraries(ggml-kompute PRIVATE ggml-base kompute) +target_include_directories(ggml-kompute PRIVATE . .. ${CMAKE_CURRENT_BINARY_DIR}) + +add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) + +function(compile_shader) + set(options) + set(oneValueArgs) + set(multiValueArgs SOURCES) + cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + foreach(source ${compile_shader_SOURCES}) + get_filename_component(filename ${source} NAME) + set(spv_file ${filename}.spv) + add_custom_command( + OUTPUT ${spv_file} + DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp + COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} + COMMENT "Compiling ${source} to ${spv_file}" + ) + + get_filename_component(RAW_FILE_NAME ${spv_file} NAME) + set(FILE_NAME "shader${RAW_FILE_NAME}") + string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) + string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) + string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") + set(OUTPUT_HEADER_FILE "${HEADER_FILE}") + message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") + if(CMAKE_GENERATOR MATCHES "Visual Studio") + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" + ) + else() + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" + ) + endif() + endforeach() +endfunction() + +if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") + message(STATUS "Kompute found") + set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level") + add_subdirectory(kompute) + + # Compile our shaders + compile_shader(SOURCES + kompute-shaders/op_scale.comp + kompute-shaders/op_scale_8.comp + kompute-shaders/op_add.comp + kompute-shaders/op_addrow.comp + kompute-shaders/op_mul.comp + kompute-shaders/op_silu.comp + kompute-shaders/op_relu.comp + kompute-shaders/op_gelu.comp + kompute-shaders/op_softmax.comp + kompute-shaders/op_norm.comp + kompute-shaders/op_rmsnorm.comp + kompute-shaders/op_diagmask.comp + kompute-shaders/op_mul_mat_mat_f32.comp + kompute-shaders/op_mul_mat_f16.comp + kompute-shaders/op_mul_mat_q8_0.comp + kompute-shaders/op_mul_mat_q4_0.comp + kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q4_k.comp + kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f32.comp + kompute-shaders/op_getrows_f16.comp + kompute-shaders/op_getrows_q4_0.comp + kompute-shaders/op_getrows_q4_1.comp + kompute-shaders/op_getrows_q6_k.comp + kompute-shaders/op_rope_f16.comp + kompute-shaders/op_rope_f32.comp + kompute-shaders/op_cpy_f16_f16.comp + kompute-shaders/op_cpy_f16_f32.comp + kompute-shaders/op_cpy_f32_f16.comp + kompute-shaders/op_cpy_f32_f32.comp + ) + + # Create a custom target for our generated shaders + add_custom_target(generated_shaders DEPENDS + shaderop_scale.h + shaderop_scale_8.h + shaderop_add.h + shaderop_addrow.h + shaderop_mul.h + shaderop_silu.h + shaderop_relu.h + shaderop_gelu.h + shaderop_softmax.h + shaderop_norm.h + shaderop_rmsnorm.h + shaderop_diagmask.h + shaderop_mul_mat_mat_f32.h + shaderop_mul_mat_f16.h + shaderop_mul_mat_q8_0.h + shaderop_mul_mat_q4_0.h + shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q4_k.h + shaderop_mul_mat_q6_k.h + shaderop_getrows_f32.h + shaderop_getrows_f16.h + shaderop_getrows_q4_0.h + shaderop_getrows_q4_1.h + shaderop_getrows_q6_k.h + shaderop_rope_f16.h + shaderop_rope_f32.h + shaderop_cpy_f16_f16.h + shaderop_cpy_f16_f32.h + shaderop_cpy_f32_f16.h + shaderop_cpy_f32_f32.h + ) + + # Create a custom command that depends on the generated_shaders + add_custom_command( + OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + DEPENDS generated_shaders + COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp" + ) + + # Add the stamp to the main sources to ensure dependency tracking + target_sources(ggml-kompute PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) +else() + message(WARNING "Kompute not found") +endif() diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute/ggml-kompute.cpp similarity index 88% rename from ggml/src/ggml-kompute.cpp rename to ggml/src/ggml-kompute/ggml-kompute.cpp index 2c926aaeec..2fea9e4cc8 100644 --- a/ggml/src/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute/ggml-kompute.cpp @@ -20,6 +20,7 @@ #include "shaderop_mul_mat_q8_0.h" #include "shaderop_mul_mat_q4_0.h" #include "shaderop_mul_mat_q4_1.h" +#include "shaderop_mul_mat_q4_k.h" #include "shaderop_mul_mat_q6_k.h" #include "shaderop_mul_mat_mat_f32.h" #include "shaderop_getrows_f32.h" @@ -42,6 +43,7 @@ #include #include #include +#include #include #include #include @@ -273,18 +275,9 @@ static std::vector ggml_vk_available_devices_internal(size_t mem return results; } -// public API returns a C-style array -ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) { - auto devices = ggml_vk_available_devices_internal(memoryRequired); - *count = devices.size(); - if (devices.empty()) { - return nullptr; - } - - size_t nbytes = sizeof (ggml_vk_device) * (devices.size()); - auto * arr = static_cast(malloc(nbytes)); - memcpy(arr, devices.data(), nbytes); - return arr; +static std::vector& ggml_vk_available_devices() { + static std::vector devices = ggml_vk_available_devices_internal(0); + return devices; } static void ggml_vk_filterByVendor(std::vector& devices, const std::string& targetVendor) { @@ -341,7 +334,7 @@ ggml_vk_device ggml_vk_current_device() { if (!komputeManager()->hasDevice()) return ggml_vk_device(); - auto devices = ggml_vk_available_devices_internal(0); + auto devices = ggml_vk_available_devices(); ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data()); GGML_ASSERT(!devices.empty()); return devices.front(); @@ -1075,6 +1068,40 @@ static void ggml_vk_mul_mat_q8_0(Args&&... args) { ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward(args)...); } +static void ggml_vk_mul_mat_q4_k( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, + int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, + int32_t ne1, int32_t r2, int32_t r3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv, + kp::shader_data::op_mul_mat_q4_k_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3; + } pushConsts { + 0, 0, 0, + ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + static void ggml_vk_mul_mat_q6_k( kp::Sequence& seq, const std::shared_ptr& inA, @@ -1323,17 +1350,7 @@ static void ggml_vk_cpy_f16_f32(Args&&... args) { ggml_vk_cpy(spirv, 2, 4, std::forward(args)...); } -static bool ggml_vk_supports_op(const struct ggml_tensor * op) { - switch (op->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - break; - default: - return false; - } - +static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -1402,6 +1419,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_K: return true; default: ; @@ -1410,6 +1428,8 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { ; } return false; + + GGML_UNUSED(dev); } static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) { @@ -1458,11 +1478,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml any_commands_recorded = true; - if (!ggml_vk_supports_op(dst)) { - fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ABORT("unsupported op"); - } - const int32_t ne00 = src0 ? src0->ne[0] : 0; const int32_t ne01 = src0 ? src0->ne[1] : 0; const int32_t ne02 = src0 ? src0->ne[2] : 0; @@ -1656,6 +1671,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 ); break; + case GGML_TYPE_Q4_K: + ggml_vk_mul_mat_q4_k( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, ne12/ne02, ne13/ne03 + ); + break; case GGML_TYPE_Q6_K: ggml_vk_mul_mat_q6_k( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, @@ -1820,11 +1841,6 @@ static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) { } } -static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) { - auto * ctx = static_cast(buffer->buft->context); - return ctx->name.c_str(); -} - static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) { auto * memory = (ggml_vk_memory *)buffer->context; if (ggml_vk_has_device()) { @@ -1868,7 +1884,6 @@ static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint } static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = { - /* .get_name = */ ggml_backend_kompute_buffer_get_name, /* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer, /* .get_base = */ ggml_backend_kompute_buffer_get_base, /* .init_tensor = */ NULL, @@ -1913,25 +1928,31 @@ static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = { }; ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) { - static std::vector bufts = []() { - std::vector vec; - auto devices = ggml_vk_available_devices_internal(0); - vec.reserve(devices.size()); + static std::mutex mutex; + std::lock_guard lock(mutex); - for (const auto & dev : devices) { - vec.push_back({ - /* .iface = */ ggml_backend_kompute_buffer_type_interface, - /* .device = */ nullptr, - /* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc) - }); + auto devices = ggml_vk_available_devices(); + int32_t device_count = (int32_t) devices.size(); + GGML_ASSERT(device < device_count); + GGML_ASSERT(devices.size() <= GGML_KOMPUTE_MAX_DEVICES); + + static ggml_backend_buffer_type + ggml_backend_kompute_buffer_types[GGML_KOMPUTE_MAX_DEVICES]; + + static bool ggml_backend_kompute_buffer_type_initialized = false; + + if (!ggml_backend_kompute_buffer_type_initialized) { + for (int32_t i = 0; i < device_count; i++) { + ggml_backend_kompute_buffer_types[i] = { + /* .iface = */ ggml_backend_kompute_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), i), + /* .context = */ new ggml_backend_kompute_buffer_type_context{ i, devices[i].bufferAlignment, devices[i].maxAlloc }, + }; } - return vec; - }(); + ggml_backend_kompute_buffer_type_initialized = true; + } - auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) { - return device == static_cast(t.context)->device; - }); - return it < bufts.end() ? &*it : nullptr; + return &ggml_backend_kompute_buffer_types[device]; } // backend @@ -1953,31 +1974,15 @@ static void ggml_backend_kompute_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) { - auto * ctx = static_cast(backend->context); - return ggml_backend_kompute_buffer_type(ctx->device); -} - static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { auto * ctx = static_cast(backend->context); ggml_vk_graph_compute(ctx, cgraph); return GGML_STATUS_SUCCESS; } -static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - GGML_UNUSED(backend); - return ggml_vk_supports_op(op); -} - -static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - GGML_UNUSED(backend); - return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name; -} - static struct ggml_backend_i kompute_backend_i = { /* .get_name = */ ggml_backend_kompute_name, /* .free = */ ggml_backend_kompute_free, - /* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -1987,9 +1992,6 @@ static struct ggml_backend_i kompute_backend_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_kompute_graph_compute, - /* .supports_op = */ ggml_backend_kompute_supports_op, - /* .supports_buft = */ ggml_backend_kompute_supports_buft, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -2006,7 +2008,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) { ggml_backend_t kompute_backend = new ggml_backend { /* .guid = */ ggml_backend_kompute_guid(), /* .interface = */ kompute_backend_i, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), device), /* .context = */ s_kompute_context, }; @@ -2016,3 +2018,167 @@ ggml_backend_t ggml_backend_kompute_init(int device) { bool ggml_backend_is_kompute(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid()); } + +static size_t ggml_backend_kompute_get_device_count() { + auto devices = ggml_vk_available_devices(); + return devices.size(); +} + +static void ggml_backend_kompute_get_device_description(int device, char * description, size_t description_size) { + auto devices = ggml_vk_available_devices(); + GGML_ASSERT((size_t) device < devices.size()); + snprintf(description, description_size, "%s", devices[device].name); +} + +static void ggml_backend_kompute_get_device_memory(int device, size_t * free, size_t * total) { + auto devices = ggml_vk_available_devices(); + GGML_ASSERT((size_t) device < devices.size()); + *total = devices[device].heapSize; + *free = devices[device].heapSize; +} + +////////////////////////// + +struct ggml_backend_kompute_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_kompute_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_kompute_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_kompute_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + ggml_backend_kompute_get_device_memory(ctx->device, free, total); +} + +static ggml_backend_buffer_type_t ggml_backend_kompute_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ggml_backend_kompute_buffer_type(ctx->device); +} + +static bool ggml_backend_kompute_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_kompute_buffer_type_get_name) { + return false; + } + + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + ggml_backend_kompute_buffer_type_context * buft_ctx = (ggml_backend_kompute_buffer_type_context *)buft->context; + + return buft_ctx->device == ctx->device; +} + +static enum ggml_backend_dev_type ggml_backend_kompute_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_kompute_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_kompute_device_get_name(dev); + props->description = ggml_backend_kompute_device_get_description(dev); + props->type = ggml_backend_kompute_device_get_type(dev); + ggml_backend_kompute_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* async = */ false, + /* host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* events = */ false, + }; +} + +static ggml_backend_t ggml_backend_kompute_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ggml_backend_kompute_init(ctx->device); +} + +static bool ggml_backend_kompute_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + const int min_batch_size = 32; + + return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || + (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_kompute_device_i = { + /* .get_name = */ ggml_backend_kompute_device_get_name, + /* .get_description = */ ggml_backend_kompute_device_get_description, + /* .get_memory = */ ggml_backend_kompute_device_get_memory, + /* .get_type = */ ggml_backend_kompute_device_get_type, + /* .get_props = */ ggml_backend_kompute_device_get_props, + /* .init_backend = */ ggml_backend_kompute_device_init, + /* .get_buffer_type = */ ggml_backend_kompute_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_kompute_device_supports_op, + /* .supports_buft = */ ggml_backend_kompute_device_supports_buft, + /* .offload_op = */ ggml_backend_kompute_device_offload_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static const char * ggml_backend_kompute_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return "Kompute"; +} + +static size_t ggml_backend_kompute_reg_get_device_count(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return ggml_backend_kompute_get_device_count(); +} + +static ggml_backend_dev_t ggml_backend_kompute_reg_get_device(ggml_backend_reg_t reg, size_t device) { + static std::vector devices; + + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + for (size_t i = 0; i < ggml_backend_kompute_get_device_count(); i++) { + ggml_backend_kompute_device_context * ctx = new ggml_backend_kompute_device_context; + char desc[256]; + ggml_backend_kompute_get_device_description(i, desc, sizeof(desc)); + ctx->device = i; + ctx->name = "Kompute" + std::to_string(i); + ctx->description = desc; + devices.push_back(new ggml_backend_device { + /* .iface = */ ggml_backend_kompute_device_i, + /* .reg = */ reg, + /* .context = */ ctx, + }); + } + initialized = true; + } + } + + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static const struct ggml_backend_reg_i ggml_backend_kompute_reg_i = { + /* .get_name = */ ggml_backend_kompute_reg_get_name, + /* .get_device_count = */ ggml_backend_kompute_reg_get_device_count, + /* .get_device = */ ggml_backend_kompute_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_kompute_reg() { + static ggml_backend_reg reg = { + /* .iface = */ ggml_backend_kompute_reg_i, + /* .context = */ nullptr, + }; + + return ® +} diff --git a/ggml/src/kompute b/ggml/src/ggml-kompute/kompute similarity index 100% rename from ggml/src/kompute rename to ggml/src/ggml-kompute/kompute diff --git a/ggml/src/kompute-shaders/common.comp b/ggml/src/ggml-kompute/kompute-shaders/common.comp similarity index 95% rename from ggml/src/kompute-shaders/common.comp rename to ggml/src/ggml-kompute/kompute-shaders/common.comp index 62d62b025e..2aaddf704a 100644 --- a/ggml/src/kompute-shaders/common.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/common.comp @@ -15,6 +15,7 @@ #define TWOPI_F 6.283185307179586f #define QK_K 256 +#define K_SCALE_SIZE 12 #define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx]) #define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx) @@ -64,6 +65,14 @@ mat4 dequantize_q4_1(const block_q4_1 xb, uint il) { return reg; } +#define sizeof_block_q4_k 144 +struct block_q4_k { + float16_t d; + float16_t dmin; + uint8_t scales[K_SCALE_SIZE]; + uint8_t qs[QK_K/2]; +}; + #define sizeof_block_q6_k 210 struct block_q6_k { uint8_t ql[QK_K/2]; // quants, lower 4 bits diff --git a/ggml/src/kompute-shaders/op_add.comp b/ggml/src/ggml-kompute/kompute-shaders/op_add.comp similarity index 100% rename from ggml/src/kompute-shaders/op_add.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_add.comp diff --git a/ggml/src/kompute-shaders/op_addrow.comp b/ggml/src/ggml-kompute/kompute-shaders/op_addrow.comp similarity index 100% rename from ggml/src/kompute-shaders/op_addrow.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_addrow.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f16_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f16.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f16_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f32.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f32_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f16.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f32_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f32.comp diff --git a/ggml/src/kompute-shaders/op_diagmask.comp b/ggml/src/ggml-kompute/kompute-shaders/op_diagmask.comp similarity index 100% rename from ggml/src/kompute-shaders/op_diagmask.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_diagmask.comp diff --git a/ggml/src/kompute-shaders/op_gelu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_gelu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_gelu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_gelu.comp diff --git a/ggml/src/kompute-shaders/op_getrows.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows.comp diff --git a/ggml/src/kompute-shaders/op_getrows_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_f16.comp diff --git a/ggml/src/kompute-shaders/op_getrows_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_f32.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q4_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q4_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_0.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q4_1.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_1.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q4_1.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_1.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q6_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q6_k.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q6_k.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q6_k.comp diff --git a/ggml/src/kompute-shaders/op_mul.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_mat_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_mat_f32.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q4_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_0.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_1.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q4_1.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_1.comp diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp new file mode 100644 index 0000000000..fc8e45aa97 --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp @@ -0,0 +1,133 @@ +#version 450 + +#include "common.comp" + +#define N_DST 4 +#define SIZE_OF_BLOCK sizeof_block_q4_k + +layout(local_size_x = 4) in; +layout(local_size_y = 8) in; +layout(local_size_z = 1) in; + +layout (binding = 0) readonly buffer tensorInA { block_q4_k inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne10; + int ne0; + int ne1; + int ne01; + int ne02; + int ne12; + int r2; + int r3; +} pcs; + +void main() { + const uint16_t kmask1 = uint16_t(0x3f3f); + const uint16_t kmask2 = uint16_t(0x0f0f); + const uint16_t kmask3 = uint16_t(0xc0c0); + + const uint ix = gl_SubgroupInvocationID/8; // 0...3 + const uint it = gl_SubgroupInvocationID%8; // 0...7 + const uint iq = it/4; // 0 or 1 + const uint ir = it%4; // 0...3 + + const uint nb = pcs.ne00/QK_K; + + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint im = gl_WorkGroupID.z; + + const uint first_row = r0 * N_DST; + const uint ib_row = first_row * nb; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + + const uint xblk = ib_row + offset0 + pcs.inAOff; + const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; + + float yl[16]; + float yh[16]; + float sumf[N_DST] = {0.f, 0.f, 0.f, 0.f}; + float all_sum = 0.f; + + uint y4 = y + ix * QK_K + 64 * iq + 8 * ir; + + for (uint ib = ix; ib < nb; ib += 4) { + const uint blk_idx = ib + xblk; + + float sumy[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; ++i) { + yl[i+0] = inB[y4+i+ 0]; sumy[0] += yl[i+0]; + yl[i+8] = inB[y4+i+ 32]; sumy[1] += yl[i+8]; + yh[i+0] = inB[y4+i+128]; sumy[2] += yh[i+0]; + yh[i+8] = inB[y4+i+160]; sumy[3] += yh[i+8]; + } + + for (int row = 0; row < N_DST; row++) { + uint row_idx = row * nb; + + uint16_t sc_0 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 0); + uint16_t sc_1 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 2); + uint16_t sc_2 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 4); + uint16_t sc_3 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 6); + uint16_t sc_4 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 8); + + uint16_t sc16[4]; + sc16[0] = sc_0 & kmask1; + sc16[1] = sc_2 & kmask1; + sc16[2] = ((sc_4 >> 0) & kmask2) | ((sc_0 & kmask3) >> 2); + sc16[3] = ((sc_4 >> 4) & kmask2) | ((sc_2 & kmask3) >> 2); + + float acc1[4] = {0.f, 0.f, 0.f, 0.f}; + float acc2[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; i += 2) { + uint16_t q1 = u8BufToU16(inA[blk_idx + row_idx].qs, 32 * iq + 8 * ir + i); + uint16_t q2 = u8BufToU16(inA[blk_idx + row_idx].qs, 64 + 32 * iq + 8 * ir + i); + acc1[0] += yl[i+0] * (q1 & 0x000F); + acc1[1] += yl[i+1] * (q1 & 0x0F00); + acc1[2] += yl[i+8] * (q1 & 0x00F0); + acc1[3] += yl[i+9] * (q1 & 0xF000); + acc2[0] += yh[i+0] * (q2 & 0x000F); + acc2[1] += yh[i+1] * (q2 & 0x0F00); + acc2[2] += yh[i+8] * (q2 & 0x00F0); + acc2[3] += yh[i+9] * (q2 & 0xF000); + } + + uint8_t sc8_0 = uint8_t(sc16[0] & 0xFF); + uint8_t sc8_1 = uint8_t(sc16[0] >> 8 ); + uint8_t sc8_2 = uint8_t(sc16[1] & 0xFF); + uint8_t sc8_3 = uint8_t(sc16[1] >> 8 ); + uint8_t sc8_4 = uint8_t(sc16[2] & 0xFF); + uint8_t sc8_5 = uint8_t(sc16[2] >> 8 ); + uint8_t sc8_6 = uint8_t(sc16[3] & 0xFF); + uint8_t sc8_7 = uint8_t(sc16[3] >> 8 ); + + float dall = float(inA[blk_idx + row_idx].d); + float dmin = float(inA[blk_idx + row_idx].dmin); + sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc8_0 + + (acc1[2] + 1.f/256.f * acc1[3]) * sc8_1 * 1.f/16.f + + (acc2[0] + 1.f/256.f * acc2[1]) * sc8_4 + + (acc2[2] + 1.f/256.f * acc2[3]) * sc8_5 * 1.f/16.f) - + dmin * (sumy[0] * sc8_2 + sumy[1] * sc8_3 + sumy[2] * sc8_6 + sumy[3] * sc8_7); + } + + y4 += 4 * QK_K; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = subgroupAdd(sumf[row]); + if (subgroupElect()) { + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = all_sum; + } + } +} diff --git a/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q6_k.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q8_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q8_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q8_0.comp diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mv_q_n.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp diff --git a/ggml/src/kompute-shaders/op_norm.comp b/ggml/src/ggml-kompute/kompute-shaders/op_norm.comp similarity index 100% rename from ggml/src/kompute-shaders/op_norm.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_norm.comp diff --git a/ggml/src/kompute-shaders/op_relu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_relu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_relu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_relu.comp diff --git a/ggml/src/kompute-shaders/op_rmsnorm.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rmsnorm.comp similarity index 100% rename from ggml/src/kompute-shaders/op_rmsnorm.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_rmsnorm.comp diff --git a/ggml/src/kompute-shaders/op_rope_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_rope_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp diff --git a/ggml/src/kompute-shaders/op_rope_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_rope_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp diff --git a/ggml/src/kompute-shaders/op_scale.comp b/ggml/src/ggml-kompute/kompute-shaders/op_scale.comp similarity index 100% rename from ggml/src/kompute-shaders/op_scale.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_scale.comp diff --git a/ggml/src/kompute-shaders/op_scale_8.comp b/ggml/src/ggml-kompute/kompute-shaders/op_scale_8.comp similarity index 100% rename from ggml/src/kompute-shaders/op_scale_8.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_scale_8.comp diff --git a/ggml/src/kompute-shaders/op_silu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_silu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_silu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_silu.comp diff --git a/ggml/src/kompute-shaders/op_softmax.comp b/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp similarity index 100% rename from ggml/src/kompute-shaders/op_softmax.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp diff --git a/ggml/src/kompute-shaders/rope_common.comp b/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp similarity index 100% rename from ggml/src/kompute-shaders/rope_common.comp rename to ggml/src/ggml-kompute/kompute-shaders/rope_common.comp diff --git a/ggml/src/ggml-metal/CMakeLists.txt b/ggml/src/ggml-metal/CMakeLists.txt new file mode 100644 index 0000000000..b237d79f47 --- /dev/null +++ b/ggml/src/ggml-metal/CMakeLists.txt @@ -0,0 +1,108 @@ +find_library(FOUNDATION_LIBRARY Foundation REQUIRED) +find_library(METAL_FRAMEWORK Metal REQUIRED) +find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + +message(STATUS "Metal framework found") + +add_library(ggml-metal + ggml-metal.m + ) + +target_link_libraries(ggml-metal PRIVATE + ggml-base + ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} + ${METALKIT_FRAMEWORK} + ) + +target_include_directories(ggml-metal PRIVATE . ..) + +if (GGML_METAL_NDEBUG) + add_compile_definitions(GGML_METAL_NDEBUG) +endif() + +if (GGML_METAL_USE_BF16) + add_compile_definitions(GGML_METAL_USE_BF16) +endif() + +# copy metal files to bin directory +configure_file(../ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) +configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) +configure_file(ggml-metal-impl.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h COPYONLY) + +if (GGML_METAL_EMBED_LIBRARY) + enable_language(ASM) + + add_compile_definitions(GGML_METAL_EMBED_LIBRARY) + + set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h") + set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") + set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h") + + file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") + + # merge ggml-common.h and ggml-metal.metal into a single file + set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s") + set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal") + set(METALLIB_SOURCE_EMBED_TMP "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal.tmp") + + add_custom_command( + OUTPUT ${METALLIB_EMBED_ASM} + COMMAND echo "Embedding Metal library" + COMMAND sed -e '/__embed_ggml-common.h__/r ${METALLIB_COMMON}' -e '/__embed_ggml-common.h__/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED_TMP} + COMMAND sed -e '/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}' -e '/\#include \"ggml-metal-impl.h\"/d' < ${METALLIB_SOURCE_EMBED_TMP} > ${METALLIB_SOURCE_EMBED} + COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM} + DEPENDS ../ggml-common.h ggml-metal.metal ggml-metal-impl.h + COMMENT "Generate assembly for embedded Metal library" + ) + + target_sources(ggml-metal PRIVATE ${METALLIB_EMBED_ASM}) +else() + if (GGML_METAL_SHADER_DEBUG) + # custom command to do the following: + # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air + # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib + # + # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works + # disabling fast math is needed in order to pass tests/test-backend-ops + # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 + # note: unfortunately, we have to call it default.metallib instead of ggml.metallib + # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 + set(XC_FLAGS -fno-fast-math -fno-inline -g) + else() + set(XC_FLAGS -O3) + endif() + + # Append macOS metal versioning flags + if (GGML_METAL_MACOSX_VERSION_MIN) + message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation") + list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN}) + endif() + + if (GGML_METAL_STD) + message(STATUS "Adding -std=${GGML_METAL_STD} flag to metal compilation") + list (APPEND XC_FLAGS -std=${GGML_METAL_STD}) + endif() + + add_custom_command( + OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal + DEPENDS ggml-metal.metal ggml-common.h + COMMENT "Compiling Metal kernels" + ) + + # FIXME: only add to the ggml-metal target? + add_custom_target( + ggml-metal-lib ALL + DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + ) +endif() # GGML_METAL_EMBED_LIBRARY diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h new file mode 100644 index 0000000000..53c1354965 --- /dev/null +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -0,0 +1,249 @@ +#ifndef GGML_METAL_IMPL +#define GGML_METAL_IMPL + +// kernel argument structs +// +// - element counters (e.g. ne00) typically use int32_t to reduce register usage +// however, be careful from int overflows when using those in the kernel implementation +// +// - strides (e.g. nb00) use uint64_t + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + int32_t dim; +} ggml_metal_kargs_concat; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + uint64_t offs; +} ggml_metal_kargs_bin; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; +} ggml_metal_kargs_repeat; + +typedef struct { + int64_t ne00; + int64_t ne01; + int64_t ne02; + int64_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int64_t ne0; + int64_t ne1; + int64_t ne2; + int64_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; +} ggml_metal_kargs_cpy; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + int32_t n_past; + int32_t n_dims; + int32_t n_ctx_orig; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; +} ggml_metal_kargs_rope; + +typedef struct { + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne11; + int32_t ne_12_2; // assume K and V are same shape + int32_t ne_12_3; + uint64_t nb_12_1; + uint64_t nb_12_2; + uint64_t nb_12_3; + uint64_t nb31; + int32_t ne1; + int32_t ne2; + float scale; + float max_bias; + float m0; + float m1; + uint16_t n_head_log2; + float logit_softcap; +} ggml_metal_kargs_flash_attn_ext; + +typedef struct { + int32_t ne00; + int32_t ne02; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int16_t r2; + int16_t r3; +} ggml_metal_kargs_mul_mm; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int16_t r2; + int16_t r3; +} ggml_metal_kargs_mul_mv; + +typedef struct { + int32_t nei0; + int32_t nei1; + uint64_t nbi1; + int32_t ne00; + int32_t ne02; + uint64_t nb01; + uint64_t nb02; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + int32_t ne0; + int32_t ne1; +} ggml_metal_kargs_mul_mm_id; + +typedef struct { + int32_t nei0; + int32_t nei1; + uint64_t nbi1; + int32_t ne00; + int32_t ne01; + int32_t ne02; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + int32_t ne0; + int32_t ne1; + uint64_t nb1; +} ggml_metal_kargs_mul_mv_id; + +typedef struct { + int32_t ne00; + int32_t ne00_4; + uint64_t nb01; + float eps; +} ggml_metal_kargs_norm; + +typedef struct { + int32_t ne00; + int32_t ne00_4; + uint64_t nb01; + float eps; +} ggml_metal_kargs_rms_norm; + +#endif // GGML_METAL_IMPL diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m similarity index 71% rename from ggml/src/ggml-metal.m rename to ggml/src/ggml-metal/ggml-metal.m index 172a0f925d..d1abb3cef0 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -2,6 +2,7 @@ #import "ggml-impl.h" #import "ggml-backend-impl.h" +#import "ggml-metal-impl.h" #import @@ -36,16 +37,20 @@ static struct ggml_backend_metal_device_context { id mtl_device; int mtl_device_ref_count; - bool support_simdgroup_reduction; - bool support_simdgroup_mm; + bool has_simdgroup_reduction; + bool has_simdgroup_mm; + bool has_bfloat; + bool use_bfloat; char name[128]; } g_ggml_ctx_dev_main = { - /*.mtl_device =*/ nil, - /*.mtl_device_ref_count =*/ 0, - /*.support_simdgroup_reduction =*/ false, - /*.support_simdgroup_mm =*/ false, - /*.name =*/ "", + /*.mtl_device =*/ nil, + /*.mtl_device_ref_count =*/ 0, + /*.has_simdgroup_reduction =*/ false, + /*.has_simdgroup_mm =*/ false, + /*.has_bfloat =*/ false, + /*.use_bfloat =*/ false, + /*.name =*/ "", }; // acquire @@ -55,10 +60,19 @@ static id ggml_backend_metal_device_acq(struct ggml_backend_metal_dev if (ctx->mtl_device == nil) { ctx->mtl_device = MTLCreateSystemDefaultDevice(); - ctx->support_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; - ctx->support_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; + ctx->has_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; - ctx->support_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + + ctx->has_bfloat = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; + ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6]; + +#if defined(GGML_METAL_USE_BF16) + ctx->use_bfloat = ctx->has_bfloat; +#else + ctx->use_bfloat = false; +#endif strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1); } @@ -112,6 +126,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, GGML_METAL_KERNEL_TYPE_SILU, GGML_METAL_KERNEL_TYPE_SILU_4, + GGML_METAL_KERNEL_TYPE_ELU, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, @@ -120,6 +135,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, + GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, @@ -146,10 +162,14 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, @@ -170,10 +190,11 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, - //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, @@ -195,6 +216,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, @@ -216,6 +238,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, @@ -241,6 +264,8 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F32, + GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, + GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, GGML_METAL_KERNEL_TYPE_ARANGE_F32, @@ -253,13 +278,64 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, - //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_F16, + GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, GGML_METAL_KERNEL_TYPE_CPY_F16_F16, GGML_METAL_KERNEL_TYPE_CPY_F16_F32, + GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, + GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, @@ -272,6 +348,8 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_SIN, GGML_METAL_KERNEL_TYPE_COS, GGML_METAL_KERNEL_TYPE_SUM_ROWS, + GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, + GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, GGML_METAL_KERNEL_TYPE_COUNT }; @@ -436,7 +514,15 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; - MTLCompileOptions* options = [MTLCompileOptions new]; + if (ctx_dev->use_bfloat) { + [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"]; + } + +#if GGML_METAL_EMBED_LIBRARY + [prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"]; +#endif + + MTLCompileOptions * options = [MTLCompileOptions new]; options.preprocessorMacros = prep; //[options setFastMathEnabled:false]; @@ -446,7 +532,14 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } + +#if !__has_feature(objc_arc) + [options release]; +#endif } +#if GGML_METAL_EMBED_LIBRARY + [src release]; +#endif // GGML_METAL_EMBED_LIBRARY } } @@ -479,9 +572,11 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de } } - GGML_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx_dev->support_simdgroup_reduction ? "true" : "false"); - GGML_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx_dev->support_simdgroup_mm ? "true" : "false"); - GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); + GGML_LOG_INFO("%s: simdgroup reduction = %s\n", __func__, ctx_dev->has_simdgroup_reduction ? "true" : "false"); + GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, ctx_dev->has_simdgroup_mm ? "true" : "false"); + GGML_LOG_INFO("%s: has bfloat = %s\n", __func__, ctx_dev->has_bfloat ? "true" : "false"); + GGML_LOG_INFO("%s: use bfloat = %s\n", __func__, ctx_dev->use_bfloat ? "true" : "false"); + GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); ctx->capture_next_compute = false; ctx->capture_started = false; @@ -507,16 +602,14 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de ctx->kernels[i].pipeline = nil; } - /* - GGML_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ - (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ - (int) kernel->pipeline.threadExecutionWidth); \ - */ #define GGML_METAL_ADD_KERNEL(e, name, supported) \ if (supported) { \ struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ id metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \ kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \ + GGML_LOG_DEBUG("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ [metal_function release]; \ if (error) { \ GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ @@ -527,8 +620,9 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \ } - const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; - const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; + const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; + const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; + const bool use_bfloat = ctx_dev->use_bfloat; // simd_sum and simd_max requires MTLGPUFamilyApple7 @@ -556,14 +650,16 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ELU, elu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, get_rows_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); @@ -584,107 +680,116 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, ssm_conv_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, ssm_scan_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, mul_mv_bf16_f32, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, mul_mv_bf16_f32_1row, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, mul_mv_bf16_f32_l4, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, mul_mv_bf16_bf16, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, mul_mv_id_bf16_f32, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, mul_mm_bf16_f32, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, mul_mm_id_bf16_f32, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, has_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); @@ -692,18 +797,69 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, support_simdgroup_mm); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, flash_attn_ext_bf16_h64, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, flash_attn_ext_bf16_h80, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, flash_attn_ext_bf16_h96, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, flash_attn_ext_bf16_h112, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, flash_attn_ext_bf16_h128, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, flash_attn_ext_bf16_h256, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, flash_attn_ext_q4_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, flash_attn_ext_q4_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, flash_attn_ext_q4_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, flash_attn_ext_q4_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, flash_attn_ext_q4_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, flash_attn_ext_q4_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, flash_attn_ext_q4_1_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, flash_attn_ext_q4_1_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, flash_attn_ext_q4_1_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, flash_attn_ext_q4_1_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, flash_attn_ext_q4_1_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, flash_attn_ext_q4_1_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, flash_attn_ext_q5_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, flash_attn_ext_q5_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, flash_attn_ext_q5_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, flash_attn_ext_q5_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, flash_attn_ext_q5_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, flash_attn_ext_q5_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, flash_attn_ext_q5_1_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, flash_attn_ext_q5_1_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, flash_attn_ext_q5_1_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, flash_attn_ext_q5_1_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, flash_attn_ext_q5_1_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, flash_attn_ext_q5_1_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, flash_attn_ext_q8_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, flash_attn_ext_q8_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, flash_attn_ext_q8_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, flash_attn_ext_q8_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, flash_attn_ext_q8_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, flash_attn_ext_q8_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, flash_attn_ext_vec_bf16_h128, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, flash_attn_ext_vec_q4_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, flash_attn_ext_vec_q4_1_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, flash_attn_ext_vec_q5_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, flash_attn_ext_vec_q5_1_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, flash_attn_ext_vec_q8_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, flash_attn_ext_vec_bf16_h256, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, flash_attn_ext_vec_q4_0_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, flash_attn_ext_vec_q4_1_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, flash_attn_ext_vec_q5_0_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, flash_attn_ext_vec_q5_1_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, flash_attn_ext_vec_q8_0_h256, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, cpy_f32_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, cpy_bf16_f32, use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, cpy_bf16_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); @@ -716,6 +872,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true); } [metal_library release]; @@ -791,15 +949,18 @@ static id ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs } static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_context * ctx_dev, const struct ggml_tensor * op) { - for (size_t i = 0, n = 3; i < n; ++i) { - if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { - return false; + const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; + const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; + const bool use_bfloat = ctx_dev->use_bfloat; + + if (!use_bfloat) { + for (size_t i = 0, n = 3; i < n; ++i) { + if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { + return false; + } } } - const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; - const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; - switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -809,6 +970,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_ELU: return ggml_is_contiguous(op->src[0]); default: return false; @@ -837,15 +999,15 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_SOFT_MAX: case GGML_OP_RMS_NORM: case GGML_OP_GROUP_NORM: - return support_simdgroup_reduction; + return has_simdgroup_reduction; case GGML_OP_NORM: case GGML_OP_ROPE: return true; case GGML_OP_IM2COL: return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: return false; + case GGML_OP_POOL_2D: case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARANGE: @@ -854,22 +1016,16 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: - if (op->src[1]->type != GGML_TYPE_F16) { + if (op->src[1]->type != op->src[2]->type) { return false; } - if (op->src[2]->type != GGML_TYPE_F16) { - return false; - } - if (op->src[0]->ne[0] == 256) { - return false; - } - return support_simdgroup_mm; // TODO: over-restricted for vec-kernels + return has_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_SSM_CONV: case GGML_OP_SSM_SCAN: return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return support_simdgroup_reduction && + return has_simdgroup_reduction && (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32); case GGML_OP_CPY: case GGML_OP_DUP: @@ -880,6 +1036,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex switch (op->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -892,10 +1049,18 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex } case GGML_TYPE_F16: switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: + case GGML_TYPE_F32: + case GGML_TYPE_F16: return true; - default: + default: + return false; + } + case GGML_TYPE_BF16: + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_BF16: + return true; + default: return false; } default: @@ -981,7 +1146,7 @@ static void ggml_metal_encode_node( const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); const uint64_t nb21 = src2 ? src2->nb[1] : 0; const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); const int64_t ne0 = dst ? dst->ne[0] : 0; const int64_t ne1 = dst ? dst->ne[1] : 0; @@ -1007,19 +1172,21 @@ static void ggml_metal_encode_node( id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; - //GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - //if (src0) { - // GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, - // ggml_is_contiguous(src0), src0->name); - //} - //if (src1) { - // GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, - // ggml_is_contiguous(src1), src1->name); - //} - //if (dst) { - // GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, - // dst->name); - //} +#if 0 + GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); + if (src0) { + GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, + ggml_is_contiguous(src0), src0->name); + } + if (src1) { + GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, + ggml_is_contiguous(src1), src1->name); + } + if (dst) { + GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3, + dst->name); + } +#endif id device = ctx_dev->mtl_device; @@ -1030,35 +1197,39 @@ static void ggml_metal_encode_node( const int32_t dim = ((const int32_t *) dst->op_params)[0]; + ggml_metal_kargs_concat args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.dim =*/ dim, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; const int nth = MIN(1024, ne0); @@ -1076,8 +1247,6 @@ static void ggml_metal_encode_node( bool bcast_row = false; - int64_t nb = ne00; // used by the "row" kernels - id pipeline = nil; if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { @@ -1086,7 +1255,6 @@ static void ggml_metal_encode_node( // src1 is a row GGML_ASSERT(ne11 == 1); - nb = ne00 / 4; switch (dst->op) { case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; @@ -1106,36 +1274,39 @@ static void ggml_metal_encode_node( } } + ggml_metal_kargs_bin args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.offs =*/ offs, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; if (bcast_row) { const int64_t n = ggml_nelements(dst)/4; @@ -1159,25 +1330,29 @@ static void ggml_metal_encode_node( default: GGML_ABORT("fatal error"); } + ggml_metal_kargs_repeat args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); @@ -1206,25 +1381,29 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; + ggml_metal_kargs_cpy args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); @@ -1233,35 +1412,39 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + ggml_metal_kargs_bin args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ pnb1, + /*.nb02 =*/ pnb2, + /*.nb03 =*/ pnb3, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ pnb1, + /*.nb2 =*/ pnb2, + /*.nb3 =*/ pnb3, + /*.offs =*/ offs, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); @@ -1302,10 +1485,10 @@ static void ggml_metal_encode_node( memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float)); [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; const int64_t n = ggml_nelements(dst); @@ -1409,6 +1592,18 @@ static void ggml_metal_encode_node( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_UNARY_OP_ELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; default: { GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); @@ -1477,6 +1672,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1552,6 +1748,8 @@ static void ggml_metal_encode_node( const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + // TODO: add ggml_metal_kargs struct + // TODO: optimize (see https://github.com/ggerganov/llama.cpp/pull/10238/commits/7941b6b9ec29a2866fec6fa6c51612515ca509f6) [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; if (id_src1) { @@ -1568,6 +1766,7 @@ static void ggml_metal_encode_node( [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; @@ -1584,6 +1783,7 @@ static void ggml_metal_encode_node( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; } + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1608,6 +1808,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1678,6 +1879,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1764,6 +1966,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; default: break; } @@ -1772,6 +1975,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32 ].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; @@ -1794,22 +1998,29 @@ static void ggml_metal_encode_node( default: GGML_ABORT("MUL MAT-MAT not implemented"); } + ggml_metal_kargs_mul_mm args = { + /*.ne00 =*/ ne00, + /*.ne02 =*/ ne02, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.r2 =*/ r2, + /*.r3 =*/ r3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; } else { @@ -1847,6 +2058,25 @@ static void ggml_metal_encode_node( nrows = 4; } } break; + case GGML_TYPE_BF16: + { + nth0 = 32; + nth1 = 1; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW].pipeline; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4].pipeline; + nrows = ne11; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32].pipeline; + nrows = 4; + } + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16].pipeline; + nrows = 4; + } + } break; case GGML_TYPE_Q4_0: { nth0 = 8; @@ -1968,30 +2198,36 @@ static void ggml_metal_encode_node( } }; + ggml_metal_kargs_mul_mv args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.r2 =*/ r2, + /*.r3 =*/ r3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { + src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || + src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -2040,6 +2276,9 @@ static void ggml_metal_encode_node( GGML_ASSERT(src1t == GGML_TYPE_F32); + GGML_ASSERT(ne03 == 1); + GGML_ASSERT(ne13 == 1); + // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel // ne20 = n_used_experts @@ -2060,12 +2299,12 @@ static void ggml_metal_encode_node( if ([device supportsFamily:MTLGPUFamilyApple7] && ne00 % 32 == 0 && ne00 >= 64 && dst_rows > dst_rows_min) { - // some Metal matrix data types require aligned pointers // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; default: break; } @@ -2074,6 +2313,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32 ].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; @@ -2096,27 +2336,30 @@ static void ggml_metal_encode_node( default: GGML_ABORT("MUL_MAT_ID not implemented"); } + ggml_metal_kargs_mul_mm_id args = { + /*.nei0 =*/ ne20, + /*.nei1 =*/ ne21, + /*.nbi1 =*/ nb21, + /*.ne00 =*/ ne00, + /*.ne02 =*/ ne02, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4]; [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; @@ -2143,6 +2386,13 @@ static void ggml_metal_encode_node( nth1 = 1; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + nth0 = 32; + nth1 = 1; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32].pipeline; + } break; case GGML_TYPE_Q4_0: { nth0 = 8; @@ -2268,30 +2518,34 @@ static void ggml_metal_encode_node( GGML_ASSERT(ne00 >= nth0*nth1); } + ggml_metal_kargs_mul_mv_id args = { + /*.nei0 =*/ ne20, + /*.nei1 =*/ ne21, + /*.nbi1 =*/ nb21, + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.nb1 =*/ nb1, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4]; const int64_t _ne1 = 1; const int tgz = dst_rows; @@ -2340,6 +2594,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16 ].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; @@ -2363,6 +2618,7 @@ static void ggml_metal_encode_node( default: GGML_ABORT("not implemented"); } + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -2386,20 +2642,28 @@ static void ggml_metal_encode_node( float eps; memcpy(&eps, dst->op_params, sizeof(float)); + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + int nth = 32; // SIMD width - while (nth < ne00/4 && nth < 1024) { + while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) { nth *= 2; } - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + nth = MIN(nth, ne00/4); + + ggml_metal_kargs_rms_norm args = { + /*.ne00 =*/ ne00, + /*.ne00_4 =*/ ne00/4, + /*.nb01 =*/ nb01, + /*.eps =*/ eps, + }; [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -2424,6 +2688,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2441,22 +2706,35 @@ static void ggml_metal_encode_node( } break; case GGML_OP_NORM: { + GGML_ASSERT(ne00 % 4 == 0); GGML_ASSERT(ggml_is_contiguous_1(src0)); float eps; memcpy(&eps, dst->op_params, sizeof(float)); - const int nth = MIN(256, ne00); - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + int nth = 32; // SIMD width + + while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) { + nth *= 2; + } + + nth = MIN(nth, ne00/4); + + ggml_metal_kargs_norm args = { + /*.ne00 =*/ ne00, + /*.ne00_4 =*/ ne00/4, + /*.nb01 =*/ nb01, + /*.eps =*/ eps, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -2506,45 +2784,51 @@ static void ggml_metal_encode_node( }; } + ggml_metal_kargs_rope args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.n_past =*/ n_past, + /*.n_dims =*/ n_dims, + /*.n_ctx_orig =*/ n_ctx_orig, + /*.freq_base =*/ freq_base, + /*.freq_scale =*/ freq_scale, + /*.ext_factor =*/ ext_factor, + /*.attn_factor =*/ attn_factor, + /*.beta_fast =*/ beta_fast, + /*.beta_slow =*/ beta_slow, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; if (id_src2 != nil) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; case GGML_OP_IM2COL: { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); @@ -2574,30 +2858,55 @@ static void ggml_metal_encode_node( const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - id pipeline = nil; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; + + const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup; switch (dst->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; + case GGML_TYPE_F32: { + pipeline = (is_gt_mttpt ? + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline + : + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline); + } break; + case GGML_TYPE_F16: { + pipeline = (is_gt_mttpt ? + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline + : + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline); + } break; default: GGML_ABORT("fatal error"); }; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof(int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof(int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12]; - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + if (is_gt_mttpt) { + [encoder setBytes:&N length:sizeof(int32_t) atIndex:13]; + [encoder setBytes:&KH length:sizeof(int32_t) atIndex:14]; + [encoder setBytes:&KW length:sizeof(int32_t) atIndex:15]; + + const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N); + + const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0); + + [encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; + } else { + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } } break; case GGML_OP_UPSCALE: { @@ -2610,6 +2919,7 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2644,6 +2954,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2680,6 +2991,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; @@ -2701,6 +3013,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2739,6 +3052,7 @@ static void ggml_metal_encode_node( default: GGML_ABORT("fatal error"); }; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2757,6 +3071,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2772,6 +3087,7 @@ static void ggml_metal_encode_node( GGML_ASSERT(ne11 % 32 == 0); GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == src2->type); GGML_ASSERT(ggml_are_same_shape (src1, src2)); @@ -2818,27 +3134,176 @@ static void ggml_metal_encode_node( bool use_vec_kernel = false; + // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0) + // for now avoiding mainly to keep the number of templates/kernels a bit lower if (ne01 >= 4 || (ne00%128 != 0)) { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + switch (src1->type) { + case GGML_TYPE_F16: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_BF16: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q4_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q4_1: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q5_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q5_1: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q8_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } } } else { use_vec_kernel = true; switch (ne00) { - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + case 128: + { + switch (src1->type) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } + } + } break; + case 256: + { + switch (src1->type) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } + } + } break; default: { GGML_LOG_ERROR("unsupported size: %lld\n", ne00); @@ -2848,40 +3313,41 @@ static void ggml_metal_encode_node( } } + ggml_metal_kargs_flash_attn_ext args = { + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne11 =*/ ne11, + /*.ne_12_2 =*/ ne12, + /*.ne_12_3 =*/ ne13, + /*.nb_12_1 =*/ nb11, + /*.nb_12_2 =*/ nb12, + /*.nb_12_3 =*/ nb13, + /*.nb31 =*/ nb31, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.scale =*/ scale, + /*.max_bias =*/ max_bias, + /*.m0 =*/ m0, + /*.m1 =*/ m1, + /*.n_head_log2 =*/ n_head_log2, + /*.logit_softcap =*/ logit_softcap, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; if (id_src3) { - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:4]; } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:4]; } - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; - [encoder setBytes:&scale length:sizeof( float) atIndex:23]; - [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; - [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:5]; if (!use_vec_kernel) { // half8x8 kernel @@ -2892,10 +3358,19 @@ static void ggml_metal_encode_node( GGML_ASSERT(nqptg % 8 == 0); GGML_ASSERT(ncpsg % 32 == 0); + // 2*(2*ncpsg + nqptg)*(nsg) + // ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float) + // + // 16*32*(nsg) + // the shared memory needed for the simdgroups to load the KV cache + // each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG + // +#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16)) + int64_t nsgmax = 2; while (true) { - const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsgmax); if (smem > device.maxThreadgroupMemoryLength) { break; } @@ -2906,16 +3381,15 @@ static void ggml_metal_encode_node( // simdgroups per threadgroup (a.k.a. warps) const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; - const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsg); - //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); + //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; - + [encoder setThreadgroupMemoryLength:smem atIndex:0]; +#undef FATTN_SMEM [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; } else { - // half1x4 kernel + // half4x4 kernel const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! @@ -2923,8 +3397,28 @@ static void ggml_metal_encode_node( GGML_ASSERT(nqptg % 1 == 0); GGML_ASSERT(ncpsg % 32 == 0); + // ne00 + 2*ncpsg*(nsg) + // for each query, we load it as f16 in shared memory (ne00) + // and store the soft_max values and the mask + // + // ne00*(nsg) + // each simdgroup has a full f16 head vector in shared mem to accumulate results + // +#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*ncpsg*(nsg)) + ne00*(nsg))*(sizeof(float)/2), 16)) + + int64_t nsgmax = 2; + + while (true) { + const size_t smem = FATTN_SMEM(nsgmax); + if (smem > device.maxThreadgroupMemoryLength) { + break; + } + nsgmax *= 2; + } + nsgmax /= 2; + // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); + const int64_t nsgt = MAX(2, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))); int64_t nsg = 1; while (nsg <= nsgt) { @@ -2932,12 +3426,12 @@ static void ggml_metal_encode_node( } nsg /= 2; - const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsg); - //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); + //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; - + [encoder setThreadgroupMemoryLength:smem atIndex:0]; +#undef FATTN_SMEM [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; } } break; @@ -2959,6 +3453,7 @@ static void ggml_metal_encode_node( switch (dstt) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_BF16].pipeline; break; case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; @@ -2976,31 +3471,102 @@ static void ggml_metal_encode_node( default: GGML_ABORT("not implemented"); }; } break; + case GGML_TYPE_BF16: + { + switch (dstt) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_F32].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; default: GGML_ABORT("not implemented"); } + ggml_metal_kargs_cpy args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_POOL_2D: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt); + + const int32_t * opts = dst->op_params; + enum ggml_op_pool op = opts[0]; + + id pipeline = nil; + switch (src0t) { + case GGML_TYPE_F32: { + switch(op) { + case GGML_OP_POOL_AVG: + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break; + case GGML_OP_POOL_MAX: + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + } + } break; + default: GGML_ASSERT(false && "not implemented"); + } + + const int32_t k0 = opts[1]; + const int32_t k1 = opts[2]; + const int32_t s0 = opts[3]; + const int32_t s1 = opts[4]; + const int32_t p0 = opts[5]; + const int32_t p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int64_t parallel_elements = N * OC * OH * OW; + const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements); + const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads; + + // TODO: add ggml_metal_kargs struct + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2]; + [encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3]; + [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4]; + [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5]; + [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6]; + [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7]; + [encoder setBytes:&IH length:sizeof(int64_t) atIndex:8]; + [encoder setBytes:&IW length:sizeof(int64_t) atIndex:9]; + [encoder setBytes:&OH length:sizeof(int64_t) atIndex:10]; + [encoder setBytes:&OW length:sizeof(int64_t) atIndex:11]; + [encoder setBytes:¶llel_elements length:sizeof(int64_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; + } break; default: { GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); @@ -3146,12 +3712,6 @@ static enum ggml_status ggml_metal_graph_compute( // backend interface -static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { - return "Metal"; - - UNUSED(buffer); -} - static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; @@ -3177,6 +3737,12 @@ static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { return ctx->all_data; } +static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + UNUSED(buffer); +} + static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); @@ -3206,11 +3772,10 @@ static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { - /* .get_name = */ ggml_backend_metal_buffer_get_name, /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, - /* .memset_tensor = */ NULL, + /* .memset_tensor = */ ggml_backend_metal_buffer_memset_tensor, /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, @@ -3331,6 +3896,29 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { return &ggml_backend_buffer_type_metal; } +static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { + return "Metal_Mapped"; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) { + static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = { + /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_from_ptr_type_get_name, + /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_metal_buffer_type_is_host, + }, + /* .device = */ &g_ggml_backend_metal_device, + /* .context = */ NULL, + }; + + return &ggml_backend_buffer_from_ptr_type_metal; +} + // TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); @@ -3407,7 +3995,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz } } - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); + return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); } // backend @@ -3428,12 +4016,6 @@ static void ggml_backend_metal_free(ggml_backend_t backend) { free(backend); } -static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_metal_buffer_type(); - - UNUSED(backend); -} - static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return ggml_metal_graph_compute(backend, cgraph); } @@ -3500,7 +4082,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, - /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -3510,9 +4091,6 @@ static struct ggml_backend_i ggml_backend_metal_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -3607,7 +4185,7 @@ static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t } static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; GGML_UNUSED(dev); } @@ -3730,7 +4308,7 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back } } - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); + return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); } static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { @@ -3740,7 +4318,8 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const } static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name; + return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name || + buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name; UNUSED(dev); } diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal similarity index 55% rename from ggml/src/ggml-metal.metal rename to ggml/src/ggml-metal/ggml-metal.metal index 2b20003239..971f5054bc 100644 --- a/ggml/src/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -1,6 +1,12 @@ #define GGML_COMMON_DECL_METAL #define GGML_COMMON_IMPL_METAL -#include "ggml-common.h" +#if defined(GGML_METAL_EMBED_LIBRARY) +__embed_ggml-common.h__ +#else +// TODO: this should not be a relative path, but can't figure out how to set Metal include paths in Package.swift +#include "../ggml-common.h" +#endif +#include "ggml-metal-impl.h" #include @@ -12,6 +18,477 @@ using namespace metal; #define N_SIMDWIDTH 32 // assuming SIMD group size is 32 +// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf +// +// cmd: +// .../usr/bin/metal -dM -E -c ggml/src/ggml-metal/ggml-metal.metal +// .../usr/bin/metal -dM -E -c -target air64-apple-ios14.0 ggml/src/ggml-metal/ggml-metal.metal +// +#if __METAL_VERSION__ < 310 && defined(GGML_METAL_USE_BF16) +#undef GGML_METAL_USE_BF16 +#endif + +#if defined(GGML_METAL_USE_BF16) +typedef matrix bfloat4x4; +#endif + +constexpr constant static float kvalues_iq4nl_f[16] = { + -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f +}; + +// NOTE: this is not dequantizing - we are simply fitting the template +template +void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} + +template +void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} + +#if defined(GGML_METAL_USE_BF16) +template +void dequantize_bf16(device const bfloat4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} +#endif + +template +void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 1); + const float d1 = il ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float md = -8.h * xb->d; + const ushort mask0 = il ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + reg_f[i/2][2*(i%2) + 0] = d1 * (qs[i] & mask0) + md; + reg_f[i/2][2*(i%2) + 1] = d2 * (qs[i] & mask1) + md; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 2); + const float d1 = il ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float m = xb->m; + const ushort mask0 = il ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + reg_f[i/2][2*(i%2) + 0] = ((qs[i] & mask0) * d1) + m; + reg_f[i/2][2*(i%2) + 1] = ((qs[i] & mask1) * d2) + m; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 3); + const float d = xb->d; + const float md = -16.h * xb->d; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg_f[i/2][2*(i%2) + 0] = d * x0 + md; + reg_f[i/2][2*(i%2) + 1] = d * x1 + md; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 4); + const float d = xb->d; + const float m = xb->m; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg_f[i/2][2*(i%2) + 0] = d * x0 + m; + reg_f[i/2][2*(i%2) + 1] = d * x1 + m; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) { + device const int8_t * qs = ((device const int8_t *)xb->qs); + const half d = xb->d; + + float4x4 reg_f; + + for (int i = 0; i < 16; i++) { + reg_f[i/4][i%4] = (qs[i + 16*il] * d); + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) { + const float d = xb->d; + const float min = xb->dmin; + device const uint8_t * q = (device const uint8_t *)xb->qs; + float dl, ml; + uint8_t sc = xb->scales[il]; + + q = q + 32*(il/8) + 16*(il&1); + il = (il/2)%4; + + half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); + uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - ml; + } +} + +template +void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) { + const half d_all = xb->d; + device const uint8_t * q = (device const uint8_t *)xb->qs; + device const uint8_t * h = (device const uint8_t *)xb->hmask; + device const int8_t * scales = (device const int8_t *)xb->scales; + + q = q + 32 * (il/8) + 16 * (il&1); + h = h + 16 * (il&1); + uint8_t m = 1 << (il/2); + uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \ + ((il/4)>0 ? 12 : 3); + uint16_t kmask2 = il/8 ? 0xF0 : 0x0F; + uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; + int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) + : (scale_2&kmask2) | ((scale_1&kmask1) << 4); + float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); + const float ml = 4.f * dl; + + il = (il/2) & 3; + const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); + const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + dl *= coef; + + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); + } +} + +static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { + return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)} + : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))}; +} + +template +void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { + device const uchar * q = xb->qs; + + short is = (il/4) * 2; + q = q + (il/4) * 32 + 16 * (il&1); + il = il & 3; + const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); + const float d = il < 2 ? xb->d : xb->d / 16.h; + const float min = xb->dmin; + const float dl = d * sc[0]; + const float ml = min * sc[1]; + + const ushort mask = il<2 ? 0x0F : 0xF0; + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - ml; + } +} + +template +void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) { + device const uint8_t * q = xb->qs; + device const uint8_t * qh = xb->qh; + + short is = (il/4) * 2; + q = q + 32 * (il/4) + 16 * (il&1); + qh = qh + 16 * (il&1); + uint8_t ul = 1 << (il/2); + il = il & 3; + const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); + const float d = il < 2 ? xb->d : xb->d / 16.f; + const float min = xb->dmin; + const float dl = d * sc[0]; + const float ml = min * sc[1]; + + const ushort mask = il<2 ? 0x0F : 0xF0; + const float qh_val = il<2 ? 16.f : 256.f; + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; + } +} + +template +void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) { + const half d_all = xb->d; + device const uint8_t * ql = (device const uint8_t *)xb->ql; + device const uint8_t * qh = (device const uint8_t *)xb->qh; + device const int8_t * scales = (device const int8_t *)xb->scales; + + ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); + qh = qh + 32*(il/8) + 16*(il&1); + float sc = scales[(il%2) + 2 * ((il/2))]; + il = (il/2) & 3; + + const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; + const float coef = il>1 ? 1.f/16.f : 1.f; + const float ml = d_all * sc * 32.f; + const float dl = d_all * sc * coef; + for (int i = 0; i < 16; ++i) { + const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) + : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); + reg[i/4][i%4] = dl * q - ml; + } +} + +template +void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. + device const uint16_t * q2 = xb->qs + 4*ib32; + const uint32_t aux32_g = q2[0] | (q2[1] << 16); + const uint32_t aux32_s = q2[2] | (q2[3] << 16); + thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; + const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); + signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + +template +void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint16_t * q2 = xb->qs + 4*ib32; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); + uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); + signs = ksigns_iq2xs[q2[2*il+1] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + +template +void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * q3 = xb->qs + 8*ib32; + device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; + constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); + constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); + uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } + grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); + grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); + signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } +} + +template +void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 8*ib32; + device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); + constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); + reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); + } + grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); + reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); + } +} + +template +void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * signs = qs + QK_K/8; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); + for (int i = 0; i < 8; ++i) { + reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); + reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); + } +} + +template +void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + const float d = xb->d; + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint16_t * qh = xb->qh; + const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); + const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); + const uint16_t h = qh[ib32] >> 6*il; + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * (grid1[i] & 0xf) + ml; + reg[1][i] = dl * (grid1[i] >> 4) + ml; + reg[2][i] = dl * (grid2[i] & 0xf) + ml; + reg[3][i] = dl * (grid2[i] >> 4) + ml; + } +} + +template +void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + device const uint16_t * sc = (device const uint16_t *)xb->scales; + + iq1m_scale_t scale; + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + const float d = scale.f16; + + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * qh = xb->qh + 2*ib32 + il; + + const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); + const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); + const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * (grid1[i] & 0xf) + ml1; + reg[1][i] = dl * (grid1[i] >> 4) + ml1; + reg[2][i] = dl * (grid2[i] & 0xf) + ml2; + reg[3][i] = dl * (grid2[i] >> 4) + ml2; + } +} + +template +void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { + device const uint16_t * q4 = (device const uint16_t *)xb->qs; + const float d = xb->d; + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + +template +void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; + const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)xb->d * (ls - 32); + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + enum ggml_sort_order { GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC, @@ -21,240 +498,131 @@ enum ggml_sort_order { // pros: works for non-contiguous tensors, supports broadcast across all dims // cons: not very efficient kernel void kernel_add( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) + *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) + *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_sub( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) - *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) - *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_mul( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) * *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_div( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) / *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10)); } } template kernel void kernel_repeat( + constant ggml_metal_kargs_repeat & args, device const char * src0, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i3 = tgpig.z; + const int i2 = tgpig.y; + const int i1 = tgpig.x; - const int64_t i03 = i3 % ne03; - const int64_t i02 = i2 % ne02; - const int64_t i01 = i1 % ne01; + const int i03 = i3%args.ne03; + const int i02 = i2%args.ne02; + const int i01 = i1%args.ne01; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device char * dst_ptr = dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i00 = i0 % ne00; - *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i00 = i0%args.ne00; + *((device T *)(dst_ptr + i0*args.nb0)) = *((device T *)(src0_ptr + i00*args.nb00)); } } @@ -268,38 +636,42 @@ template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] + src1[tpig % nb]; } kernel void kernel_sub_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] - src1[tpig % nb]; } kernel void kernel_mul_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] * src1[tpig % nb]; } kernel void kernel_div_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] / src1[tpig % nb]; } @@ -410,6 +782,14 @@ kernel void kernel_silu_4( dst[tpig] = x / (1.0f + exp(-x)); } +kernel void kernel_elu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + dst[tpig] = (x > 0.0f) ? x : (exp(x) - 1.0f); +} + kernel void kernel_sqr( device const float * src0, device float * dst, @@ -777,10 +1157,10 @@ kernel void kernel_ssm_conv_f32( const int64_t i3 = tgpig.z; const int64_t nc = ne10; - const int64_t ncs = ne00; - const int64_t nr = ne01; - const int64_t n_t = ne1; - const int64_t n_s = ne2; + //const int64_t ncs = ne00; + //const int64_t nr = ne01; + //const int64_t n_t = ne1; + //const int64_t n_s = ne2; device const float * s = (device const float *) ((device const char *) src0 + ir*nb01 + i2*nb00 + i3*nb02); device const float * c = (device const float *) ((device const char *) src1 + ir*nb11); @@ -834,9 +1214,9 @@ kernel void kernel_ssm_scan_f32( const int64_t i3 = tgpig.y; const int64_t nc = d_state; - const int64_t nr = d_inner; + //const int64_t nr = d_inner; const int64_t n_t = n_seq_tokens; - const int64_t n_s = n_seqs; + //const int64_t n_s = n_seqs; for (int64_t i2 = 0; i2 < n_t; ++i2) { device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb01 + i3*nb02); @@ -869,102 +1249,112 @@ kernel void kernel_ssm_scan_f32( } kernel void kernel_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * sum [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float * x = (device const float *) ((device const char *) src0 + tgpig*nb01); - // MEAN - // parallel sum - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - sum[tpitg] += x[i00]; + constant ggml_metal_kargs_norm & args, + device const char * src0, + device char * dst, + threadgroup float * shmem_f32 [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + ushort tpitg[[thread_position_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort ntg[[threads_per_threadgroup]]) { + if (sgitg == 0) { + shmem_f32[tiisg] = 0.0f; } - // reduce - threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float mean = sum[0] / ne00; - // recenter and VARIANCE + device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01); + + float4 sumf4(0.0f); + + float sumf = 0.0f; + + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + sumf4 += x[i00]; + } + sumf = sumf4[0] + sumf4[1] + sumf4[2] + sumf4[3]; + sumf = simd_sum(sumf); + threadgroup_barrier(mem_flags::mem_threadgroup); - device float * y = dst + tgpig*ne00; - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float mean = sumf/args.ne00; + + device float4 * y = (device float4 *) dst + tgpig*args.ne00_4; + + sumf = 0.0f; + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { y[i00] = x[i00] - mean; - sum[tpitg] += y[i00] * y[i00]; + sumf += dot(y[i00], y[i00]); } + sumf = simd_sum(sumf); - // reduce threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float variance = sum[0] / ne00; - const float scale = 1.0f/sqrt(variance + eps); - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float variance = sumf/args.ne00; + + const float scale = 1.0f/sqrt(variance + args.eps); + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { y[i00] = y[i00] * scale; } } kernel void kernel_rms_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01); + constant ggml_metal_kargs_rms_norm & args, + device const char * src0, + device char * dst, + threadgroup float * shmem_f32 [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + ushort tpitg[[thread_position_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort ntg[[threads_per_threadgroup]]) { + if (sgitg == 0) { + shmem_f32[tiisg] = 0.0f; + } - float4 sumf = 0; - float all_sum = 0; + device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01); + + float sumf = 0.0f; // parallel sum - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - sumf += x[i00] * x[i00]; + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + sumf += dot(x[i00], x[i00]); } - all_sum = sumf[0] + sumf[1] + sumf[2] + sumf[3]; - all_sum = simd_sum(all_sum); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } + sumf = simd_sum(sumf); - threadgroup_barrier(mem_flags::mem_threadgroup); + threadgroup_barrier(mem_flags::mem_threadgroup); - if (tiisg == 0) { - buf[sgitg] = all_sum; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - all_sum = buf[tiisg]; - all_sum = simd_sum(all_sum); + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; } - const float mean = all_sum/ne00; - const float scale = 1.0f/sqrt(mean + eps); + threadgroup_barrier(mem_flags::mem_threadgroup); - device float4 * y = (device float4 *) (dst + tgpig*ne00); - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float mean = sumf/args.ne00; + const float scale = 1.0f/sqrt(mean + args.eps); + + device float4 * y = (device float4 *) dst + tgpig*args.ne00_4; + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { y[i00] = x[i00] * scale; } } @@ -1064,17 +1454,18 @@ kernel void kernel_group_norm( inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2); + device const uint16_t * qs = ((device const uint16_t *) qb_curr + 1 + il/2); - for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) - + yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0) - + yl[i + 9] * (qs[i / 2] & 0xF000); + for (int i = 0; i < 8; i += 2) { + acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); + acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); + acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); + acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); } - return d * (sumy * -8.f + acc[0] + acc[1]); + + return d * (sumy * -8.f + acc[0] + acc[1] + acc[2] + acc[3]); } // function for calculate inner product between half a q4_1 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1085,17 +1476,18 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre float d = qb_curr->d; float m = qb_curr->m; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2); + device const uint16_t * qs = ((device const uint16_t *) qb_curr + 2 + il/2); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) - + yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0) - + yl[i + 9] * (qs[i / 2] & 0xF000); + acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); + acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); + acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); + acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); } - return d * (acc[0] + acc[1]) + sumy * m; + + return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; } // function for calculate inner product between half a q5_0 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1105,18 +1497,19 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre inline float block_q_n_dot_y(device const block_q5_0 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; device const uint16_t * qs = ((device const uint16_t *)qb_curr + 3 + il/2); const uint32_t qh = *((device const uint32_t *)qb_curr->qh); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) - + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) - + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); + acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); + acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); } - return d * (sumy * -16.f + acc[0] + acc[1]); + + return d * (sumy * -16.f + acc[0] + acc[1] + acc[2] + acc[3]); } // function for calculate inner product between half a q5_1 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1127,18 +1520,19 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre float d = qb_curr->d; float m = qb_curr->m; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; device const uint16_t * qs = ((device const uint16_t *)qb_curr + 4 + il/2); const uint32_t qh = *((device const uint32_t *)qb_curr->qh); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) - + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) - + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); + acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); + acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); } - return d * (acc[0] + acc[1]) + sumy * m; + + return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; } // putting them in the kernel cause a significant performance penalty @@ -1148,23 +1542,17 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre // quantizations where the block size is 32. It also does not // guard against the number of rows not being divisible by // N_DST, so this is another explicit assumption of the implementation. -template +template void mul_vec_q_n_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, uint tiisg, uint sgitg) { - const int nb = ne00/QK4_0; + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { + const int nb = args.ne00/QK4_0; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -1172,329 +1560,264 @@ void mul_vec_q_n_f32_impl( const int first_row = (r0 * nsg + sgitg) * nr; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + //const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q_type * x = (device const block_q_type *) src0 + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + //device const block_q_type * x = (device const block_q_type *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); + + // pointers to src0 rows + device const block_q_type * ax[nr]; + for (int row = 0; row < nr; ++row) { + const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + + ax[row] = (device const block_q_type *) ((device char *) src0 + offset0); + } float yl[16]; // src1 vector cache float sumf[nr] = {0.f}; - const int ix = (tiisg/2); - const int il = (tiisg%2)*8; + const short ix = (tiisg/2); + const short il = (tiisg%2)*8; - device const float * yb = y + ix * QK4_0 + il; + device const float * yb = y + ix*QK4_0 + il; // each thread in a SIMD group deals with half a block. for (int ib = ix; ib < nb; ib += nw/2) { - float sumy = 0; - for (int i = 0; i < 8; i += 2) { - sumy += yb[i] + yb[i+1]; - yl[i+0] = yb[i+ 0]; - yl[i+1] = yb[i+ 1]/256.f; + float sumy[2] = { 0.f, 0.f }; - sumy += yb[i+16] + yb[i+17]; - yl[i+8] = yb[i+16]/16.f; - yl[i+9] = yb[i+17]/4096.f; +#pragma unroll + for (int i = 0; i < 8; i += 2) { + sumy[0] += yb[i + 0] + yb[i + 1]; + yl[i + 0] = yb[i + 0]; + yl[i + 1] = yb[i + 1]/256.f; + + sumy[1] += yb[i + 16] + yb[i + 17]; + yl[i + 8] = yb[i + 16]/16.f; + yl[i + 9] = yb[i + 17]/4096.f; } +#pragma unroll for (int row = 0; row < nr; row++) { - sumf[row] += block_q_n_dot_y(x+ib+row*nb, sumy, yl, il); + sumf[row] += block_q_n_dot_y(ax[row] + ib, sumy[0] + sumy[1], yl, il); } yb += QK4_0 * 16; } + device float * dst_f32 = (device float *) dst + im*args.ne0*args.ne1 + r1*args.ne0; + for (int row = 0; row < nr; ++row) { const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[im*ne0*ne1 + r1*ne0 + first_row + row] = tot; + + if (tiisg == 0 && first_row + row < args.ne01) { + dst_f32[first_row + row] = tot; } } } kernel void kernel_mul_mv_q4_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q4_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q5_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q5_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } - #define NB_Q8_0 8 +template void kernel_mul_mv_q8_0_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const int nr = N_DST; const int nsg = N_SIMDGROUP; const int nw = N_SIMDWIDTH; - const int nb = ne00/QK8_0; + const int nb = args.ne00/QK8_0; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * nsg + sgitg) * nr; + const int first_row = (r0*nsg + sgitg)*nr; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + //const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + //device const block_q8_0 * x = (device const block_q8_0 *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); + + // pointers to src0 rows + device const block_q8_0 * ax[nr]; + for (int row = 0; row < nr; ++row) { + const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + + ax[row] = (device const block_q8_0 *) ((device char *) src0 + offset0); + } float yl[NB_Q8_0]; - float sumf[nr]={0.f}; + float sumf[nr] = { 0.f }; - const int ix = tiisg/4; - const int il = tiisg%4; + const short ix = tiisg/4; + const short il = tiisg%4; - device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; + device const float * yb = y + ix*QK8_0 + il*NB_Q8_0; // each thread in a SIMD group deals with NB_Q8_0 quants at a time for (int ib = ix; ib < nb; ib += nw/4) { - for (int i = 0; i < NB_Q8_0; ++i) { + for (short i = 0; i < NB_Q8_0; ++i) { yl[i] = yb[i]; } for (int row = 0; row < nr; row++) { - device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il; + device const int8_t * qs = ax[row][ib].qs + il*NB_Q8_0; float sumq = 0.f; - for (int iq = 0; iq < NB_Q8_0; ++iq) { + for (short iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } - sumf[row] += sumq*x[ib+row*nb].d; + sumf[row] += sumq*ax[row][ib].d; } - yb += NB_Q8_0 * nw; + yb += nw*NB_Q8_0; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < nr; ++row) { const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; + + if (tiisg == 0 && first_row + row < args.ne01) { + dst_f32[first_row + row] = tot; } } } [[host_name("kernel_mul_mv_q8_0_f32")]] kernel void kernel_mul_mv_q8_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + kernel_mul_mv_q8_0_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } #define N_MV_T_T 4 -template +template void kernel_mul_mv_impl( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg) { - const int64_t r0 = tgpig.x; - const int64_t rb = tgpig.y*N_MV_T_T; - const int64_t im = tgpig.z; + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig, + ushort tiisg) { + const int r0 = tgpig.x; + const int rb = tgpig.y*N_MV_T_T; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; device const T0 * x = (device const T0 *) (src0 + offset0); - if (ne00 < 128) { + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1; + + if (args.ne00 < 128) { for (int row = 0; row < N_MV_T_T; ++row) { int r1 = rb + row; - if (r1 >= ne11) { + if (r1 >= args.ne11) { break; } - device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12); + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const T1 * y = (device const T1 *) (src1 + offset1); float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { + for (int i = tiisg; i < args.ne00; i += 32) { sumf += (T0) x[i] * (T1) y[i]; } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } else { device const T04 * x4 = (device const T04 *) x; for (int row = 0; row < N_MV_T_T; ++row) { int r1 = rb + row; - if (r1 >= ne11) { + if (r1 >= args.ne11) { break; } - device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12); + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const T1 * y = (device const T1 *) (src1 + offset1); device const T14 * y4 = (device const T14 *) y; float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], (float4) y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + for (int i = 4*(args.ne00/4); i < args.ne00; ++i) all_sum += (float) (x[i] * y[i]); + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } @@ -1502,47 +1825,17 @@ void kernel_mul_mv_impl( template kernel void kernel_mul_mv( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { - kernel_mul_mv_impl( + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { + kernel_mul_mv_impl( + args, src0, src1, dst, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - ne1, - r2, - r3, tgpig, tiisg); } @@ -1552,65 +1845,57 @@ typedef decltype(kernel_mul_mv) mul_mv_t; template [[host_name("kernel_mul_mv_f32_f32")]] kernel mul_mv_t kernel_mul_mv; template [[host_name("kernel_mul_mv_f16_f32")]] kernel mul_mv_t kernel_mul_mv; template [[host_name("kernel_mul_mv_f16_f16")]] kernel mul_mv_t kernel_mul_mv; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32")]] kernel mul_mv_t kernel_mul_mv; +template [[host_name("kernel_mul_mv_bf16_bf16")]] kernel mul_mv_t kernel_mul_mv; +#endif template kernel void kernel_mul_mv_1row( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; device const T * x = (device const T *) (src0 + offset0); - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float * y = (device const float *) (src1 + offset1); + + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { + if (args.ne00 < 128) { + for (int i = tiisg; i < args.ne00; i += 32) { sumf += (float) x[i] * (float) y[i]; } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[r0] = all_sum; } } else { device const T4 * x4 = (device const T4 *) x; device const float4 * y4 = (device const float4 *) y; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + for (int i = 4*(args.ne00/4); i < args.ne00; ++i) all_sum += (float) (x[i] * y[i]); + dst_f32[r0] = all_sum; } } } @@ -1618,54 +1903,46 @@ kernel void kernel_mul_mv_1row( typedef decltype(kernel_mul_mv_1row) mul_mv_1row_t; template [[host_name("kernel_mul_mv_f16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; +#endif // Assumes row size (ne00) is a multiple of 4 template kernel void kernel_mul_mv_l4( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { - const int nrows = ne11; - const int64_t r0 = tgpig.x; - const int64_t im = tgpig.z; + const int nrows = args.ne11; + const int r0 = tgpig.x; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; device const T4 * x4 = (device const T4 *) (src0 + offset0); + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1; + for (int r1 = 0; r1 < nrows; ++r1) { - device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12); + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const float4 * y4 = (device const float4 *) (src1 + offset1); float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } @@ -1673,6 +1950,9 @@ kernel void kernel_mul_mv_l4( typedef decltype(kernel_mul_mv_l4) mul_mv_l4_t; template [[host_name("kernel_mul_mv_f16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; +#endif static float rope_yarn_ramp(const float low, const float high, const int i0) { const float y = (i0 / 2 - low) / max(0.001f, high - low); @@ -1682,7 +1962,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float theta_extrap, float freq_scale, float corr_dims[2], int i0, float ext_factor, float mscale, thread float * cos_theta, thread float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; @@ -1714,65 +1994,41 @@ static void rope_yarn_corr_dims( template kernel void kernel_rope_norm( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); - device const int32_t * pos = src1; + device const int32_t * pos = (device const int32_t *) src1; const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; + const float inv_ndims = -1.f/args.n_dims; float cos_theta; float sin_theta; - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < args.n_dims) { + const int ic = i0/2; - const float theta = theta_base * pow(freq_base, inv_ndims*i0); + const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); const float x0 = src[0]; const float x1 = src[1]; @@ -1780,8 +2036,8 @@ kernel void kernel_rope_norm( dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; @@ -1791,74 +2047,50 @@ kernel void kernel_rope_norm( template kernel void kernel_rope_neox( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); - device const int32_t * pos = src1; + device const int32_t * pos = (device const int32_t *) src1; const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; + const float inv_ndims = -1.f/args.n_dims; float cos_theta; float sin_theta; - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < args.n_dims) { + const int ic = i0/2; - const float theta = theta_base * pow(freq_base, inv_ndims*i0); + const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0); const float x0 = src[0]; - const float x1 = src[n_dims/2]; + const float x1 = src[args.n_dims/2]; - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta; } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; @@ -1913,26 +2145,119 @@ kernel void kernel_im2col( uint3 tgpg[[threadgroups_per_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { - const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0; - const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1; +// const int64_t IC = tgpg[0]; + const int64_t OH = tgpg[1]; + const int64_t OW = tgpg[2]; - const int32_t offset_dst = - (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + - (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); +// const int64_t N = ntg[0]; + const int64_t KH = ntg[1]; + const int64_t KW = ntg[2]; + + const int64_t in = tpitg[0]; + const int64_t ikh = tpitg[1]; + const int64_t ikw = tpitg[2]; + + const int64_t iic = tgpig[0]; + const int64_t ioh = tgpig[1]; + const int64_t iow = tgpig[2]; + + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + const int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*CHW + (iic*(KH*KW) + ikh*KW + ikw); device T * pdst = (device T *) (dst); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { pdst[offset_dst] = 0.0f; } else { - const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - pdst[offset_dst] = x[offset_src + iih * IW + iiw]; + const int64_t offset_src = in*ofs0 + iic*ofs1 + iih*IW + iiw; + pdst[offset_dst] = x[offset_src]; } } template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; +typedef void (im2col_ext_t)( + device const float * x, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + constant int32_t & N, + constant int32_t & KH, + constant int32_t & KW, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template +kernel void kernel_im2col_ext( + device const float * x, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + constant int32_t & N, + constant int32_t & KH, + constant int32_t & KW, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1] + const int64_t KHW = KH * KW; // KHW == ntg[1] * ntg[2], KW == ntg[2] + + const int64_t d = tgpig[0] / CHW; + const int64_t chw = tgpig[0] % CHW; + const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1) + const int64_t HW = tgpig[0] % KHW; + + const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0]; + if (tpitg_0 >= N) { + return; + } + + const int64_t tpitg_1 = HW / KW; + const int64_t tpitg_2 = HW % KW; + + const int64_t iiw = tgpig[2] * s0 + tpitg_2 * d0 - p0; + const int64_t iih = tgpig[1] * s1 + tpitg_1 * d1 - p1; + + const int64_t offset_dst = + (tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + + (tgpig_0 * KHW + tpitg_1 * KW + tpitg_2); + + device T * pdst = (device T *) (dst); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + pdst[offset_dst] = 0.0f; + } else { + const int64_t offset_src = tpitg_0 * ofs0 + tgpig_0 * ofs1; + pdst[offset_dst] = x[offset_src + iih * IW + iiw]; + } +} + +template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext; +template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext; + kernel void kernel_upscale_f32( device const char * src0, device char * dst, @@ -2148,252 +2473,281 @@ kernel void kernel_leaky_relu_f32( dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope; } -typedef void (flash_attn_ext_f16_t)( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]); - // ref: https://arxiv.org/pdf/2307.08691.pdf -template // head size, queries per threadgroup, cache items per threadgroup -kernel void kernel_flash_attn_ext_f16( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { +template< + typename q_t, // query types in shared memory + typename q4_t, + typename q8x8_t, + typename k_t, // key types in shared memory + typename k4x4_t, + typename k8x8_t, + typename v_t, // value types in shared memory + typename v4x4_t, + typename v8x8_t, + typename qk_t, // Q*K types + typename qk8x8_t, + typename s_t, // soft-max types + typename s8x8_t, + typename o_t, // attention accumulation types + typename o4_t, + typename o8x8_t, + typename kd4x4_t, // key type in device memory + short nl_k, + void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), + typename vd4x4_t, // key type in device memory + short nl_v, + void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), + short D, // head size + short Q = 8, // queries per threadgroup + short KV = 8, // key/value processed per each simdgroup + short C = 32> // cache items per threadgroup +kernel void kernel_flash_attn_ext( + constant ggml_metal_kargs_flash_attn_ext & args, + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device char * dst, + threadgroup half * shmem_f16 [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const short nsg = ntg.y; // number of simdgroups - const short iq3 = tgpig[2]; - const short iq2 = tgpig[1]; - const short iq1 = tgpig[0]*Q; + const int iq3 = tgpig[2]; + const int iq2 = tgpig[1]; + const int iq1 = tgpig[0]*Q; - const short D4 = D/4; - const short D8 = D/8; - //const short Q8 = Q/8; - const short NW = N_SIMDWIDTH; - const short SH = (C + Q); // shared memory per simdgroup in (half) + const short D4 = D/4; + const short D8 = D/8; + const short D16 = D/16; + const short NW = N_SIMDWIDTH; + const short SH = (2*C + Q); // shared memory per simdgroup (s_t == float) - const short T = D + 2*nsg*SH; // shared memory size per query in (half) - const short TF = T/2; // shared memory size per query in (float) - const short T4 = T/4; // shared memory size per query in (half4) + const short TS = nsg*SH; // shared memory size per query in (s_t == float) + const short T = D + 2*TS; // shared memory size per query in (half) - threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data - threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 - threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix + threadgroup q_t * sq = (threadgroup q_t *) (shmem_f16 + 0*D); // holds the query data + threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 + 0*D); // same as above but in q4_t + threadgroup o_t * so = (threadgroup o_t *) (shmem_f16 + 0*D); // reuse query data for accumulation + threadgroup o4_t * so4 = (threadgroup o4_t *) (shmem_f16 + 0*D); // same as above but in o4_t + threadgroup s_t * ss = (threadgroup s_t *) (shmem_f16 + 2*sgitg*SH + Q*D); // scratch buffer for attention, mask and diagonal matrix + + threadgroup k_t * sk = (threadgroup k_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // scratch buffer to load K in shared memory + threadgroup k4x4_t * sk4x4 = (threadgroup k4x4_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // same as above but in k4x4_t + + threadgroup v_t * sv = (threadgroup v_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // scratch buffer to load V in shared memory + threadgroup v4x4_t * sv4x4 = (threadgroup v4x4_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // same as above but in v4x4_t // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - simdgroup_half8x8 lo[D8]; + o8x8_t lo[D8]; // load heads from Q to shared memory for (short j = sgitg; j < Q; j += nsg) { - device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03)); + device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*args.nb01 + iq2*args.nb02 + iq3*args.nb03)); for (short i = tiisg; i < D4; i += NW) { - if (iq1 + j < ne01) { - sq4[j*T4 + i] = (half4) q4[i]; + if (iq1 + j < args.ne01) { + sq4[j*D4 + i] = (q4_t) q4[i]; } else { - sq4[j*T4 + i] = 0.0h; + sq4[j*D4 + i] = (q4_t) 0.0f; } } } // zero out lo for (short i = 0; i < D8; ++i) { - lo[i] = make_filled_simdgroup_matrix(0.0h); + lo[i] = make_filled_simdgroup_matrix((o_t) 0.0f); } // zero out shared memory SH for (short j = 0; j < Q; ++j) { for (short i = tiisg; i < SH; i += NW) { - ss[j*TF + i] = 0.0f; + ss[j*TS + i] = 0.0f; } } threadgroup_barrier(mem_flags::mem_threadgroup); { - float S[Q] = { [0 ... Q-1] = 0.0h }; - float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 }; + half S[Q] = { [0 ... Q-1] = 0.0f }; + half M[Q] = { [0 ... Q-1] = -__FLT16_MAX__/2 }; - // assume K and V are same shape - const short ne22 = ne12; - const short ne23 = ne13; + // thread indices inside the simdgroup + // TODO: see if we can utilize quad-group functions for better performance + // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (6.9.3) + const short tx = tiisg%4; + const short ty = tiisg/4; - // broadcast - const short rk2 = ne02/ne12; - const short rk3 = ne03/ne13; + // broadcast kv + //const short rk2 = args.ne02/args.ne12; + //const short rk3 = args.ne03/args.ne13; - const short rv2 = ne02/ne22; - const short rv3 = ne03/ne23; - - // k indices - const short ik2 = iq2/rk2; - const short ik3 = iq3/rk3; - - // v indices - const short iv2 = iq2/rv2; - const short iv3 = iq3/rv3; + const short ikv2 = iq2/(args.ne02/args.ne_12_2); + const short ikv3 = iq3/(args.ne03/args.ne_12_3); // load the queries from shared memory into local memory - simdgroup_half8x8 mq[D8]; + q8x8_t mq[D8]; for (short i = 0; i < D8; ++i) { - simdgroup_load(mq[i], sq + i*8, T); + simdgroup_load(mq[i], sq + i*8, D); } - // pointer to the mask - device const half * mp = (device const half *) (mask + iq1*nb31); + const bool has_mask = mask != q; - float slope = 1.0f; + half slope = 1.0f; // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = iq2; + if (args.max_bias > 0.0f) { + const short h = iq2; - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + const half base = h < args.n_head_log2 ? args.m0 : args.m1; + const short exph = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1; slope = pow(base, exph); } // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + for (int ic0 = 0; ic0 < args.ne11; ic0 += C*nsg) { const int ic = ic0 + C*sgitg; - if (ic >= ne11) { + if (ic >= args.ne11) { break; } + if (has_mask) { + // used to detect blocks full of -INF + half smax = -INFINITY; + + // load the mask in shared memory + #pragma unroll(Q) + for (short j = 0; j < Q; ++j) { + device const half * pm = (device const half *) ((device const char *) mask + (iq1 + j)*args.nb31); + + const half m = pm[ic + tiisg]; + + ss[j*TS + C + tiisg] = m; + smax = max(smax, m); + } + + smax = simd_max(smax); + + if (smax == -INFINITY) { + continue; + } + } + // Q*K^T { for (short cc = 0; cc < C/8; ++cc) { - simdgroup_float8x8 mqk = make_filled_simdgroup_matrix(0.h); + qk8x8_t mqk = make_filled_simdgroup_matrix((qk_t) 0.0f); - device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13)); + // this is compile-time check, so it does not have runtime overhead + if (is_same::value) { + // we can read directly from global memory + device const k_t * pk = (device const k_t *) ((device const char *) k + ((ic + 8*cc)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); - for (short i = 0; i < D8; ++i) { - simdgroup_half8x8 mk; - simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose + #pragma unroll(D8) + for (short i = 0; i < D8; ++i) { + k8x8_t mk; + simdgroup_load(mk, pk + i*8, args.nb_12_1/sizeof(k_t), 0, true); // transpose // TODO: use ne10 - simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); + simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); + } + } else { + for (short ii = 0; ii < D16; ii += 4) { + device const kd4x4_t * pk4x4 = (device const kd4x4_t *) ((device const char *) k + ((ic + 8*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); + + if (D16%4 == 0) { + // the head is evenly divisible by 4*16 = 64, so no need for bound checks + { + k4x4_t tmp; + deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); + sk4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + #pragma unroll(4) + for (short k = 0; k < 4; ++k) { + k8x8_t mk; + + simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); + + simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); + } + } else { + if (ii + tx < D16) { + k4x4_t tmp; + deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); + sk4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + for (short k = 0; k < 4 && ii + k < D16; ++k) { + k8x8_t mk; + + simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); + + simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); + } + } + } } - simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + // cast qk_t -> s_t + //s8x8_t mqks(1.0f); + //simdgroup_multiply(mqks, mqk, mqks); + //simdgroup_store(mqks, ss + 8*cc, TS, 0, false); + + simdgroup_store(mqk, ss + 8*cc, TS, 0, false); } } - // used to detect blocks full of -INF - float smax = -INFINITY; - // online softmax { - float ms[Q]; - - for (short j = 0; j < Q; ++j) { - const float m = M[j]; + for (ushort j = 0; j < Q; ++j) { + const half m = M[j]; // scale and apply the logitcap / mask - float s = ss[j*TF + tiisg]*scale; + half s = ss[j*TS + tiisg]*args.scale; - if (logit_softcap != 0.0f) { - s = logit_softcap*precise::tanh(s); + if (args.logit_softcap != 0.0f) { + s = args.logit_softcap*precise::tanh(s); } - if (mask != q) { - // mqk = mqk + mask*slope - s += slope*mp[ic + j*nb31/sizeof(half) + tiisg]; - } + // mqk = mqk + mask*slope + s += slope*ss[j*TS + C + tiisg]; - smax = simd_max(max(smax, s)); M[j] = simd_max(max(M[j], s)); - ms[j] = exp(m - M[j]); - const float vs = exp(s - M[j]); + const half ms = exp(m - M[j]); + const half vs = exp(s - M[j]); - S[j] = S[j]*ms[j] + simd_sum(vs); + S[j] = S[j]*ms + simd_sum(vs); // the P matrix from the paper (Q rows, C columns) - ss[j*TF + tiisg] = vs; - } + ss[j*TS + tiisg] = vs; - // create a QxQ diagonal matrix for rescaling the output - if (tiisg < Q) { - ss[tiisg*TF + C + tiisg] = ms[tiisg]; + // create a QxQ diagonal matrix for rescaling the output + if (tiisg == j) { + ss[j*TS + 2*C + j] = ms; + } } } - // skip -INF blocks - if (smax == -INFINITY) { - continue; - } - // O = diag(ms)*O { - simdgroup_float8x8 mm; - simdgroup_load(mm, ss + C, TF, 0, false); + s8x8_t mm; + simdgroup_load(mm, ss + 2*C, TS, 0, false); + #pragma unroll(D8) for (short i = 0; i < D8; ++i) { simdgroup_multiply(lo[i], mm, lo[i]); } @@ -2402,16 +2756,64 @@ kernel void kernel_flash_attn_ext_f16( // O = O + (Q*K^T)*V { for (short cc = 0; cc < C/8; ++cc) { - device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23)); + s8x8_t ms; + simdgroup_load(ms, ss + 8*cc, TS, 0, false); - for (short i = 0; i < D8; ++i) { - simdgroup_half8x8 mk; - simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false); + if (is_same::value) { + // we can read directly from global memory + device const v_t * pv = (device const v_t *) ((device const char *) v + ((ic + 8*cc)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); - simdgroup_float8x8 mv; - simdgroup_load(mv, ss + 8*cc, TF, 0, false); + #pragma unroll(D8) + for (short i = 0; i < D8; ++i) { + v8x8_t mv; + simdgroup_load(mv, pv + i*8, args.nb_12_1/sizeof(v_t), 0, false); // TODO: use ne20 - simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]); + simdgroup_multiply_accumulate(lo[i], ms, mv, lo[i]); + } + } else { + for (short ii = 0; ii < D16; ii += 4) { + device const vd4x4_t * pv4x4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 8*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); + + if (D16%4 == 0) { + // no need for bound checks + { + v4x4_t tmp; + deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); + sv4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + #pragma unroll(4) + for (short k = 0; k < 4; ++k) { + v8x8_t mv; + + simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); + + simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); + } + } else { + if (ii + tx < D16) { + v4x4_t tmp; + deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); + sv4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + for (short k = 0; k < 4 && ii + k < D16; ++k) { + v8x8_t mv; + + simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); + + simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); + } + } + } } } } @@ -2420,23 +2822,23 @@ kernel void kernel_flash_attn_ext_f16( // these are needed for reducing the results from the simdgroups (reuse the ss buffer) for (short j = 0; j < Q; ++j) { if (tiisg == 0) { - ss[j*TF + 0] = S[j]; - ss[j*TF + 1] = M[j]; + ss[j*TS + 0] = S[j]; + ss[j*TS + 1] = M[j]; } } } // reduce the warps sequentially - for (short sg = 1; sg < nsg; ++sg) { - float S = { 0.0h }; - float M = { -FLT_MAX/2 }; + for (ushort sg = 1; sg < nsg; ++sg) { + half S = { 0.0f }; + half M = { -__FLT16_MAX__/2 }; threadgroup_barrier(mem_flags::mem_threadgroup); // each simdgroup stores its output to shared memory, reusing sq if (sgitg == sg) { for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], sq + i*8, T, 0, false); + simdgroup_store(lo[i], so + i*8, D, 0, false); } } @@ -2445,39 +2847,41 @@ kernel void kernel_flash_attn_ext_f16( // the first simdgroup accumulates the results from the other simdgroups if (sgitg == 0) { for (short j = 0; j < Q; ++j) { - const float S0 = ss[j*TF + 0]; - const float S1 = ss[j*TF + sg*SH + 0]; + const half S0 = ss[j*TS + 0]; + const half S1 = ss[j*TS + sg*SH + 0]; - const float M0 = ss[j*TF + 1]; - const float M1 = ss[j*TF + sg*SH + 1]; + const half M0 = ss[j*TS + 1]; + const half M1 = ss[j*TS + sg*SH + 1]; M = max(M0, M1); - const float ms0 = exp(M0 - M); - const float ms1 = exp(M1 - M); + const half ms0 = exp(M0 - M); + const half ms1 = exp(M1 - M); S = S0*ms0 + S1*ms1; if (tiisg == 0) { - ss[j*TF + 0] = S; - ss[j*TF + 1] = M; + ss[j*TS + 0] = S; + ss[j*TS + 1] = M; - ss[j*TF + C + j ] = ms0; - ss[j*TF + C + j + sg*SH] = ms1; + ss[j*TS + 2*C + j ] = ms0; + ss[j*TS + 2*C + j + sg*SH] = ms1; } } // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 { - simdgroup_half8x8 t; - simdgroup_float8x8 ms0; - simdgroup_float8x8 ms1; + s8x8_t ms0; + s8x8_t ms1; - simdgroup_load(ms0, ss + C, TF, 0, false); - simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false); + simdgroup_load(ms0, ss + 2*C, TS, 0, false); + simdgroup_load(ms1, ss + 2*C + sg*SH, TS, 0, false); + #pragma unroll(D8) for (short i = 0; i < D8; ++i) { - simdgroup_load (t, sq + i*8, T, 0, false); + o8x8_t t; + + simdgroup_load (t, so + i*8, D, 0, false); simdgroup_multiply(t, ms1, t); simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t); @@ -2489,7 +2893,7 @@ kernel void kernel_flash_attn_ext_f16( // store result to shared memory (reuse sq) if (sgitg == 0) { for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], sq + i*8, T, 0, false); + simdgroup_store(lo[i], so + i*8, D, 0, false); } } @@ -2497,257 +2901,361 @@ kernel void kernel_flash_attn_ext_f16( // final rescale with 1/S and store to global memory if (sgitg == 0) { - for (short j = 0; j < Q && iq1 + j < ne01; ++j) { - const float S = ss[j*TF + 0]; + for (short j = 0; j < Q && iq1 + j < args.ne01; ++j) { + const float S = ss[j*TS + 0]; for (short i = tiisg; i < D4; i += NW) { - dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S; + dst4[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)(iq1 + j)*args.ne1)*D4 + i] = (float4) so4[j*D4 + i]/S; } } } } -template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>; -template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>; -template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; -template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; -template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; -//template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; +// TODO: this is quite ugly. in the future these types will be hardcoded in the kernel, but for now keep them as +// template to be able to explore different combinations +// +#define FA_TYPES \ + half, half4, simdgroup_half8x8, \ + half, half4x4, simdgroup_half8x8, \ + half, half4x4, simdgroup_half8x8, \ + float, simdgroup_float8x8, \ + float, simdgroup_float8x8, \ + half, half4, simdgroup_half8x8 -template // head size, queries per threadgroup, cache items per threadgroup -kernel void kernel_flash_attn_ext_vec_f16( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { +typedef decltype(kernel_flash_attn_ext) flash_attn_ext_t; + +template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_bf16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +#endif + +template [[host_name("kernel_flash_attn_ext_q4_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q4_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q5_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q5_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q8_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +#undef FA_TYPES + +template< + typename q4_t, // query types in shared memory + typename q4x4_t, + typename k4x4_t, // key types in shared memory + typename v4x4_t, // value types in shared memory + typename qk_t, // Q*K types + typename s_t, // soft-max types + typename s4_t, + typename s4x4_t, + typename o4x4_t, // attention accumulation types + typename kd4x4_t, // key type in device memory + short nl_k, + void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), + typename vd4x4_t, // key type in device memory + short nl_v, + void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), + short D, // head size + short Q = 1, // queries per threadgroup + short C = 32> // cache items per threadgroup +kernel void kernel_flash_attn_ext_vec( + constant ggml_metal_kargs_flash_attn_ext & args, + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device char * dst, + threadgroup half * shmem_f16 [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const short nsg = ntg.y; // number of simdgroups - const short iq3 = tgpig[2]; - const short iq2 = tgpig[1]; - const short iq1 = tgpig[0]; + const int iq3 = tgpig[2]; + const int iq2 = tgpig[1]; + const int iq1 = tgpig[0]; - const short D4 = D/4; - const short NW = N_SIMDWIDTH; - const short SH = (C + Q); // shared memory per simdgroup in (half) + const short D4 = D/4; + const short D16 = D/16; + const short NW = N_SIMDWIDTH; + const short NL = NW/4; // note: this can be adjusted to support D%64 == 0 and D%32 == 0 + const short SH = 2*C; // shared memory per simdgroup - const short T = D + 2*nsg*SH; // shared memory size per query in (half) + const short T = D + nsg*SH; // shared memory size per query in (half) - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = iq2; - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exp); - } - - //threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data - threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 - threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix - threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4 - threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + 1*T); // scratch buffer for the results + //threadgroup q_t * sq = (threadgroup q_t *) (shmem_f16 + 0*D); // holds the query data + threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 + 0*D); // same as above but in q4_t + threadgroup q4x4_t * sq4x4 = (threadgroup q4x4_t *) (shmem_f16 + 0*D); // same as above but in q4x4_t + threadgroup s_t * ss = (threadgroup s_t *) (shmem_f16 + sgitg*SH + Q*D); // scratch buffer for attention + threadgroup s4_t * ss4 = (threadgroup s4_t *) (shmem_f16 + sgitg*SH + Q*D); // same as above but in s4_t + threadgroup half * sm = (threadgroup half *) (shmem_f16 + sgitg*SH + C + Q*D); // scratch buffer for mask + threadgroup o4x4_t * sr4x4 = (threadgroup o4x4_t *) (shmem_f16 + sgitg*D + Q*T); // scratch buffer for the results // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - half4 lo[D4/NW]; + o4x4_t lo[D16/NL]; // load heads from Q to shared memory - device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03)); + device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*args.nb01 + iq2*args.nb02 + iq3*args.nb03)); for (short i = tiisg; i < D4; i += NW) { - if (iq1 < ne01) { - sq4[i] = (half4) q4[i]; + if (iq1 < args.ne01) { + sq4[i] = (q4_t) q4[i]; } else { - sq4[i] = 0.0h; + sq4[i] = (q4_t) 0.0f; } } // zero out lo - for (short i = tiisg; i < D4; i += NW) { - lo[i/NW] = 0.0h; + for (short i = 0; i < D16/NL; ++i) { + lo[i] = (o4x4_t) 0.0f; } // zero out shared memory SH for (short i = tiisg; i < SH/4; i += NW) { - ss4[i] = 0.0h; + ss4[i] = (s4_t) 0.0f; } threadgroup_barrier(mem_flags::mem_threadgroup); { - float S = { 0.0h }; - float M = { -FLT_MAX/2 }; + half S = 0.0f; + half M = -__FLT16_MAX__/2; - // assume K and V are same shape - const short ne22 = ne12; - const short ne23 = ne13; + // thread indices inside the simdgroup + const short tx = tiisg%NL; + const short ty = tiisg/NL; - // broadcast - const short rk2 = ne02/ne12; - const short rk3 = ne03/ne13; + // broadcast kv + //const short rk2 = args.ne02/args.ne12; + //const short rk3 = args.ne03/args.ne13; - const short rv2 = ne02/ne22; - const short rv3 = ne03/ne23; - - // k indices - const short ik2 = iq2 / rk2; - const short ik3 = iq3 / rk3; - - // v indices - const short iv2 = iq2 / rv2; - const short iv3 = iq3 / rv3; + const short ikv2 = iq2/(args.ne02/args.ne_12_2); + const short ikv3 = iq3/(args.ne03/args.ne_12_3); // load the queries from shared memory into local memory - float4 mq[D4]; + q4x4_t mq[D16/NL]; - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - mq[i] = (float4) sq4[i]; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + mq[ii/NL] = sq4x4[ii + tx]; } + const bool has_mask = mask != q; + // pointer to the mask - device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31); + device const half * pm = (device const half *) (mask + iq1*args.nb31); + + half slope = 1.0f; + + // ALiBi + if (args.max_bias > 0.0f) { + const short h = iq2; + + const half base = h < args.n_head_log2 ? args.m0 : args.m1; + const short exph = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1; + + slope = pow(base, exph); + } // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + for (int ic0 = 0; ic0 < args.ne11; ic0 += C*nsg) { const int ic = ic0 + C*sgitg; - if (ic >= ne11) { + if (ic >= args.ne11) { break; } + if (has_mask) { + sm[tiisg] = pm[ic + tiisg]; + } + // Q*K^T { -#pragma unroll + // each simdgroup processes 1 query and 4 (NW/NL) keys for (short cc = 0; cc < C/4; ++cc) { - float4 mqk = { 0.0h }; + qk_t mqka[4] = { 0.0, 0.0, 0.0, 0.0 }; - device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13)); + device const kd4x4_t * pk = (device const kd4x4_t *) ((device const char *) k + ((ic + 4*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + const short i = ii + tx; - float4x4 mk; - mk[0] = (float4) pk4[i + 0*(nb11/8)]; - mk[1] = (float4) pk4[i + 1*(nb11/8)]; - mk[2] = (float4) pk4[i + 2*(nb11/8)]; - mk[3] = (float4) pk4[i + 3*(nb11/8)]; + k4x4_t mk; + deq_k(pk + i/nl_k, i%nl_k, mk); - mqk += (float4) (mq[i] * mk); + // note: this is less precise than the version below + //mqka[0] += dot(mq[ii/NL][0], mk[0]); + //mqka[1] += dot(mq[ii/NL][1], mk[1]); + //mqka[2] += dot(mq[ii/NL][2], mk[2]); + //mqka[3] += dot(mq[ii/NL][3], mk[3]); + + mqka[0] += dot((float4) mq[ii/NL][0], (float4) mk[0]); + mqka[1] += dot((float4) mq[ii/NL][1], (float4) mk[1]); + mqka[2] += dot((float4) mq[ii/NL][2], (float4) mk[2]); + mqka[3] += dot((float4) mq[ii/NL][3], (float4) mk[3]); } - // reduce the results from the threads in the simdgroup - mqk += simd_shuffle_down(mqk, 16); - mqk += simd_shuffle_down(mqk, 8); + qk_t mqk = mqka[0] + mqka[1] + mqka[2] + mqka[3]; + + // simdgroup reduce + // [ 0 .. 7] -> [ 0] + // [ 8 .. 15] -> [ 8] + // [16 .. 23] -> [16] + // [24 .. 31] -> [24] + //mqk += simd_shuffle_down(mqk, 16); + //mqk += simd_shuffle_down(mqk, 8); mqk += simd_shuffle_down(mqk, 4); mqk += simd_shuffle_down(mqk, 2); mqk += simd_shuffle_down(mqk, 1); // mqk = mqk*scale + mask*slope - if (tiisg == 0) { - mqk *= scale; + if (tx == 0) { + mqk *= args.scale; - if (logit_softcap != 0.0f) { - mqk = logit_softcap*precise::tanh(mqk); + if (args.logit_softcap != 0.0f) { + mqk = args.logit_softcap*precise::tanh(mqk); } - mqk += (mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f; + mqk += sm[4*cc + ty]*slope; - ss4[cc] = mqk; + ss[4*cc + ty] = mqk; } } } + simdgroup_barrier(mem_flags::mem_threadgroup); + // online softmax { - const short p = tiisg; - - const float m = M; - const float s = ss[p]; + const half m = M; + const half s = ss[tiisg]; M = simd_max(max(M, s)); - const float ms = exp(m - M); - const float vs = exp(s - M); + const half ms = exp(m - M); + const half vs = exp(s - M); S = S*ms + simd_sum(vs); // the P matrix from the paper (Q rows, C columns) - ss[p] = vs; + ss[tiisg] = vs; // O = diag(ms)*O -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; - lo[i/NW] *= ms; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + lo[ii/NL] *= ms; } } + simdgroup_barrier(mem_flags::mem_threadgroup); + // O = O + (Q*K^T)*V { -#pragma unroll for (short cc = 0; cc < C/4; ++cc) { - device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23)); + device const vd4x4_t * pv4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 4*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; + const s4x4_t ms(ss[4*cc + ty]); - lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0]; - lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1]; - lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2]; - lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3]; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + const short i = ii + tx; + + v4x4_t mv; + deq_v(pv4 + i/nl_v, i%nl_v, mv); + + lo[ii/NL] += mv*ms; } } } - } // these are needed for reducing the results from the simdgroups (reuse the ss buffer) if (tiisg == 0) { - ss[0] = S; - ss[1] = M; + ss[0] = (s_t) S; + ss[1] = (s_t) M; } } + // simdgroup reduce + // [ 0, 8, 16, 24] -> [ 0] + // [ 1, 9, 17, 25] -> [ 1] + // [ 2, 10, 18, 26] -> [ 2] + // [ 3, 11, 19, 27] -> [ 3] + // [ 4, 12, 20, 28] -> [ 4] + // [ 5, 13, 21, 29] -> [ 5] + // [ 6, 14, 22, 30] -> [ 6] + // [ 7, 15, 23, 31] -> [ 7] + for (short ii = 0; ii < D16; ii += NL) { + lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 16); + lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 8); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 4); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 2); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 1); + + lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 16); + lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 8); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 4); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 2); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 1); + + lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 16); + lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 8); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 4); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 2); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 1); + + lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 16); + lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 8); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 4); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 2); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 1); + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + // store results to shared memory - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - sr4[i] = lo[ii/NW]; + for (short i = tiisg; i < D16; i += NL) { + sr4x4[i] = lo[i/NL]; } threadgroup_barrier(mem_flags::mem_threadgroup); @@ -2755,18 +3263,18 @@ kernel void kernel_flash_attn_ext_vec_f16( // parallel reduce for (short r = nsg/2; r > 0; r >>= 1) { if (sgitg < r) { - const float S0 = ss[ 0]; - const float S1 = ss[r*SH + 0]; + const half S0 = ss[ 0]; + const half S1 = ss[r*SH + 0]; - const float M0 = ss[ 1]; - const float M1 = ss[r*SH + 1]; + const half M0 = ss[ 1]; + const half M1 = ss[r*SH + 1]; - const float M = max(M0, M1); + const half M = max(M0, M1); - const float ms0 = exp(M0 - M); - const float ms1 = exp(M1 - M); + const half ms0 = exp(M0 - M); + const half ms1 = exp(M1 - M); - const float S = S0*ms0 + S1*ms1; + const half S = S0*ms0 + S1*ms1; if (tiisg == 0) { ss[0] = S; @@ -2774,117 +3282,124 @@ kernel void kernel_flash_attn_ext_vec_f16( } // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1; + for (short i = tiisg; i < D16; i += NW) { + sr4x4[i] = sr4x4[i]*ms0 + sr4x4[i + r*D16]*ms1; } } threadgroup_barrier(mem_flags::mem_threadgroup); } - device float4 * dst4 = (device float4 *) dst; + device float4x4 * dst44 = (device float4x4 *) dst; // final rescale with 1/S and store to global memory if (sgitg == 0) { const float S = ss[0]; - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S; + for (short i = tiisg; i < D16; i += NW) { + dst44[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)iq1*args.ne1)*D16 + i] = (float4x4) sr4x4[i]/S; } } } -template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; -//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; +// note: I think the s_t can be half instead of float, because the Q*K scaling is done before storing to shared mem +// in the other (non-vec) kernel, we need s_t to also be float because we scale during the soft_max +// +#define FA_TYPES \ + half4, half4x4, \ + half4x4, \ + half4x4, \ + float, \ + half, half4, half4x4, \ + half4x4 + +typedef decltype(kernel_flash_attn_ext_vec) flash_attn_ext_vec_t; + +template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + +template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + +#undef FA_TYPES template kernel void kernel_cpy( - device const void * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); + const int64_t i3 = n/(args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0); - device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device T1 * dst_data = (device T1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { - device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) { + device const T0 * src = (device T0 *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); dst_data[i00] = (T1) src[0]; } } typedef decltype(kernel_cpy) kernel_cpy_t; -template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy; +#endif +template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_cpy_bf16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_bf16_bf16")]] kernel kernel_cpy_t kernel_cpy; +#endif kernel void kernel_cpy_f32_q8_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK8_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK8_0; - device block_q8_0 * dst_data = (device block_q8_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q8_0 * dst_data = (device block_q8_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK8_0; i00 < ne00; i00 += ntg.x*QK8_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK8_0; i00 < args.ne00; i00 += ntg.x*QK8_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max @@ -2907,42 +3422,27 @@ kernel void kernel_cpy_f32_q8_0( } kernel void kernel_cpy_f32_q4_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_0; - device block_q4_0 * dst_data = (device block_q4_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q4_0 * dst_data = (device block_q4_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_0; i00 < ne00; i00 += ntg.x*QK4_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_0; i00 < args.ne00; i00 += ntg.x*QK4_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -2974,42 +3474,27 @@ kernel void kernel_cpy_f32_q4_0( } kernel void kernel_cpy_f32_q4_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_1; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_1; - device block_q4_1 * dst_data = (device block_q4_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q4_1 * dst_data = (device block_q4_1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_1; i00 < ne00; i00 += ntg.x*QK4_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_1; i00 < args.ne00; i00 += ntg.x*QK4_1) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float min = FLT_MAX; float max = -FLT_MAX; @@ -3040,42 +3525,27 @@ kernel void kernel_cpy_f32_q4_1( } kernel void kernel_cpy_f32_q5_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK5_0; - device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q5_0 * dst_data = (device block_q5_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK5_0; i00 < args.ne00; i00 += ntg.x*QK5_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -3113,42 +3583,27 @@ kernel void kernel_cpy_f32_q5_0( } kernel void kernel_cpy_f32_q5_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK5_1; - device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q5_1 * dst_data = (device block_q5_1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK5_1; i00 < args.ne00; i00 += ntg.x*QK5_1) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float max = src[0]; float min = src[0]; @@ -3195,47 +3650,28 @@ static inline int best_index_int8(int n, constant float * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -constexpr constant static float kvalues_iq4nl_f[16] = { - -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f -}; - kernel void kernel_cpy_f32_iq4_nl( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_NL; - device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_iq4_nl * dst_data = (device block_iq4_nl *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_NL; i00 < args.ne00; i00 += ntg.x*QK4_NL) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -3271,104 +3707,70 @@ kernel void kernel_cpy_f32_iq4_nl( } dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d; - } } kernel void kernel_concat( + constant ggml_metal_kargs_concat & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int32_t & dim, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; + const int i3 = tgpig.z; + const int i2 = tgpig.y; + const int i1 = tgpig.x; - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + int o[4] = {0, 0, 0, 0}; + o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03)); device const float * x; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) { + x = (device const float *)(src0 + (i3 )*args.nb03 + (i2 )*args.nb02 + (i1 )*args.nb01 + (i0 )*args.nb00); } else { - x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); + x = (device const float *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10); } - device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device float * y = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); *y = *x; } } +template void kernel_mul_mv_q2_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q2_K * x = (device const block_q2_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q2_K * x = (device const block_q2_K *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; - const int step = sizeof(block_q2_K) * nb; - const int ix = tiisg/8; // 0...3 const int it = tiisg%8; // 0...7 const int iq = it/4; // 0 or 1 @@ -3413,83 +3815,64 @@ void kernel_mul_mv_q2_K_f32_impl( (acc1[3] + 1.f/256.f * acc2[3]) * (sc[6] & 0xF) * 1.f/64.f) - dmin * (sumy[0] * (sc[0] & 0xF0) + sumy[1] * (sc[2] & 0xF0) + sumy[2] * (sc[4] & 0xF0) + sumy[3] * (sc[6] & 0xF0)); - qs += step/2; - sc += step; - dh += step/2; + qs += args.nb01/2; + sc += args.nb01; + dh += args.nb01/2; } y4 += 4 * QK_K; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_q2_K_f32")]] kernel void kernel_mul_mv_q2_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q2_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q3_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q3_K * x = (device const block_q3_K *) src0 + first_row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q3_K * x = (device const block_q3_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float yl[32]; @@ -3519,9 +3902,10 @@ void kernel_mul_mv_q3_K_f32_impl( const ushort4 hm = mm[2*ip + il/2]; - const int shift = 2*il; - const float v1 = il == 0 ? 4.f : 64.f; - const float v2 = 4.f * v1; + const short shift = 2*il; + + const float v1 = il == 0 ? 4.f : 64.f; + const float v2 = 4.f * v1; const uint16_t s_shift1 = 4*ip; const uint16_t s_shift2 = s_shift1 + il; @@ -3529,8 +3913,6 @@ void kernel_mul_mv_q3_K_f32_impl( const int q_offset = 32*ip + l0; const int y_offset = 128*ip + 32*il + l0; - const int step = sizeof(block_q3_K) * nb / 2; - device const float * y1 = yy + ix*QK_K + y_offset; uint32_t scales32, aux32; @@ -3540,7 +3922,6 @@ void kernel_mul_mv_q3_K_f32_impl( float sumf1[2] = {0.f}; float sumf2[2] = {0.f}; for (int i = ix; i < nb; i += 4) { - for (int l = 0; l < 8; ++l) { yl[l+ 0] = y1[l+ 0]; yl[l+ 8] = y1[l+16]; @@ -3554,7 +3935,6 @@ void kernel_mul_mv_q3_K_f32_impl( device const half * dh = &x[i].d; for (int row = 0; row < 2; ++row) { - const float d_all = (float)dh[0]; scales16[0] = a[4]; @@ -3594,73 +3974,52 @@ void kernel_mul_mv_q3_K_f32_impl( sumf1[row] += d1 * (scales[1] - 32); sumf2[row] += d2 * (scales[3] - 32); - q += step; - h += step; - a += step; - dh += step; - + q += args.nb01/2; + h += args.nb01/2; + a += args.nb01/2; + dh += args.nb01/2; } y1 += 4 * QK_K; - } for (int row = 0; row < 2; ++row) { const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift); sumf1[row] = simd_sum(sumf); } + + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + if (tiisg == 0) { for (int row = 0; row < 2; ++row) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = sumf1[row]; + dst_f32[first_row + row] = sumf1[row]; } } } [[host_name("kernel_mul_mv_q3_K_f32")]] kernel void kernel_mul_mv_q3_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q3_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q4_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; @@ -3671,35 +4030,32 @@ void kernel_mul_mv_q4_K_f32_impl( const int iq = it/4; // 0 or 1 const int ir = it%4; // 0...3 - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; const int first_row = r0 * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q4_K * x = (device const block_q4_K *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[16]; float yh[16]; float sumf[N_DST]={0.f}, all_sum; - const int step = sizeof(block_q4_K) * nb / 2; - device const float * y4 = y + ix * QK_K + 64 * iq + 8 * ir; uint16_t sc16[4]; thread const uint8_t * sc8 = (thread const uint8_t *)sc16; for (int ib = ix; ib < nb; ib += 4) { - float4 sumy = {0.f, 0.f, 0.f, 0.f}; for (int i = 0; i < 8; ++i) { yl[i+0] = y4[i+ 0]; sumy[0] += yl[i+0]; @@ -3713,7 +4069,6 @@ void kernel_mul_mv_q4_K_f32_impl( device const half * dh = &x[ib].d; for (int row = 0; row < N_DST; row++) { - sc16[0] = sc[0] & kmask1; sc16[1] = sc[2] & kmask1; sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2); @@ -3742,88 +4097,67 @@ void kernel_mul_mv_q4_K_f32_impl( (acc2[2] + 1.f/256.f * acc2[3]) * sc8[5] * 1.f/16.f) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += step; - sc += step; - dh += step; + q1 += args.nb01/2; + sc += args.nb01/2; + dh += args.nb01/2; } y4 += 4 * QK_K; } + device float * dst_f32 = (device float *) dst + (int64_t)im*args.ne0*args.ne1 + (int64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_q4_K_f32")]] kernel void kernel_mul_mv_q4_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q4_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q5_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; + const int r0 = tgpig.x; + const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q5_K * x = (device const block_q5_K *) src0 + first_row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q5_K * x = (device const block_q5_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float sumf[2]={0.f}; - const int step = sizeof(block_q5_K) * nb; - float yl[16], yh[16]; const uint16_t kmask1 = 0x3f3f; @@ -3851,7 +4185,6 @@ void kernel_mul_mv_q5_K_f32_impl( device const float * y1 = yy + ix*QK_K + y_offset; for (int i = ix; i < nb; i += 4) { - device const uint8_t * q1 = x[i].qs + q_offset; device const uint8_t * qh = x[i].qh + l0; device const half * dh = &x[i].d; @@ -3867,7 +4200,6 @@ void kernel_mul_mv_q5_K_f32_impl( } for (int row = 0; row < 2; ++row) { - device const uint8_t * q2 = q1 + 64; sc16[0] = a[0] & kmask1; @@ -3896,91 +4228,70 @@ void kernel_mul_mv_q5_K_f32_impl( sc8[5] * (acc1[3]/16.f + 16.f*acc2[3])) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += step; - qh += step; - dh += step/2; - a += step/2; - + q1 += args.nb01; + qh += args.nb01; + dh += args.nb01/2; + a += args.nb01/2; } y1 += 4 * QK_K; - } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < 2; ++row) { const float tot = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; + dst_f32[first_row + row] = tot; } } } [[host_name("kernel_mul_mv_q5_K_f32")]] kernel void kernel_mul_mv_q5_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q5_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q6_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const uint8_t kmask1 = 0x03; const uint8_t kmask2 = 0x0C; const uint8_t kmask3 = 0x30; const uint8_t kmask4 = 0xC0; - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; - const int row = 2 * r0 + sgitg; + const int row = 2*r0 + sgitg; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q6_K * x = (device const block_q6_K *) src0 + row * nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q6_K * x = (device const block_q6_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float sumf = 0; @@ -3997,7 +4308,6 @@ void kernel_mul_mv_q6_K_f32_impl( const int q_offset_h = 32*ip + l0; for (int i = ix; i < nb; i += 2) { - device const uint8_t * q1 = x[i].ql + q_offset_l; device const uint8_t * q2 = q1 + 32; device const uint8_t * qh = x[i].qh + q_offset_h; @@ -4019,90 +4329,70 @@ void kernel_mul_mv_q6_K_f32_impl( } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + const float tot = simd_sum(sumf); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; + dst_f32[row] = tot; } } [[host_name("kernel_mul_mv_q6_K_f32")]] kernel void kernel_mul_mv_q6_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q6_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } // ======================= "True" 2-bit +template void kernel_mul_mv_iq2_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_xxs * x = (device const block_iq2_xxs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_xxs * x = (device const block_iq2_xxs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + threadgroup uint64_t * svalues = (threadgroup uint64_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 256); { int nval = 4; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xxs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2xxs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4132,106 +4422,85 @@ void kernel_mul_mv_iq2_xxs_f32_impl( float sum = 0; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + aux8[l]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + aux8[l]); + const uint8_t signs = ssigns[(aux32 >> 7*l) & 127]; for (int j = 0; j < 8; ++j) { sum += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } sumf[row] += d * sum; - dh += nb*sizeof(block_iq2_xxs)/2; - q2 += nb*sizeof(block_iq2_xxs)/2; + dh += args.nb01/2; + q2 += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_xxs_f32")]] kernel void kernel_mul_mv_iq2_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + kernel_mul_mv_iq2_xxs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq2_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_xs * x = (device const block_iq2_xs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_xs * x = (device const block_iq2_xs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 512); + threadgroup uint64_t * svalues = (threadgroup uint64_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 512); { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2xs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4263,114 +4532,94 @@ void kernel_mul_mv_iq2_xs_f32_impl( float sum1 = 0, sum2 = 0; for (int l = 0; l < 2; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + (q2[l] & 511)); + const uint8_t signs = ssigns[(q2[l] >> 9)]; for (int j = 0; j < 8; ++j) { sum1 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } for (int l = 2; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + (q2[l] & 511)); + const uint8_t signs = ssigns[(q2[l] >> 9)]; for (int j = 0; j < 8; ++j) { sum2 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } sumf[row] += d1 * sum1 + d2 * sum2; - dh += nb*sizeof(block_iq2_xs)/2; - q2 += nb*sizeof(block_iq2_xs)/2; - sc += nb*sizeof(block_iq2_xs); + dh += args.nb01/2; + q2 += args.nb01/2; + sc += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_xs_f32")]] kernel void kernel_mul_mv_iq2_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq3_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq3_xxs * x = (device const block_iq3_xxs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq3_xxs * x = (device const block_iq3_xxs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + threadgroup uint32_t * svalues = (threadgroup uint32_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 256); { int nval = 4; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq3xxs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4379,7 +4628,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( device const float * y4 = y + 32 * ix; for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - for (int i = 0; i < 32; ++i) { yl[i] = y4[i]; } @@ -4393,16 +4641,15 @@ void kernel_mul_mv_iq3_xxs_f32_impl( device const half * dh = &xr->d; for (int row = 0; row < N_DST; row++) { - const float db = dh[0]; const uint32_t aux32 = gas[0] | (gas[1] << 16); const float d = db * (0.5f + (aux32 >> 28)); float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(svalues + q3[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(svalues + q3[2*l+1]); + const uint8_t signs = ssigns[(aux32 >> 7*l) & 127]; for (int j = 0; j < 4; ++j) { sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -4410,95 +4657,75 @@ void kernel_mul_mv_iq3_xxs_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb*sizeof(block_iq3_xxs)/2; - q3 += nb*sizeof(block_iq3_xxs); - gas += nb*sizeof(block_iq3_xxs)/2; + dh += args.nb01/2; + q3 += args.nb01; + gas += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + dst_f32[first_row + row] = all_sum * 0.5f; } } } [[host_name("kernel_mul_mv_iq3_xxs_f32")]] kernel void kernel_mul_mv_iq3_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_xxs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq3_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq3_s * x = (device const block_iq3_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq3_s * x = (device const block_iq3_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; + threadgroup uint32_t * svalues = (threadgroup uint32_t *) shmem; { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq3s_grid[pos + i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4529,8 +4756,8 @@ void kernel_mul_mv_iq3_s_f32_impl( float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; - const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; + const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? svalues + 256 : svalues; + const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? svalues + 256 : svalues; const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); for (int j = 0; j < 4; ++j) { @@ -4540,97 +4767,77 @@ void kernel_mul_mv_iq3_s_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb*sizeof(block_iq3_s)/2; - qs += nb*sizeof(block_iq3_s); - qh += nb*sizeof(block_iq3_s); - sc += nb*sizeof(block_iq3_s); - signs += nb*sizeof(block_iq3_s); + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01; + sc += args.nb01; + signs += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_iq3_s_f32")]] kernel void kernel_mul_mv_iq3_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_s_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq2_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_s * x = (device const block_iq2_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + //threadgroup uint64_t * svalues = (threadgroup uint64_t *) shmem; //{ // int nval = 32; // int pos = (32*sgitg + tiisg)*nval; - // for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i]; + // for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2s_grid[pos + i]; // threadgroup_barrier(mem_flags::mem_threadgroup); //} @@ -4662,8 +4869,8 @@ void kernel_mul_mv_iq2_s_f32_impl( float2 sum = {0}; for (int l = 0; l < 2; ++l) { - //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); - //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(svalues + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(svalues + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); for (int j = 0; j < 8; ++j) { @@ -4673,85 +4880,66 @@ void kernel_mul_mv_iq2_s_f32_impl( } sumf[row] += d1 * sum[0] + d2 * sum[1]; - dh += nb*sizeof(block_iq2_s)/2; - qs += nb*sizeof(block_iq2_s); - qh += nb*sizeof(block_iq2_s); - sc += nb*sizeof(block_iq2_s); - signs += nb*sizeof(block_iq2_s); + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01; + sc += args.nb01; + signs += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_s_f32")]] kernel void kernel_mul_mv_iq2_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_s_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq1_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const block_iq1_s * x = (device const block_iq1_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4794,54 +4982,50 @@ void kernel_mul_mv_iq1_s_f32_impl( } sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1); - dh += nb*sizeof(block_iq1_s)/2; - qs += nb*sizeof(block_iq1_s); - qh += nb*sizeof(block_iq1_s)/2; + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq1_m_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const block_iq1_m * x = (device const block_iq1_m *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4893,59 +5077,55 @@ void kernel_mul_mv_iq1_m_f32_impl( sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); - sc += nb*sizeof(block_iq1_m)/2; - qs += nb*sizeof(block_iq1_m); - qh += nb*sizeof(block_iq1_m); + sc += args.nb01/2; + qs += args.nb01; + qh += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq4_nl_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK4_NL; + threadgroup float * shmem_f32 = (threadgroup float *) shmem; + const int nb = args.ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq4_nl * x = (device const block_iq4_nl *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const block_iq4_nl * x = (device const block_iq4_nl *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); const int ix = tiisg/2; // 0...15 const int it = tiisg%2; // 0 or 1 - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + shmem_f32[tiisg] = kvalues_iq4nl_f[tiisg%16]; threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; @@ -4963,7 +5143,7 @@ void kernel_mul_mv_iq4_nl_f32_impl( device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { + for (int row = 0; row < 2 && first_row + row < args.ne01; ++row) { device const block_iq4_nl & xb = x[row*nb + ib]; device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); @@ -4973,16 +5153,16 @@ void kernel_mul_mv_iq4_nl_f32_impl( aux32[0] = q4[0] | (q4[1] << 16); aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[0] * qf1; acc2 += yl[1] * qf2; aux32[0] = q4[2] | (q4[3] << 16); aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[2] * qf1; acc2 += yl[3] * qf2; @@ -4995,53 +5175,49 @@ void kernel_mul_mv_iq4_nl_f32_impl( yb += 16 * QK4_NL; } - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + + for (int row = 0; row < 2 && first_row + row < args.ne01; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq4_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK_K; + threadgroup float * shmem_f32 = (threadgroup float *) shmem; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const int ib_row = first_row * nb; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const block_iq4_xs * x = (device const block_iq4_xs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); const int ix = tiisg/16; // 0 or 1 const int it = tiisg%16; // 0...15 const int ib = it/2; const int il = it%2; - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + shmem_f32[tiisg] = kvalues_iq4nl_f[tiisg%16]; threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; @@ -5055,28 +5231,26 @@ void kernel_mul_mv_iq4_xs_f32_impl( float4 qf1, qf2; for (int ibl = ix; ibl < nb; ibl += 2) { - device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; for (int row = 0; row < 2; ++row) { - device const block_iq4_xs & xb = x[row*nb + ibl]; device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); float4 acc1 = {0.f}, acc2 = {0.f}; - aux32[0] = q4[0] & 0x0f0f0f0f; + aux32[0] = (q4[0] ) & 0x0f0f0f0f; aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[0] * qf1; acc2 += yl[1] * qf2; - aux32[0] = q4[1] & 0x0f0f0f0f; + aux32[0] = (q4[1] ) & 0x0f0f0f0f; aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[2] * qf1; acc2 += yl[3] * qf2; @@ -5090,560 +5264,68 @@ void kernel_mul_mv_iq4_xs_f32_impl( yb += 2 * QK_K; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < 2; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_s_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq1_m_f32")]] kernel void kernel_mul_mv_iq1_m_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_m_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_nl_f32")]] kernel void kernel_mul_mv_iq4_nl_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq4_nl_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_xs_f32")]] kernel void kernel_mul_mv_iq4_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -//============================= templates and their specializations ============================= - -// NOTE: this is not dequantizing - we are simply fitting the template -template -void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) { - float4x4 temp = *(((device float4x4 *)src)); - for (int i = 0; i < 16; i++){ - reg[i/4][i%4] = temp[i/4][i%4]; - } -} - -template -void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) { - half4x4 temp = *(((device half4x4 *)src)); - for (int i = 0; i < 16; i++){ - reg[i/4][i%4] = temp[i/4][i%4]; - } -} - -template -void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 1); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float md = -8.h * xb->d; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - for (int i=0;i<8;i++) { - reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md; - reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md; - } -} - -template -void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 2); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float m = xb->m; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - for (int i=0;i<8;i++) { - reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m; - reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m; - } -} - -template -void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 3); - const float d = xb->d; - const float md = -16.h * xb->d; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg[i/2][2*(i%2)+0] = d * x0 + md; - reg[i/2][2*(i%2)+1] = d * x1 + md; - } -} - -template -void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 4); - const float d = xb->d; - const float m = xb->m; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg[i/2][2*(i%2)+0] = d * x0 + m; - reg[i/2][2*(i%2)+1] = d * x1 + m; - } -} - -template -void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) { - device const int8_t * qs = ((device const int8_t *)xb->qs); - const half d = xb->d; - - for (int i = 0; i < 16; i++) { - reg[i/4][i%4] = (qs[i + 16*il] * d); - } -} - -template -void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) { - const float d = xb->d; - const float min = xb->dmin; - device const uint8_t * q = (device const uint8_t *)xb->qs; - float dl, ml; - uint8_t sc = xb->scales[il]; - - q = q + 32*(il/8) + 16*(il&1); - il = (il/2)%4; - - half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * q = (device const uint8_t *)xb->qs; - device const uint8_t * h = (device const uint8_t *)xb->hmask; - device const int8_t * scales = (device const int8_t *)xb->scales; - - q = q + 32 * (il/8) + 16 * (il&1); - h = h + 16 * (il&1); - uint8_t m = 1 << (il/2); - uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \ - ((il/4)>0 ? 12 : 3); - uint16_t kmask2 = il/8 ? 0xF0 : 0x0F; - uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; - int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) - : (scale_2&kmask2) | ((scale_1&kmask1) << 4); - float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); - const float ml = 4.f * dl; - - il = (il/2) & 3; - const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl *= coef; - - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); - } -} - -static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { - return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)} - : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))}; -} - -template -void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { - device const uchar * q = xb->qs; - - short is = (il/4) * 2; - q = q + (il/4) * 32 + 16 * (il&1); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.h; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) { - device const uint8_t * q = xb->qs; - device const uint8_t * qh = xb->qh; - - short is = (il/4) * 2; - q = q + 32 * (il/4) + 16 * (il&1); - qh = qh + 16 * (il&1); - uint8_t ul = 1 << (il/2); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.f; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - const float qh_val = il<2 ? 16.f : 256.f; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; - } -} - -template -void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * ql = (device const uint8_t *)xb->ql; - device const uint8_t * qh = (device const uint8_t *)xb->qh; - device const int8_t * scales = (device const int8_t *)xb->scales; - - ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); - qh = qh + 32*(il/8) + 16*(il&1); - float sc = scales[(il%2) + 2 * ((il/2))]; - il = (il/2) & 3; - - const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; - const float coef = il>1 ? 1.f/16.f : 1.f; - const float ml = d_all * sc * 32.f; - const float dl = d_all * sc * coef; - for (int i = 0; i < 16; ++i) { - const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) - : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); - reg[i/4][i%4] = dl * q - ml; - } -} - -template -void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. - device const uint16_t * q2 = xb->qs + 4*ib32; - const uint32_t aux32_g = q2[0] | (q2[1] << 16); - const uint32_t aux32_s = q2[2] | (q2[3] << 16); - thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; - const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint16_t * q2 = xb->qs + 4*ib32; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); - uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); - signs = ksigns_iq2xs[q2[2*il+1] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * q3 = xb->qs + 8*ib32; - device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); - uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } - grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); - grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); - signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 8*ib32; - device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); - constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); - reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); - } - grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); - reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); - } -} - -template -void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * signs = qs + QK_K/8; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); - constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); - for (int i = 0; i < 8; ++i) { - reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); - reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); - } -} - -template -void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - const float d = xb->d; - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint16_t * qh = xb->qh; - const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); - const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); - const uint16_t h = qh[ib32] >> 6*il; - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml; - reg[1][i] = dl * (grid1[i] >> 4) + ml; - reg[2][i] = dl * (grid2[i] & 0xf) + ml; - reg[3][i] = dl * (grid2[i] >> 4) + ml; - } -} - -template -void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - device const uint16_t * sc = (device const uint16_t *)xb->scales; - - iq1m_scale_t scale; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const float d = scale.f16; - - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * qh = xb->qh + 2*ib32 + il; - - const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); - const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml1; - reg[1][i] = dl * (grid1[i] >> 4) + ml1; - reg[2][i] = dl * (grid2[i] & 0xf) + ml2; - reg[3][i] = dl * (grid2[i] >> 4) + ml2; - } -} - -template -void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { - device const uint16_t * q4 = (device const uint16_t *)xb->qs; - const float d = xb->d; - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } -} - -template -void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; - const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); - const float d = (float)xb->d * (ls - 32); - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } + kernel_mul_mv_iq4_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } template @@ -5747,36 +5429,26 @@ kernel void kernel_get_rows_i32( // each block_q contains 16*nl weights template -kernel void kernel_mul_mm(device const uchar * src0, - device const uchar * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { +kernel void kernel_mul_mm( + constant ggml_metal_kargs_mul_mm & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - threadgroup T * sa = (threadgroup T *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); + threadgroup T * sa = (threadgroup T *)(shmem); + threadgroup float * sb = (threadgroup float *)(shmem + 4096); - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; - const uint im = tgpig.z; + const int r0 = tgpig.y; + const int r1 = tgpig.x; + const int im = tgpig.z; // if this block is of 64x32 shape or smaller - short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; - short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N; + short n_rows = (args.ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M; + short n_cols = (args.ne1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (args.ne1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N; // a thread shouldn't load data outside of the matrix short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; @@ -5784,39 +5456,41 @@ kernel void kernel_mul_mm(device const uchar * src0, simdgroup_T8x8 ma[4]; simdgroup_float8x8 mb[2]; - simdgroup_float8x8 c_res[8]; - for (int i = 0; i < 8; i++){ - c_res[i] = make_filled_simdgroup_matrix(0.f); + simdgroup_float8x8 mc[8]; + + for (short i = 0; i < 8; i++){ + mc[i] = make_filled_simdgroup_matrix(0.f); } short il = (tiitg % THREAD_PER_ROW); - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const int i12 = im%args.ne12; + const int i13 = im/args.ne12; - uint offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02); - ushort offset1 = il/nl; + uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + short offset1 = il/nl; - device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1; + device const block_q * x = (device const block_q *)(src0 + (r0*BLOCK_SIZE_M + thread_row)*args.nb01 + offset0) + offset1; device const float * y = (device const float *)(src1 - + nb12 * im - + nb11 * (r1 * BLOCK_SIZE_N + thread_col) - + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); + + args.nb13*i13 + + args.nb12*i12 + + args.nb11*(r1 * BLOCK_SIZE_N + thread_col) + + args.nb10*(BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); - for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { + for (int loop_k = 0; loop_k < args.ne00; loop_k += BLOCK_SIZE_K) { // load data and store to threadgroup memory T4x4 temp_a; dequantize_func(x, il, temp_a); threadgroup_barrier(mem_flags::mem_threadgroup); #pragma unroll(16) - for (int i = 0; i < 16; i++) { - *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \ - + (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \ - + (tiitg / THREAD_PER_ROW) % 8 + (i & 7) * 8) = temp_a[i/4][i%4]; + for (short i = 0; i < 16; i++) { + *(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \ + + (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \ + + (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = temp_a[i/4][i%4]; } - *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y); + *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL)*8*32 + 8*(tiitg/THREAD_PER_COL)) = *((device float2x4 *) y); il = (il + 2 < nl) ? il + 2 : il % 2; x = (il < 2) ? x + (2+nl-1)/nl : x; @@ -5825,53 +5499,66 @@ kernel void kernel_mul_mm(device const uchar * src0, threadgroup_barrier(mem_flags::mem_threadgroup); // load matrices from threadgroup memory and conduct outer products - threadgroup T * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); - threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); + threadgroup T * lsma = (sa + THREAD_MAT_M*SG_MAT_SIZE*(sgitg%2)); + threadgroup float * lsmb = (sb + THREAD_MAT_N*SG_MAT_SIZE*(sgitg/2)); #pragma unroll(4) - for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { + for (short ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { #pragma unroll(4) - for (int i = 0; i < 4; i++) { - simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i); + for (short i = 0; i < 4; i++) { + simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); } simdgroup_barrier(mem_flags::mem_none); #pragma unroll(2) - for (int i = 0; i < 2; i++) { - simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i); + for (short i = 0; i < 2; i++) { + simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); } - lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; - lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; + lsma += BLOCK_SIZE_M/SG_MAT_ROW * SG_MAT_SIZE; + lsmb += BLOCK_SIZE_N/SG_MAT_ROW * SG_MAT_SIZE; #pragma unroll(8) - for (int i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); + for (short i = 0; i < 8; i++){ + simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]); } } } - if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) { - device float * C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \ - + (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0; - for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0); + if ((r0 + 1) * BLOCK_SIZE_M <= args.ne0 && (r1 + 1) * BLOCK_SIZE_N <= args.ne1) { + device float * C = (device float *) dst + + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) + \ + (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0; + + for (short i = 0; i < 8; i++) { + simdgroup_store(mc[i], C + 8 * (i%4) + 8 * args.ne0 * (i/4), args.ne0); } } else { // block is smaller than 64x32, we should avoid writing data outside of the matrix threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ - + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; - for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); + threadgroup float * temp_str = ((threadgroup float *) shmem) \ + + 32 * (sgitg&1) + (16 * (sgitg>>1))*BLOCK_SIZE_M; + for (short i = 0; i < 8; i++) { + simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M); } threadgroup_barrier(mem_flags::mem_threadgroup); - device float * C = dst + (BLOCK_SIZE_M * r0) + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0; if (sgitg == 0) { - for (int i = 0; i < n_rows; i++) { - for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { - *(C + i + j * ne0) = *(temp_str + i + j * BLOCK_SIZE_M); + for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { + device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + (r1*BLOCK_SIZE_N + j)*args.ne0 + im*args.ne1*args.ne0; + device float4 * D4 = (device float4 *) D; + + threadgroup float * C = temp_str + (j*BLOCK_SIZE_M); + threadgroup float4 * C4 = (threadgroup float4 *) C; + + int i = 0; + for (; i < n_rows/4; i++) { + *(D4 + i) = *(C4 + i); + } + + i *= 4; + for (; i < n_rows; i++) { + *(D + i) = *(C + i); } } } @@ -5879,36 +5566,37 @@ kernel void kernel_mul_mm(device const uchar * src0, } // same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in rowids +// TODO: this kernel needs to be reimplemented from scratch for better performance template void kernel_mul_mm_id_impl( - device const uchar * src0, - device const uchar * src1, + int32_t ne00, + int32_t ne02, + uint64_t nb01, + uint64_t nb02, + int32_t ne11, + int32_t ne12, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int32_t ne0, + int32_t ne1, + int64_t ne0ne1, + device const char * src0, + device const char * src1, threadgroup ushort2 * rowids, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - int64_t ne1, - int64_t ne0ne1, - threadgroup uchar * shared_memory, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + device char * dst, + threadgroup char * shmem, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - threadgroup half * sa = (threadgroup half *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); + threadgroup half * sa = (threadgroup half *)(shmem); + threadgroup float * sb = (threadgroup float *)(shmem + 4096); - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; + const int r0 = tgpig.y; + const int r1 = tgpig.x; - if (r1 * BLOCK_SIZE_N >= ne1) return; + if (r1*BLOCK_SIZE_N >= ne1) return; // if this block is of 64x32 shape or smaller short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; @@ -5920,9 +5608,9 @@ void kernel_mul_mm_id_impl( simdgroup_half8x8 ma[4]; simdgroup_float8x8 mb[2]; - simdgroup_float8x8 c_res[8]; + simdgroup_float8x8 mc[8]; for (int i = 0; i < 8; i++){ - c_res[i] = make_filled_simdgroup_matrix(0.f); + mc[i] = make_filled_simdgroup_matrix(0.f); } short il = (tiitg % THREAD_PER_ROW); @@ -5960,11 +5648,14 @@ void kernel_mul_mm_id_impl( threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); + #pragma unroll(BLOCK_SIZE_K/8) for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { + #pragma unroll(4) for (int i = 0; i < 4; i++) { simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); } simdgroup_barrier(mem_flags::mem_none); + #pragma unroll(2) for (int i = 0; i < 2; i++) { simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); } @@ -5972,29 +5663,42 @@ void kernel_mul_mm_id_impl( lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; + #pragma unroll(8) for (int i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); + simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]); } } } { threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ + threadgroup float * temp_str = ((threadgroup float *) shmem) \ + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); + simdgroup_store(mc[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); } threadgroup_barrier(mem_flags::mem_threadgroup); - device float * C = dst + (BLOCK_SIZE_M * r0); if (sgitg == 0) { for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { threadgroup const auto & jid = rowids[r1 * BLOCK_SIZE_N + j]; - int joff = jid[0] * ne0 + jid[1] * ne0ne1; - for (int i = 0; i < n_rows; i++) { - *(C + i + joff) = *(temp_str + i + j * BLOCK_SIZE_M); + int64_t joff = jid[0]*ne0 + jid[1]*ne0ne1; + + device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + joff; + device float4 * D4 = (device float4 *) D; + + threadgroup float * C = temp_str + (j*BLOCK_SIZE_M); + threadgroup float4 * C4 = (threadgroup float4 *) C; + + int i = 0; + for (; i < n_rows/4; i++) { + *(D4 + i) = *(C4 + i); + } + + i *= 4; + for (; i < n_rows; i++) { + *(D + i) = *(C + i); } } } @@ -6003,48 +5707,34 @@ void kernel_mul_mm_id_impl( template kernel void kernel_mul_mm_id( - device const uchar * src0s, - device const uchar * src1, - device float * dst, - device const uchar * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mm_id & args, + device const char * src0s, + device const char * src1, + device char * dst, + device const char * ids, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const int32_t i02 = tgpig.z; + tgpig.z = 0; - device const uchar * src0 = src0s + i02*nb02; + device const char * src0 = src0s + i02*args.nb02; // row indices - threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shared_memory + 8192); + threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shmem + 8192); // TODO: parallelize this loop - int64_t _ne1 = 0; - for (ushort ii1 = 0; ii1 < nei1; ii1++) { - for (ushort ii0 = 0; ii0 < nei0; ii0++) { - int32_t id = ((device int32_t *) (ids + ii1*nbi1))[ii0]; + int32_t _ne1 = 0; + for (ushort ii1 = 0; ii1 < args.nei1; ii1++) { + for (ushort ii0 = 0; ii0 < args.nei0; ii0++) { + int32_t id = ((device int32_t *) (ids + ii1*args.nbi1))[ii0]; if (id == i02) { - //if (tiitg == 0) { + if (tiitg == 0) { rowids[_ne1] = ushort2(ii0, ii1); - //} + } _ne1++; } } @@ -6053,23 +5743,23 @@ kernel void kernel_mul_mm_id( threadgroup_barrier(mem_flags::mem_threadgroup); kernel_mul_mm_id_impl( + args.ne00, + args.ne02, + args.nb01, + args.nb02, + args.ne11, + args.ne12, + args.nb10, + args.nb11, + args.nb12, + args.ne0, + _ne1, + (int64_t)args.ne0*args.ne1, src0, src1, rowids, dst, - ne00, - ne02, - nb01, - nb02, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - _ne1, - ne0*ne1, - shared_memory, + shmem, tgpig, tiitg, sgitg); @@ -6085,6 +5775,9 @@ typedef decltype(kernel_get_rows_f) get_rows_f_t; template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f; template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f; +#endif typedef decltype(kernel_get_rows_q) get_rows_q_t; @@ -6116,6 +5809,9 @@ typedef decltype(kernel_mul_mm; template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mm_bf16_f32")]] kernel mat_mm_t kernel_mul_mm; +#endif template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -6144,6 +5840,9 @@ typedef decltype(kernel_mul_mm_id) mat_mm_id_t; template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mm_id_bf16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#endif template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; @@ -6169,180 +5868,110 @@ template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel // typedef void (kernel_mul_mv_impl_t)( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig, + ushort tiisg); typedef void (kernel_mul_mv2_impl_t)( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - int64_t ne10, - int64_t ne12, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg); template void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,ne10,ne11,ne12,nb10,nb11,nb12,ne0,ne1,r2,r3,tgpig,tiisg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiitg, + ushort tiisg, + ushort sgitg) { + impl_fn(args, src0, src1, dst, tgpig, tiisg); } template void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiitg, + ushort tiisg, + ushort sgitg) { + impl_fn(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -typedef decltype(mmv_fn>) mul_mv_impl_fn_t; +typedef decltype(mmv_fn>) mul_mv_impl_fn_t; template kernel void kernel_mul_mv_id( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int iid1 = tgpig.z/nei0; - const int idx = tgpig.z%nei0; + constant ggml_metal_kargs_mul_mv_id & args, + device const char * src0s, + device const char * src1, + device char * dst, + device const char * ids, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const int iid1 = tgpig.z/args.nei0; + const int idx = tgpig.z%args.nei0; tgpig.z = 0; - const int32_t i02 = ((device const int32_t *) (ids + iid1*nbi1))[idx]; + const int32_t i02 = ((device const int32_t *) (ids + iid1*args.nbi1))[idx]; - const int64_t i11 = idx % ne11; + const int64_t i11 = idx % args.ne11; const int64_t i12 = iid1; const int64_t i1 = idx; const int64_t i2 = i12; - device const char * src0_cur = src0s + i02*nb02; - device const char * src1_cur = src1 + i11*nb11 + i12*nb12; - device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; + device const char * src0_cur = src0s + i02*args.nb02; + device const char * src1_cur = src1 + i11*args.nb11 + i12*args.nb12; + + device char * dst_cur = dst + (i1*args.ne0 + i2*args.ne1*args.ne0)*sizeof(float); + + ggml_metal_kargs_mul_mv args0 = { + /*.ne00 =*/ args.ne00, + /*.ne01 =*/ args.ne01, + /*.ne02 =*/ 1, // args.ne02, + /*.nb00 =*/ args.nb00, + /*.nb01 =*/ args.nb01, + /*.nb02 =*/ args.nb02, + /*.nb03 =*/ args.nb02, // args.ne02 == 1 + /*.ne10 =*/ args.ne10, + /*.ne11 =*/ 1, // args.ne11, + /*.ne12 =*/ 1, // args.ne12, + /*.nb10 =*/ args.nb10, + /*.nb11 =*/ args.nb11, + /*.nb12 =*/ args.nb12, + /*.nb13 =*/ args.nb12, // ne12 == 1 + /*.ne0 =*/ args.ne0, + /*.ne1 =*/ 1, // args.ne1, + /*.r2 =*/ 1, + /*.r3 =*/ 1, + }; impl_fn( + args0, /* src0 */ src0_cur, /* src1 */ src1_cur, /* dst */ dst_cur, - /* ne00 */ ne00, - /* ne01 */ ne01, - /* ne02 */ 1,//ne02, - /* nb00 */ nb00, - /* nb01 */ nb01, - /* nb02 */ nb02, - /* ne10 */ ne10, - /* ne11 */ 1,//ne11, - /* ne12 */ 1,//ne12, - /* ne13 */ 1,//ne13, - /* nb10 */ nb10, - /* nb11 */ nb11, - /* nb12 */ nb12, - /* ne0 */ ne0, - /* ne1 */ 1,//ne1, - /* nb1 */ nb1, - /* r2 */ 1, - /* r3 */ 1, - shared_values, + shmem, tgpig, tiitg, tiisg, @@ -6353,6 +5982,9 @@ typedef decltype(kernel_mul_mv_id>>; template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_id_bf16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +#endif template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; @@ -6372,3 +6004,102 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; + +kernel void kernel_pool_2d_max_f32( + device const float * src0, + device float * dst, + constant int32_t & k0, + constant int32_t & k1, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int64_t & IH, + constant int64_t & IW, + constant int64_t & OH, + constant int64_t & OW, + constant int64_t & parallel_elements, + uint gid[[thread_position_in_grid]]) { + + if (gid >= parallel_elements) { + return; + } + + const int idx = gid; + const int I_HW = IH * IW; + const int O_HW = OH * OW; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / OW; + const int cur_ow = idx % O_HW % OW; + + device const float * i_ptr = src0 + nc * I_HW; + device float * o_ptr = dst + nc * O_HW; + + const int start_h = cur_oh * s1 - p1; + const int bh = MAX(0, start_h); + const int eh = MIN(IH, start_h + k1); + const int start_w = cur_ow * s0 - p0; + const int bw = MAX(0, start_w); + const int ew = MIN(IW, start_w + k0); + + float res = -INFINITY; + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + res = MAX(res, i_ptr[i * IW + j]); + } + } + + o_ptr[cur_oh * OW + cur_ow] = res; +} + +kernel void kernel_pool_2d_avg_f32( + device const float * src0, + device float * dst, + constant int32_t & k0, + constant int32_t & k1, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int64_t & IH, + constant int64_t & IW, + constant int64_t & OH, + constant int64_t & OW, + constant int64_t & parallel_elements, + uint gid[[thread_position_in_grid]]) { + + if (gid >= parallel_elements) { + return; + } + + const int idx = gid; + const int I_HW = IH * IW; + const int O_HW = OH * OW; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / OW; + const int cur_ow = idx % O_HW % OW; + + device const float * i_ptr = src0 + nc * I_HW; + device float * o_ptr = dst + nc * O_HW; + + const int start_h = cur_oh * s1 - p1; + const int bh = MAX(0, start_h); + const int eh = MIN(IH, start_h + k1); + const int start_w = cur_ow * s0 - p0; + const int bw = MAX(0, start_w); + const int ew = MIN(IW, start_w + k0); + // const float scale = 1. / ((eh - bh) * (ew - bw)); + const float scale = 1. / (k0 * k1); + + float res = 0; + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + float cur = i_ptr[i * IW + j]; + res += cur * scale; + } + } + + o_ptr[cur_oh * OW + cur_ow] = res; +} diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt new file mode 100644 index 0000000000..f3c0136920 --- /dev/null +++ b/ggml/src/ggml-musa/CMakeLists.txt @@ -0,0 +1,100 @@ +if (NOT EXISTS $ENV{MUSA_PATH}) + if (NOT EXISTS /opt/musa) + set(MUSA_PATH /usr/local/musa) + else() + set(MUSA_PATH /opt/musa) + endif() +else() + set(MUSA_PATH $ENV{MUSA_PATH}) +endif() + +set(CMAKE_C_COMPILER "${MUSA_PATH}/bin/clang") +set(CMAKE_C_EXTENSIONS OFF) +set(CMAKE_CXX_COMPILER "${MUSA_PATH}/bin/clang++") +set(CMAKE_CXX_EXTENSIONS OFF) + +list(APPEND CMAKE_MODULE_PATH "${MUSA_PATH}/cmake") + +find_package(MUSAToolkit) + +if (MUSAToolkit_FOUND) + message(STATUS "MUSA Toolkit found") + + file(GLOB GGML_HEADERS_MUSA "../ggml-cuda/*.cuh") + list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu") + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + endif() + + set_source_files_properties(${GGML_SOURCES_MUSA} PROPERTIES LANGUAGE CXX) + foreach(SOURCE ${GGML_SOURCES_MUSA}) + set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS "-x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22") + endforeach() + + add_library(ggml-musa + ${GGML_HEADERS_MUSA} + ${GGML_SOURCES_MUSA}) + + target_link_libraries(ggml-musa PRIVATE ggml-base) + target_include_directories(ggml-musa PRIVATE . ..) + + # TODO: do not use CUDA definitions for MUSA + target_compile_definitions(ggml PUBLIC GGML_USE_CUDA) + + add_compile_definitions(GGML_USE_MUSA) + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + target_link_libraries(ggml-musa PRIVATE MUSA::musart_static MUSA::mublas_static) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musart MUSA::mublas) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the musa driver lib (libmusa.so) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musa_driver) + endif() +else() + message(FATAL_ERROR "MUSA Toolkit not found") +endif() diff --git a/ggml/src/ggml-opt.cpp b/ggml/src/ggml-opt.cpp new file mode 100644 index 0000000000..7c3e24103a --- /dev/null +++ b/ggml/src/ggml-opt.cpp @@ -0,0 +1,854 @@ +#include "ggml-opt.h" + +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include +#include +#include +#include +#include +#include +#include + +struct ggml_opt_dataset { + struct ggml_context * ctx = nullptr; + ggml_backend_buffer_t buf = nullptr; + struct ggml_tensor * data = nullptr; + struct ggml_tensor * labels = nullptr; + + int64_t ndata = -1; + int64_t ndata_shard = -1; + size_t nbs_data = -1; + size_t nbs_labels = -1; + + std::vector permutation; +}; + +struct ggml_opt_context { + ggml_backend_sched_t backend_sched = nullptr; + ggml_cgraph * allocated_graph = nullptr; + ggml_cgraph * allocated_graph_copy = nullptr; + struct ggml_context * ctx_static = nullptr; + struct ggml_context * ctx_static_cpu = nullptr; + struct ggml_context * ctx_compute = nullptr; + struct ggml_context * ctx_copy = nullptr; + ggml_backend_buffer_t buf_static = nullptr; + ggml_backend_buffer_t buf_static_cpu = nullptr; + std::mt19937 rng; + + struct ggml_tensor * inputs = nullptr; + struct ggml_tensor * outputs = nullptr; + struct ggml_tensor * labels = nullptr; + + struct ggml_tensor * loss = nullptr; + struct ggml_tensor * pred = nullptr; + struct ggml_tensor * ncorrect = nullptr; + + struct ggml_cgraph * gf = nullptr; + struct ggml_cgraph * gb_grad = nullptr; + struct ggml_cgraph * gb_opt = nullptr; + + int64_t iter = 1; + int32_t opt_period = 1; + int32_t opt_i = 0; + bool loss_per_datapoint = false; + + ggml_opt_get_optimizer_params get_opt_pars = nullptr; + void * get_opt_pars_ud = nullptr; + struct ggml_tensor * adamw_params = nullptr; +}; + +struct ggml_opt_result { + int64_t ndata = 0; + std::vector loss; + std::vector pred; + int64_t ncorrect = 0; + + int64_t opt_period = -1; + bool loss_per_datapoint = false; +}; + +// ====== Dataset ====== + +ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, int64_t ndata, int64_t ndata_shard) { + GGML_ASSERT(ne_datapoint > 0); + GGML_ASSERT(ne_label >= 0); + GGML_ASSERT(ndata > 0); + GGML_ASSERT(ndata_shard > 0); + + ggml_opt_dataset_t result = new ggml_opt_dataset; + result->ndata = ndata; + result->ndata_shard = ndata_shard; + + { + struct ggml_init_params params = { + /*.mem_size =*/ 2*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx = ggml_init(params); + } + + result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_datapoint, ndata); + result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata; + + if (ne_label > 0) { + result->labels = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_label, ndata); + result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata; + } else { + result->labels = nullptr; + result->nbs_labels = 0; + } + + result->buf = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx, ggml_backend_cpu_buffer_type()); + + const int64_t nshards = ndata/ndata_shard; + result->permutation.resize(nshards); + for (int64_t i = 0; i < nshards; ++i) { + result->permutation[i] = i; + } + return result; +} + +void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) { + ggml_backend_buffer_free(dataset->buf); + ggml_free(dataset->ctx); + delete dataset; +} + +struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) { + return dataset->data; +} + +struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset) { + return dataset->labels; +} + +void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata) { + GGML_ASSERT(idata <= dataset->ndata); + + if (idata < 0) { + std::shuffle(dataset->permutation.begin(), dataset->permutation.end(), opt_ctx->rng); + return; + } + + GGML_ASSERT(idata % dataset->ndata_shard == 0); + const int64_t ishard_max = idata / dataset->ndata_shard; + std::shuffle(dataset->permutation.begin(), dataset->permutation.begin() + ishard_max, opt_ctx->rng); +} + +void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * data_batch, struct ggml_tensor * labels_batch, int64_t ibatch) { + GGML_ASSERT( data_batch && ggml_is_contiguous(data_batch)); + GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch)); + GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr)); + + const size_t nb_data_batch = ggml_nbytes(data_batch); + GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0); + const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data; + + if (labels_batch) { + const size_t nb_labels_batch = ggml_nbytes(labels_batch); + GGML_ASSERT(nb_labels_batch == shards_per_batch*dataset->nbs_labels); + } + + GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size())); + + for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) { + const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch]; + + const char * ptr_data = (const char *) dataset->data->data + ishard*dataset->nbs_data; + ggml_backend_tensor_set(data_batch, ptr_data, ishard_batch*dataset->nbs_data, dataset->nbs_data); + + if (!labels_batch) { + continue; + } + + const char * ptr_labels = (const char *) dataset->labels->data + ishard*dataset->nbs_labels; + ggml_backend_tensor_set(labels_batch, ptr_labels, ishard_batch*dataset->nbs_labels, dataset->nbs_labels); + } +} + +// ====== Model / Context ====== + +struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) { + GGML_UNUSED(userdata); + + ggml_opt_optimizer_params result; + + result.adamw.alpha = 0.001f; + result.adamw.beta1 = 0.9f; + result.adamw.beta2 = 0.999f; + result.adamw.eps = 1e-8f; + result.adamw.wd = 0.0f; + + return result; +} + +struct ggml_opt_params ggml_opt_default_params( + ggml_backend_sched_t backend_sched, + struct ggml_context * ctx_compute, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs, + enum ggml_opt_loss_type loss_type) { + return { + /*backend_sched =*/ backend_sched, + /*ctx_compute =*/ ctx_compute, + /*inputs =*/ inputs, + /*logits =*/ outputs, + /*loss_type =*/ loss_type, + /*build_type =*/ GGML_OPT_BUILD_TYPE_OPT, + /*opt_period =*/ 1, + /*get_opt_pars =*/ ggml_opt_get_default_optimizer_params, + /*get_opt_pars_ud =*/ nullptr, + }; +} + +static ggml_tensor * map_tensor(std::map & tensor_map, ggml_context * ctx, ggml_tensor * tensor) { + if (!tensor) { + return nullptr; + } + + if (tensor_map.find(tensor) != tensor_map.end()) { + return tensor_map[tensor]; + } + + ggml_tensor * new_tensor = ggml_dup_tensor(ctx, tensor); + tensor_map[tensor] = new_tensor; + + new_tensor->op = tensor->op; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + new_tensor->nb[i] = tensor->nb[i]; + } + new_tensor->flags = tensor->flags; + memcpy(new_tensor->op_params, tensor->op_params, sizeof(tensor->op_params)); + strcpy(new_tensor->name, tensor->name); + new_tensor->data = tensor->data; + new_tensor->buffer = tensor->buffer; + new_tensor->extra = tensor->extra; + new_tensor->view_offs = tensor->view_offs; + new_tensor->view_src = map_tensor(tensor_map, ctx, tensor->view_src); + for (int i = 0; i < GGML_MAX_SRC; i++) { + new_tensor->src[i] = map_tensor(tensor_map, ctx, tensor->src[i]); + } + + return new_tensor; +} + +static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) { + std::map tensor_map; + + ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true); + + for (int i = 0; i < src->n_leafs; i++) { + ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i])); + } + GGML_ASSERT(dst->n_leafs == src->n_leafs); + for (int i = 0; i < src->n_nodes; i++) { + ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i])); + } + GGML_ASSERT(dst->n_nodes == src->n_nodes); + for (int i = 0; i < src->n_nodes; ++i) { + const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]); + const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]); + + GGML_ASSERT(igrad_src != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src)); + GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst)); + + dst->grads[igrad_dst] = src->grads[igrad_src]; + dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src]; + } + + return dst; +} + +static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) { + GGML_ASSERT(graph); + if (opt_ctx->allocated_graph == graph) { + return; + } + + ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph + + { + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ggml_free(opt_ctx->ctx_copy); + opt_ctx->ctx_copy = ggml_init(params); + } + + opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph); + + ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); + opt_ctx->allocated_graph = graph; +} + +ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) { + ggml_opt_context_t result = new struct ggml_opt_context; + result->backend_sched = params.backend_sched; + result->ctx_compute = params.ctx_compute; + result->inputs = params.inputs; + result->outputs = params.outputs; + result->opt_period = params.opt_period; + result->get_opt_pars = params.get_opt_pars; + result->get_opt_pars_ud = params.get_opt_pars_ud; + + GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically"); + GGML_ASSERT(result->opt_period >= 1); + + const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD || + (params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1); + + ggml_set_input(result->inputs); + ggml_set_output(result->outputs); + + result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass. + ggml_build_forward_expand(result->gf, result->outputs); + + int n_param = 0; + for (int i = 0; i < result->gf->n_nodes; ++i) { + if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { + n_param++; + } + } + + { + // The static context is used for: + // - gradients (1 tensor per param if using gradient accumulation) + // - optimizer momenta (2 tensors per param) + // - labels + // - loss + its gradient (up to 5 tensors) + // - pred + // - ncorrect (2 tensors). + const size_t tensors_per_param = (accumulate ? 1 : 0) + (params.build_type == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0); + const size_t size_meta = (tensors_per_param*n_param + 9) * ggml_tensor_overhead(); + struct ggml_init_params params = { + /*.mem_size =*/ size_meta, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx_static = ggml_init(params); + } + { + // The static cpu context is used for: + // - optimizer parameters (1 for the entire context) + const size_t size_meta = 1 * ggml_tensor_overhead(); + struct ggml_init_params params = { + /*.mem_size =*/ size_meta, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx_static_cpu = ggml_init(params); + } + + + switch (params.loss_type) { + case GGML_OPT_LOSS_TYPE_MEAN: { + result->loss = ggml_sum(result->ctx_static, result->outputs); + ggml_set_name(result->loss, "loss_sum"); + const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); + result->loss = ggml_scale(result->ctx_static, result->loss, scale); + ggml_set_name(result->loss, "loss_mean"); + result->loss_per_datapoint = true; + break; + } + case GGML_OPT_LOSS_TYPE_SUM: { + result->loss = ggml_sum(result->ctx_static, result->outputs); + ggml_set_name(result->loss, "loss_sum"); + result->loss_per_datapoint = false; + break; + } + case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: { + result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); + ggml_set_input(result->labels); + ggml_set_name(result->labels, "labels"); + result->loss = ggml_cross_entropy_loss(result->ctx_static, result->outputs, result->labels); + ggml_set_name(result->loss, "loss_cross_entropy"); + if (result->opt_period > 1) { + result->loss = ggml_scale(result->ctx_static, result->loss, 1.0f / result->opt_period); + ggml_set_name(result->loss, "loss_cross_entropy_scaled"); + } + result->loss_per_datapoint = true; + break; + } + case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: { + result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); + ggml_set_input(result->labels); + ggml_set_name(result->labels, "labels"); + result->loss = ggml_sub(result->ctx_static, result->outputs, result->labels); + ggml_set_name(result->loss, "loss_error"); + result->loss = ggml_sqr(result->ctx_static, result->loss); + ggml_set_name(result->loss, "loss_squared_error"); + result->loss = ggml_sum(result->ctx_static, result->loss); + ggml_set_name(result->loss, "loss_sum_squared_error"); + const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); + result->loss = ggml_scale(result->ctx_static, result->loss, scale); + ggml_set_name(result->loss, "loss_mean_squared_error"); + result->loss_per_datapoint = true; + break; + } + } + ggml_set_output(result->loss); + ggml_set_loss(result->loss); + ggml_build_forward_expand(result->gf, result->loss); + + result->pred = ggml_argmax(result->ctx_static, result->outputs); + ggml_set_name(result->pred, "pred"); + ggml_set_output(result->pred); + ggml_build_forward_expand(result->gf, result->pred); + + if (result->labels) { + result->ncorrect = ggml_count_equal(result->ctx_static, result->pred, ggml_argmax(result->ctx_static, result->labels)); + ggml_set_name(result->ncorrect, "ncorrect"); + ggml_set_output(result->ncorrect); + ggml_build_forward_expand(result->gf, result->ncorrect); + } else { + result->ncorrect = nullptr; + } + + if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) { + result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + return result; + } + + // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients. + result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf); + ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate); + + if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) { + result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + ggml_graph_reset(result->gb_grad); + return result; + } + + GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT); + + // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step. + result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad); + + result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7); + ggml_set_input(result->adamw_params); + ggml_set_name(result->adamw_params, "adamw_params"); + + for (int i = result->gf->n_nodes-1; i >= 0; --i) { + struct ggml_tensor * node = result->gb_opt->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node); + + if (node->flags & GGML_TENSOR_FLAG_PARAM) { + struct ggml_tensor * m = ggml_dup_tensor(result->ctx_static, node); + struct ggml_tensor * v = ggml_dup_tensor(result->ctx_static, node); + struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params); + ggml_build_forward_expand(result->gb_opt, opt_step); + } + } + + result->buf_static = ggml_backend_alloc_ctx_tensors( + result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + + result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type()); + + ggml_graph_reset(result->gb_opt); + + return result; +} + +void ggml_opt_free(ggml_opt_context_t opt_ctx) { + if (opt_ctx == nullptr) { + return; + } + ggml_backend_buffer_free(opt_ctx->buf_static); + ggml_backend_buffer_free(opt_ctx->buf_static_cpu); + ggml_free(opt_ctx->ctx_static); + ggml_free(opt_ctx->ctx_static_cpu); + delete opt_ctx; +} + +void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer) { + if (optimizer) { + ggml_graph_reset(opt_ctx->gb_opt); + opt_ctx->iter = 1; + } else { + ggml_graph_reset(opt_ctx->gb_grad); + } +} + +struct ggml_tensor * ggml_opt_inputs(ggml_opt_context_t opt_ctx) { + return opt_ctx->inputs; +} + +struct ggml_tensor * ggml_opt_outputs(ggml_opt_context_t opt_ctx) { + return opt_ctx->outputs; +} + +struct ggml_tensor * ggml_opt_labels(ggml_opt_context_t opt_ctx) { + return opt_ctx->labels; +} + +struct ggml_tensor * ggml_opt_loss(ggml_opt_context_t opt_ctx) { + return opt_ctx->loss; +} + +struct ggml_tensor * ggml_opt_pred(ggml_opt_context_t opt_ctx) { + return opt_ctx->pred; +} + +struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx) { + return opt_ctx->ncorrect; +} + +struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node) { + return ggml_graph_get_grad_acc(opt_ctx->gb_opt, node); +} + +// ====== Optimization Result ====== + +ggml_opt_result_t ggml_opt_result_init() { + return new ggml_opt_result; +} + +void ggml_opt_result_free(ggml_opt_result_t result) { + delete result; +} + +void ggml_opt_result_reset(ggml_opt_result_t result) { + result->ndata = 0; + result->loss.clear(); + result->pred.clear(); + result->ncorrect = 0; +} + +void ggml_opt_result_ndata(ggml_opt_result_t result, int64_t * ndata) { + *ndata = result->ndata; +} + +void ggml_opt_result_loss(ggml_opt_result_t result, double * loss, double * unc) { + const int64_t nbatches = result->loss.size(); // Number of physical batches. + + if (nbatches == 0) { + *loss = 0.0; + *unc = NAN; + return; + } + + double sum = 0.0; + double sum_squared = 0.0; + + for (const float & loss : result->loss) { + // If the loss is per datapoint it was scaled by 1.0f/opt_period for each physical batch. + const float loss_scaled = result->loss_per_datapoint ? loss*result->opt_period : loss; + sum += loss_scaled; + sum_squared += loss_scaled*loss_scaled; + } + + const double mean = sum/nbatches; + *loss = result->loss_per_datapoint ? mean : sum; + + if (!unc) { + return; + } + + if (nbatches < 2) { + *unc = NAN; + return; + } + + const double var_sum = sum_squared/nbatches - mean*mean; // variance without Bessel's correction, i.e. nbatches/(nbatches-1) + *unc = result->loss_per_datapoint ? sqrt(var_sum / (nbatches - 1)) : sqrt(var_sum * nbatches/(nbatches - 1)); +} + +void ggml_opt_result_pred(ggml_opt_result_t result, int32_t * pred) { + for (size_t i = 0; i < result->pred.size(); ++i) { + pred[i] = result->pred[i]; + } +} + +void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc) { + *accuracy = result->ncorrect >= 0 ? double(result->ncorrect) / double(result->ndata) : NAN; + + if (!unc) { + return; + } + + *unc = result->ncorrect >= 0 && result->ndata >= 2 ? + sqrt((*accuracy) * (1.0 - (*accuracy)) / double(result->ndata - 1)) : NAN; +} + +// ====== Computation ====== + +static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_opt_result * result) { + if (graph != opt_ctx->gf) { + struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud); + + GGML_ASSERT(opt_pars.adamw.alpha > 0.0f); + GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f); + GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f); + GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f); + GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f); + GGML_ASSERT(opt_pars.adamw.eps >= 0.0f); + GGML_ASSERT(opt_pars.adamw.wd >= 0.0f); + GGML_ASSERT(opt_pars.adamw.wd <= 1.0f); + + // beta1, beta2 after applying warmup + const float beta1h = 1.0f/(1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter)); + const float beta2h = 1.0f/(1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter)); + + float * adamw_par_data = ggml_get_data_f32(opt_ctx->adamw_params); + adamw_par_data[0] = opt_pars.adamw.alpha; + adamw_par_data[1] = opt_pars.adamw.beta1; + adamw_par_data[2] = opt_pars.adamw.beta2; + adamw_par_data[3] = opt_pars.adamw.eps; + adamw_par_data[4] = opt_pars.adamw.wd; + adamw_par_data[5] = beta1h; + adamw_par_data[6] = beta2h; + } + + ggml_opt_alloc_graph(opt_ctx, graph); + ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); + opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt; + + if (!result) { + return; + } + + if (result->ndata == 0) { + result->loss_per_datapoint = opt_ctx->loss_per_datapoint; + result->opt_period = opt_ctx->opt_period; + } else { + GGML_ASSERT(result->loss_per_datapoint == opt_ctx->loss_per_datapoint); + GGML_ASSERT(result->opt_period == opt_ctx->opt_period); + } + + const int64_t ndata = opt_ctx->outputs->ne[1]; + GGML_ASSERT(result->ndata == ndata*int64_t(result->loss.size()) && "varying batch size not supported"); + result->ndata += ndata; + + GGML_ASSERT(ggml_is_scalar(opt_ctx->loss)); + GGML_ASSERT(opt_ctx->loss->type == GGML_TYPE_F32); + float loss; + ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss)); + result->loss.push_back(loss); + + GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32); + std::vector pred(ndata); + ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred)); + result->pred.insert(result->pred.end(), pred.begin(), pred.end()); + + if (!opt_ctx->labels || result->ncorrect < 0) { + result->ncorrect = -1; + return; + } + + GGML_ASSERT(ggml_is_scalar(opt_ctx->ncorrect)); + GGML_ASSERT(opt_ctx->ncorrect->type == GGML_TYPE_I64); + int64_t ncorrect; + ggml_backend_tensor_get(opt_ctx->ncorrect, &ncorrect, 0, ggml_nbytes(opt_ctx->ncorrect)); + result->ncorrect += ncorrect; +} + +void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gf, result); +} + +void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { + if (opt_ctx->opt_period == 1) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); + return; + } + + const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period; + if (opt_i_next == 0) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); + ggml_opt_reset(opt_ctx, /*optimizer =*/ false); + } else { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_grad, result); + } + opt_ctx->opt_i = opt_i_next; +} + +// ====== High-Level Functions ====== + +void ggml_opt_epoch( + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval) { + struct ggml_tensor * inputs = ggml_opt_inputs(opt_ctx); + struct ggml_tensor * labels = ggml_opt_labels(opt_ctx); + struct ggml_tensor * data = ggml_opt_dataset_data(dataset); + GGML_ASSERT(data->ne[0] == inputs->ne[0]); + + const int64_t ndata = data->ne[1]; + const int64_t ndata_batch = inputs->ne[1]; + + GGML_ASSERT(data->ne[1] % inputs->ne[1] == 0); + const int64_t nbatches = ndata/ndata_batch; + + idata_split = idata_split < 0 ? ndata : idata_split; + GGML_ASSERT(idata_split % ndata_batch == 0); + const int64_t ibatch_split = idata_split / ndata_batch; + + int64_t ibatch = 0; + int64_t t_loop_start = ggml_time_us(); + for (; ibatch < ibatch_split; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); + ggml_opt_forward_backward(opt_ctx, result_train); + if (callback_train) { + callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start); + } + } + t_loop_start = ggml_time_us(); + for (; ibatch < nbatches; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); + ggml_opt_forward(opt_ctx, result_eval); + if (callback_eval) { + callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start); + } + } +} + +void ggml_opt_epoch_callback_progress_bar( + bool train, + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + int64_t ibatch, + int64_t ibatch_max, + int64_t t_start_us) { + fprintf(stderr, "%s[", train ? "train: " : "val: "); + + constexpr int64_t bar_length = 25; + for (int64_t j = 0; j < bar_length; ++j) { + const int64_t ibatch_j = ibatch_max * j/bar_length; + if (ibatch_j < ibatch) { + fprintf(stderr, "="); + } else if (ibatch_max * (j - 1)/bar_length < ibatch) { + fprintf(stderr, ">"); + } else { + fprintf(stderr, " "); + } + } + + const int64_t batch_size = ggml_opt_inputs(opt_ctx)->ne[1]; + const int64_t idata = ibatch*batch_size; + const int64_t idata_max = ibatch_max*batch_size; + + double loss; + double loss_unc; + ggml_opt_result_loss(result, &loss, &loss_unc); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(result, &accuracy, &accuracy_unc); + + const int64_t t_ibatch_us = ggml_time_us() - t_start_us; + int64_t t_ibatch_s = t_ibatch_us / 1000000; + const int64_t t_ibatch_h = t_ibatch_s / 3600; + t_ibatch_s -= t_ibatch_h * 3600; + const int64_t t_ibatch_m = t_ibatch_s / 60; + t_ibatch_s -= t_ibatch_m * 60; + + const int64_t t_eta_us = t_ibatch_us * (ibatch_max - ibatch)/ibatch; + int64_t t_eta_s = t_eta_us / 1000000; + const int64_t t_eta_h = t_eta_s / 3600; + t_eta_s -= t_eta_h * 3600; + const int64_t t_eta_m = t_eta_s / 60; + t_eta_s -= t_eta_m * 60; + + fprintf(stderr, "| data=%06" PRId64 "/%06" PRId64 ", loss=%.6lf+-%.6lf, accuracy=%.2lf+-%.2lf%%, " + "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 ", ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 "]\r", + idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc, + t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s); + if (ibatch == ibatch_max) { + fprintf(stderr, "\n"); + } + fflush(stderr); + + GGML_UNUSED(dataset); +} + +void ggml_opt_fit( + ggml_backend_sched_t backend_sched, + ggml_context * ctx_compute, + ggml_tensor * inputs, + ggml_tensor * outputs, + ggml_opt_dataset_t dataset, + enum ggml_opt_loss_type loss_type, + ggml_opt_get_optimizer_params get_opt_pars, + int64_t nepoch, + int64_t nbatch_logical, + float val_split, + bool silent) { + ggml_time_init(); + const int64_t t_start_us = ggml_time_us(); + + const int64_t ndata = ggml_opt_dataset_data(dataset)->ne[1]; + const int64_t nbatch_physical = inputs->ne[1]; + GGML_ASSERT(ndata % nbatch_logical == 0); + GGML_ASSERT(nbatch_logical % nbatch_physical == 0); + + const int64_t opt_period = nbatch_logical / nbatch_physical; + const int64_t nbatches_logical = ndata / nbatch_logical; + + GGML_ASSERT(val_split >= 0.0f); + GGML_ASSERT(val_split < 1.0f); + const int64_t ibatch_split = int64_t(((1.0f - val_split) * nbatches_logical)) * opt_period; // train <-> val split index (physical) + const int64_t idata_split = ibatch_split * nbatch_physical; + + int64_t epoch = 1; + + ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); + params.opt_period = opt_period; + params.get_opt_pars = get_opt_pars; + params.get_opt_pars_ud = &epoch; + ggml_opt_context_t opt_ctx = ggml_opt_init(params); + + // Shuffling the data is generally useful but there is only a point if not all data is used in a single batch. + if (nbatch_logical < ndata) { + ggml_opt_dataset_shuffle(opt_ctx, dataset, -1); // Shuffle all data (train + validation). + } + + ggml_opt_result_t result_train = ggml_opt_result_init(); + ggml_opt_result_t result_val = ggml_opt_result_init(); + + ggml_opt_epoch_callback epoch_callback = silent ? nullptr : ggml_opt_epoch_callback_progress_bar; + + for (; epoch <= nepoch; ++epoch) { + if (nbatch_logical < idata_split) { + ggml_opt_dataset_shuffle(opt_ctx, dataset, idata_split); + } + + ggml_opt_result_reset(result_train); + ggml_opt_result_reset(result_val); + + if (!silent) { + fprintf(stderr, "%s: epoch %04" PRId64 "/%04" PRId64 ":\n", __func__, epoch, nepoch); + } + ggml_opt_epoch(opt_ctx, dataset, result_train, result_val, idata_split, epoch_callback, epoch_callback); + if (!silent) { + fprintf(stderr, "\n"); + } + } + + if (!silent) { + int64_t t_total_s = (ggml_time_us() - t_start_us) / 1000000; + const int64_t t_total_h = t_total_s / 3600; + t_total_s -= t_total_h * 3600; + const int64_t t_total_m = t_total_s / 60; + t_total_s -= t_total_m * 60; + fprintf(stderr, "%s: training took %02" PRId64 ":%02" PRId64 ":%02" PRId64 "\n", __func__, t_total_h, t_total_m, t_total_s); + } + + ggml_opt_free(opt_ctx); + ggml_opt_result_free(result_train); + ggml_opt_result_free(result_val); +} diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index 7aa6dce890..7301a9c6ca 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -3,8 +3,8 @@ #include "ggml-quants.h" #include "ggml-impl.h" -#include "ggml-cpu-impl.h" - +#include "ggml-cpu/ggml-cpu-impl.h" +#include "ggml-cpu.h" #include #include @@ -27,643 +27,6 @@ #define UNUSED GGML_UNUSED -// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 -#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -// multiply int8_t, add results pairwise twice -static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { - // Get absolute values of x vectors - const __m128i ax = _mm_sign_epi8(x, x); - // Sign the values of the y vectors - const __m128i sy = _mm_sign_epi8(y, x); - // Perform multiplication and create 16-bit values - const __m128i dot = _mm_maddubs_epi16(ax, sy); - const __m128i ones = _mm_set1_epi16(1); - return _mm_madd_epi16(ones, dot); -} - -#if __AVX__ || __AVX2__ || __AVX512F__ -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = _mm256_extractf128_ps(x, 1); - res = _mm_add_ps(res, _mm256_castps256_ps128(x)); - res = _mm_add_ps(res, _mm_movehl_ps(res, res)); - res = _mm_add_ss(res, _mm_movehdup_ps(res)); - return _mm_cvtss_f32(res); -} - -// horizontally add 8 int32_t -static inline int hsum_i32_8(const __m256i a) { - const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); - const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); - const __m128i sum64 = _mm_add_epi32(hi64, sum128); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -// horizontally add 4 int32_t -static inline int hsum_i32_4(const __m128i a) { - const __m128i hi64 = _mm_unpackhi_epi64(a, a); - const __m128i sum64 = _mm_add_epi32(hi64, a); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -#if defined(__AVX2__) || defined(__AVX512F__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m256i shuf_mask = _mm256_set_epi64x( - 0x0303030303030303, 0x0202020202020202, - 0x0101010101010101, 0x0000000000000000); - __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); - const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytes = _mm256_or_si256(bytes, bit_mask); - return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); - const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); - const __m256i lowMask = _mm256_set1_epi8( 0xF ); - return _mm256_and_si256(lowMask, bytes); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m256i x) { - const __m256i ones = _mm256_set1_epi16(1); - const __m256i summed_pairs = _mm256_madd_epi16(ones, x); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Perform multiplication and create 16-bit values - const __m256i dot = _mm256_maddubs_epi16(ax, sy); - return sum_i16_pairs_float(dot); -#endif -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { -#if __AVXVNNIINT8__ - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Get absolute values of x vectors - const __m256i ax = _mm256_sign_epi8(x, x); - // Sign the values of the y vectors - const __m256i sy = _mm256_sign_epi8(y, x); - return mul_sum_us8_pairs_float(ax, sy); -#endif -} - -static inline __m128i packNibbles( __m256i bytes ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh -#if __AVX512F__ - const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 - bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh - return _mm256_cvtepi16_epi8(bytes); // abcd_efgh -#else - const __m256i lowByte = _mm256_set1_epi16( 0xFF ); - __m256i high = _mm256_andnot_si256( lowByte, bytes ); - __m256i low = _mm256_and_si256( lowByte, bytes ); - high = _mm256_srli_epi16( high, 4 ); - bytes = _mm256_or_si256( low, high ); - - // Compress uint16_t lanes into bytes - __m128i r0 = _mm256_castsi256_si128( bytes ); - __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); - return _mm_packus_epi16( r0, r1 ); -#endif -} -#elif defined(__AVX__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); - const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); - __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); - __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); - const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytesl = _mm_or_si128(bytesl, bit_mask); - bytesh = _mm_or_si128(bytesh, bit_mask); - bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); - bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); - return MM256_SET_M128I(bytesh, bytesl); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - // Load 16 bytes from memory - __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); - __m128i tmph = _mm_srli_epi16(tmpl, 4); - const __m128i lowMask = _mm_set1_epi8(0xF); - tmpl = _mm_and_si128(lowMask, tmpl); - tmph = _mm_and_si128(lowMask, tmph); - return MM256_SET_M128I(tmph, tmpl); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { - const __m128i ones = _mm_set1_epi16(1); - const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); - const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); - const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { - const __m128i axl = _mm256_castsi256_si128(ax); - const __m128i axh = _mm256_extractf128_si256(ax, 1); - const __m128i syl = _mm256_castsi256_si128(sy); - const __m128i syh = _mm256_extractf128_si256(sy, 1); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { - const __m128i xl = _mm256_castsi256_si128(x); - const __m128i xh = _mm256_extractf128_si256(x, 1); - const __m128i yl = _mm256_castsi256_si128(y); - const __m128i yh = _mm256_extractf128_si256(y, 1); - // Get absolute values of x vectors - const __m128i axl = _mm_sign_epi8(xl, xl); - const __m128i axh = _mm_sign_epi8(xh, xh); - // Sign the values of the y vectors - const __m128i syl = _mm_sign_epi8(yl, xl); - const __m128i syh = _mm_sign_epi8(yh, xh); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh - const __m128i lowByte = _mm_set1_epi16( 0xFF ); - __m128i high = _mm_andnot_si128( lowByte, bytes1 ); - __m128i low = _mm_and_si128( lowByte, bytes1 ); - high = _mm_srli_epi16( high, 4 ); - bytes1 = _mm_or_si128( low, high ); - high = _mm_andnot_si128( lowByte, bytes2 ); - low = _mm_and_si128( lowByte, bytes2 ); - high = _mm_srli_epi16( high, 4 ); - bytes2 = _mm_or_si128( low, high ); - - return _mm_packus_epi16( bytes1, bytes2); -} - -static inline __m128i mul_add_epi8_sse(const __m128i x, const __m128i y) { - const __m128i ax = _mm_sign_epi8(x, x); - const __m128i sy = _mm_sign_epi8(y, x); - return _mm_maddubs_epi16(ax, sy); -} -#endif -#elif defined(__SSSE3__) -// horizontally add 4x4 floats -static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { - __m128 res_0 =_mm_hadd_ps(a, b); - __m128 res_1 =_mm_hadd_ps(c, d); - __m128 res =_mm_hadd_ps(res_0, res_1); - res =_mm_hadd_ps(res, res); - res =_mm_hadd_ps(res, res); - - return _mm_cvtss_f32(res); -} -#endif // __AVX__ || __AVX2__ || __AVX512F__ -#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) - -#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) -#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s -#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) -#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) -#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) -#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) -#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) -#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) -#define B8(c,s ) B7(c,s, c), B7(c,s, s) - -// precomputed tables for expanding 8bits to 8 bytes: -static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 -static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 -#endif - -#if defined(__loongarch_asx) - -#ifdef __clang__ -#define VREGS_PREFIX "$vr" -#define XREGS_PREFIX "$xr" -#else // GCC -#define VREGS_PREFIX "$f" -#define XREGS_PREFIX "$f" -#endif -#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" -// Convert __m128i to __m256i -static inline __m256i ____m256i(__m128i in) { - __m256i out = __lasx_xvldi(0); - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " XREGS_PREFIX"\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " VREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - : [out] "+f" (out) : [in] "f" (in) - ); - return out; -} -// Convert two __m128i to __m256i -static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { - __m256i out; - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".ifnc %[out], %[hi] \n\t" - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " XREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" - " xvori.b $xr\\i, $xr\\j, 0 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".endif \n\t" - : [out] "=f" (out), [hi] "+f" (inhi) - : [lo] "f" (inlo) - ); - return out; -} -// Convert __m256i low part to __m128i -static inline __m128i lasx_extracti128_lo(__m256i in) { - __m128i out; - __asm__ volatile ( - ".ifnc %[out], %[in] \n\t" - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " XREGS_PREFIX "\\j \n\t" - " vori.b $vr\\i, $vr\\j, 0 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".endif \n\t" - : [out] "=f" (out) : [in] "f" (in) - ); - return out; -} -// Convert __m256i high part to __m128i -static inline __m128i lasx_extracti128_hi(__m256i in) { - __m128i out; - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " XREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - : [out] "=f" (out) : [in] "f" (in) - ); - return out; -} - -static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { - v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; - return (__m256i)__ret; -} - -static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { - v4i32 __ret = {d, c, b, a}; - return (__m128i)__ret; -} - -static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { - v4i64 __ret = {d, c, b, a}; - return (__m256i)__ret; -} - -static __m256i lasx_insertf128( __m128i x, __m128i y) { - return lasx_set_q(x, y); -} - -static __m128i lsx_shuffle_b(__m128i a, __m128i b) { - __m128i mask_f, zero, tmp0, tmp2, mask; - int f = 0x8f; - mask_f = __lsx_vreplgr2vr_b(f); - zero = __lsx_vldi(0); - tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits - tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive - mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask - tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones - return __lsx_vshuf_b(a, zero, tmp2); -} - -static __m256i lasx_shuffle_b(__m256i a, __m256i b) { - __m256i mask_f, zero, tmp0, tmp2, mask; - int f = 0x8f; - mask_f = __lasx_xvreplgr2vr_b(f); - zero = __lasx_xvldi(0); - tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits - tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive - mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask - tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones - return __lasx_xvshuf_b(a, zero, tmp2); -} - -static __m256i lasx_extu8_16(__m128i a) { - __m128i zero = __lsx_vldi(0); - __m128i vlo = __lsx_vilvl_b(zero, a); - __m128i vhi = __lsx_vilvh_b(zero, a); - return lasx_set_q(vhi, vlo); -} - -static __m256i lasx_ext8_16(__m128i a) { - __m128i sign = __lsx_vslti_b(a, 0); - __m128i vlo = __lsx_vilvl_b(sign, a); - __m128i vhi = __lsx_vilvh_b(sign, a); - return lasx_set_q(vhi, vlo); -} - -static __m256i lasx_ext16_32(__m128i a) { - __m256i tmp1; - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); - return tmp1; -} - -static __m128i lasx_extracti128( __m256i a, int pos) { - __m128i ret; - if( pos == 0) - { - ret = lasx_extracti128_lo(a); - } else { - ret = lasx_extracti128_hi(a); - } - return ret; -} - -static __m128 lasx_extractf128( __m256 a, int pos) { - __m128 ret; - if( pos == 0) - { - ret = (__m128)lasx_extracti128_lo((__m256i)a); - } else { - ret = (__m128)lasx_extracti128_hi((__m256i)a); - } - return ret; -} - -static __m128i lsx_hadd_h(__m128i a, __m128i b) { - __m128i tmp1 = __lsx_vpickev_h(b, a); - __m128i tmp2 = __lsx_vpickod_h(b, a); - return __lsx_vadd_h(tmp1, tmp2); -} - -static __m128i lsx_hadd_w(__m128i a, __m128i b) { - __m128i tmp1 = __lsx_vpickev_w(b, a); - __m128i tmp2 = __lsx_vpickod_w(b, a); - return __lsx_vadd_w(tmp1, tmp2); -} - -static __m128 lsx_hadd_s(__m128 a, __m128 b) { - __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); - __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); - - return __lsx_vfadd_s(tmp1, tmp2); -} - -static __m256i lasx_maddubs_h(__m256i a, __m256i b) { - __m256i tmp1, tmp2; - tmp1 = __lasx_xvmulwev_h_b(a, b); - tmp2 = __lasx_xvmulwod_h_b(a, b); - return __lasx_xvsadd_h(tmp1, tmp2); -} - -static __m256i lasx_madd_h(__m256i a, __m256i b) { - __m256i tmp1, tmp2; - tmp1 = __lasx_xvmulwev_w_h(a, b); - tmp2 = __lasx_xvmulwod_w_h(a, b); - return __lasx_xvadd_w(tmp1, tmp2); -} - -static __m256i lasx_packs_w(__m256i a, __m256i b) { - __m256i tmp, tmp1; - tmp = __lasx_xvsat_w(a, 15); - tmp1 = __lasx_xvsat_w(b, 15); - return __lasx_xvpickev_h(tmp1, tmp); -} - -static __m256i lasx_packs_h(__m256i a, __m256i b) { - __m256i tmp, tmp1; - tmp = __lasx_xvsat_h(a, 7); - tmp1 = __lasx_xvsat_h(b, 7); - return __lasx_xvpickev_b(tmp1, tmp); -} - -static __m128i lsx_packs_w(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_w(a, 15); - tmp1 = __lsx_vsat_w(b, 15); - return __lsx_vpickev_h(tmp1, tmp); -} - -static __m128i lsx_packs_h(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_h(a, 7); - tmp1 = __lsx_vsat_h(b, 7); - return __lsx_vpickev_b(tmp1, tmp); -} - -static __m128i lsx_packus_h(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_hu(a, 7); - tmp1 = __lsx_vsat_hu(b, 7); - return __lsx_vpickev_b(tmp1, tmp); -} - - -static __m128i lsx_maddubs_h(__m128i a, __m128i b) { - __m128i tmp1, tmp2; - tmp1 = __lsx_vmulwev_h_b(a, b); - tmp2 = __lsx_vmulwod_h_b(a, b); - return __lsx_vsadd_h(tmp1, tmp2); -} - -static __m128i lsx_madd_h(__m128i a, __m128i b) { - __m128i tmp1, tmp2; - tmp1 = __lsx_vmulwev_w_h(a, b); - tmp2 = __lsx_vmulwod_w_h(a, b); - return __lsx_vadd_w(tmp1, tmp2); -} - -// multiply int8_t, add results pairwise twice -static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { - // Get absolute values of x vectors - const __m128i ax = __lsx_vsigncov_b(x, x); - // Sign the values of the y vectors - const __m128i sy = __lsx_vsigncov_b(x, y); - // Perform multiplication and create 16-bit values - const __m128i dot = lsx_maddubs_h(ax, sy); - const __m128i ones = __lsx_vreplgr2vr_h(1); - return lsx_madd_h(ones, dot); -} - -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = lasx_extractf128(x, 1); - ft_union tmp; - res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); - res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); - res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); - tmp.i = __lsx_vpickve2gr_w(res, 0); - return tmp.f; -} - -// horizontally add 8 int32_t -static inline int hsum_i32_8(const __m256i a) { - - __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); - __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); - - __m128i tmp1_128 = lasx_extracti128_lo(tmp1); - __m128i tmp2_128 = lasx_extracti128_lo(tmp2); - - __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); - - __m128i ev = __lsx_vpickev_w(sum128, sum128); - __m128i od = __lsx_vpickod_w(sum128, sum128); - __m128i sum64 = __lsx_vadd_w(ev, od); - - int sum64_1, sum64_2; - sum64_1 = __lsx_vpickve2gr_w(sum64, 0); - sum64_2 = __lsx_vpickve2gr_w(sum64, 1); - - return sum64_1 + sum64_2; -} - -// horizontally add 4 int32_t -static inline int hsum_i32_4(const __m128i a) { - __m128i ev = __lsx_vpickev_w(a, a); - __m128i od = __lsx_vpickod_w(a, a); - __m128i sum64 = __lsx_vadd_w(ev, od); - - int sum64_1, sum64_2; - sum64_1 = __lsx_vpickve2gr_w(sum64, 0); - sum64_2 = __lsx_vpickve2gr_w(sum64, 1); - - return sum64_1 + sum64_2; -} - -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m256i shuf_mask = lasx_set_d( - 0x0303030303030303, 0x0202020202020202, - 0x0101010101010101, 0x0000000000000000); - - __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); - const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); - bytes = __lasx_xvor_v(bytes, bit_mask); - return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { - const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); - __m128i hi = __lsx_vsrli_h(lo, 4); - return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m256i x) { - __m256i v = __lasx_xvpackod_h(x, x); - __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); - return __lasx_xvffint_s_w(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { - // Perform multiplication and create 16-bit values - const __m256i dot = lasx_maddubs_h(ax, sy); - return sum_i16_pairs_float(dot); -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { - - // Get absolute values of x vectors - const __m256i ax = __lasx_xvsigncov_b(x, x); - // Sign the values of the y vectors - const __m256i sy = __lasx_xvsigncov_b(x, y); - - return mul_sum_us8_pairs_float(ax, sy); -} - -static inline __m128i packNibbles( __m256i bytes ) { - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh - const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); - __m256i high = __lasx_xvandn_v(lowByte, bytes); - __m256i low = __lasx_xvand_v(lowByte, bytes); - high = __lasx_xvsrli_h(high, 4); - bytes = __lasx_xvor_v(low, high); - // Compress uint16_t lanes into bytes - __m128i *r0 = (__m128i *)&bytes; - __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); - __m128i *r1 = (__m128i *)&tmp_h128; - - __m128i zero = __lsx_vldi(0); - __m128i tmp, tmp2, tmp3; - - tmp = __lsx_vmax_h(zero, *r0); - tmp2 = __lsx_vsat_hu(tmp, 7); - - tmp = __lsx_vmax_h(zero, *r1); - tmp3 = __lsx_vsat_hu(tmp, 7); - return __lsx_vpickev_b(tmp3, tmp2); -} -#endif //__loongarch_asx - // reference implementation for deterministic creation of model files void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, int64_t k) { static const int qk = QK4_0; @@ -702,11 +65,6 @@ void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, in } } -void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q4_0_ref(x, y, k); -} - - void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, int64_t k) { const int qk = QK4_1; @@ -744,10 +102,6 @@ void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, in } } -void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q4_1_ref(x, y, k); -} - void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, int64_t k) { static const int qk = QK5_0; @@ -792,10 +146,6 @@ void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, in } } -void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q5_0_ref(x, y, k); -} - void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, int64_t k) { const int qk = QK5_1; @@ -840,10 +190,6 @@ void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, in } } -void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q5_1_ref(x, y, k); -} - // reference implementation for deterministic creation of model files void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, int64_t k) { assert(k % QK8_0 == 0); @@ -870,291 +216,6 @@ void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, in } } -void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - } - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - } - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = maxScalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_0); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - } - -#elif defined(__POWER9_VECTOR__) - for (int i = 0; i < nb; i++) { - vector float srcv [8]; - vector float asrcv[8]; - vector float amaxv[8]; - vector signed int vi[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(vec_extract(amaxv[0], 0), - vec_extract(amaxv[0], 1)), - MAX(vec_extract(amaxv[0], 2), - vec_extract(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - const vector float vid = vec_splats(id); - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const vector float v = vec_round(vec_mul(srcv[j], vid)); - vi[j] = vec_cts(v, 0); - } - vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); - vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); - } - -#elif defined(__loongarch_asx) - for (int i = 0; i < nb; i++) { - ft_union fi; - __m256 v0 = (__m256)__lasx_xvld( x , 0); - __m256 v1 = (__m256)__lasx_xvld( x , 32); - __m256 v2 = (__m256)__lasx_xvld( x , 64); - __m256 v3 = (__m256)__lasx_xvld( x , 96); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); - __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); - - __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); - __m128 tmp = max4; - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); - fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); - const float max_scalar = fi.f; - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); - - // Apply the multiplier - v0 = __lasx_xvfmul_s( v0, mul ); - v1 = __lasx_xvfmul_s( v1, mul ); - v2 = __lasx_xvfmul_s( v2, mul ); - v3 = __lasx_xvfmul_s( v3, mul ); - - // Round to nearest integer - __m256i i0 = __lasx_xvftintrne_w_s( v0 ); - __m256i i1 = __lasx_xvftintrne_w_s( v1 ); - __m256i i2 = __lasx_xvftintrne_w_s( v2 ); - __m256i i3 = __lasx_xvftintrne_w_s( v3 ); - - __m128i ni0 = lasx_extracti128( i0, 0 ); - __m128i ni1 = lasx_extracti128( i0, 1); - __m128i ni2 = lasx_extracti128( i1, 0); - __m128i ni3 = lasx_extracti128( i1, 1); - __m128i ni4 = lasx_extracti128( i2, 0); - __m128i ni5 = lasx_extracti128( i2, 1); - __m128i ni6 = lasx_extracti128( i3, 0); - __m128i ni7 = lasx_extracti128( i3, 1); - - // Convert int32 to int16 - ni0 = lsx_packs_w( ni0, ni1 ); - ni2 = lsx_packs_w( ni2, ni3 ); - ni4 = lsx_packs_w( ni4, ni5 ); - ni6 = lsx_packs_w( ni6, ni7 ); - // Convert int16 to int8 - ni0 = lsx_packs_h( ni0, ni2 ); - ni4 = lsx_packs_h( ni4, ni6 ); - - __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); - __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); - - } -#else - GGML_UNUSED(nb); - // scalar - quantize_row_q8_0_ref(x, y, k); -#endif -} - // reference implementation for deterministic creation of model files void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, int64_t k) { assert(QK8_1 == 32); @@ -1191,334 +252,6 @@ void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, in } } -void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK8_1 == 0); - const int nb = k / QK8_1; - - block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - int32x4_t accv = vdupq_n_s32(0); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - - accv = vaddq_s32(accv, vi); - } - - y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv)); - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - v128_t accv = wasm_i32x4_splat(0); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - - accv = wasm_i32x4_add(accv, vi); - } - - y[i].s = GGML_FP32_TO_FP16( - d * (wasm_i32x4_extract_lane(accv, 0) + - wasm_i32x4_extract_lane(accv, 1) + - wasm_i32x4_extract_lane(accv, 2) + - wasm_i32x4_extract_lane(accv, 3))); - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float max_scalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Compute the sum of the quants and set y[i].s - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)))); - - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Compute the sum of the quants and set y[i].s - const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); - const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1))); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_1); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - - // compute sum for y[i].s - vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); - vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); - - // set y[i].s - int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); - y[i].s = GGML_FP32_TO_FP16(sum*d); - } - -#elif defined(__POWER9_VECTOR__) - for (int i = 0; i < nb; i++) { - vector float srcv [8]; - vector float asrcv[8]; - vector float amaxv[8]; - vector signed int vi[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(vec_extract(amaxv[0], 0), - vec_extract(amaxv[0], 1)), - MAX(vec_extract(amaxv[0], 2), - vec_extract(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - const vector float vid = vec_splats(id); - - y[i].d = GGML_FP32_TO_FP16(d); - - vector int accv = vec_splats(0); - - for (int j = 0; j < 8; j++) { - const vector float v = vec_round(vec_mul(srcv[j], vid)); - vi[j] = vec_cts(v, 0); - - accv = vec_add(accv, vi[j]); - } - vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); - vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); - - accv = vec_add(accv, vec_sld(accv, accv, 4)); - accv = vec_add(accv, vec_sld(accv, accv, 8)); - y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); - } - -#elif defined(__loongarch_asx) - for (int i = 0; i < nb; i++) { - ft_union ft; - __m256 v0 = (__m256)__lasx_xvld( x , 0 ); - __m256 v1 = (__m256)__lasx_xvld( x , 32 ); - __m256 v2 = (__m256)__lasx_xvld( x , 64 ); - __m256 v3 = (__m256)__lasx_xvld( x , 96 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); - __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); - - __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); - __m128 tmp = max4; - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); - ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); - const float max_scalar = ft.f; - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = __lasx_xvreplfr2vr_s( id ); - - // Apply the multiplier - v0 = __lasx_xvfmul_s( v0, mul ); - v1 = __lasx_xvfmul_s( v1, mul ); - v2 = __lasx_xvfmul_s( v2, mul ); - v3 = __lasx_xvfmul_s( v3, mul ); - - // Round to nearest integer - __m256i i0 = __lasx_xvftintrne_w_s( v0 ); - __m256i i1 = __lasx_xvftintrne_w_s( v1 ); - __m256i i2 = __lasx_xvftintrne_w_s( v2 ); - __m256i i3 = __lasx_xvftintrne_w_s( v3 ); - - __m128i ni0 = lasx_extracti128(i0, 0); - __m128i ni1 = lasx_extracti128( i0, 1); - __m128i ni2 = lasx_extracti128( i1, 0); - __m128i ni3 = lasx_extracti128( i1, 1); - __m128i ni4 = lasx_extracti128( i2, 0 ); - __m128i ni5 = lasx_extracti128( i2, 1); - __m128i ni6 = lasx_extracti128( i3, 0); - __m128i ni7 = lasx_extracti128( i3, 1); - - // Compute the sum of the quants and set y[i].s - const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); - const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); - - // Convert int32 to int16 - ni0 = lsx_packs_w( ni0, ni1 ); - ni2 = lsx_packs_w( ni2, ni3 ); - ni4 = lsx_packs_w( ni4, ni5 ); - ni6 = lsx_packs_w( ni6, ni7 ); - // Convert int16 to int8 - ni0 = lsx_packs_h( ni0, ni2 ); - ni4 = lsx_packs_h( ni4, ni6 ); - - __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); - __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); - } -#else - GGML_UNUSED(nb); - // scalar - quantize_row_q8_1_ref(x, y, k); -#endif -} - void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int64_t k) { static const int qk = QK4_0; @@ -2008,10 +741,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 } } -void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) { - quantize_row_q2_K_ref(x, vy, k); -} - static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, float rmin, float rdelta, int nstep, bool use_mad) { @@ -2374,10 +1103,6 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6 } } -void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { - quantize_row_q3_K_ref(x, vy, k); -} - static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { assert(n_per_row % QK_K == 0); const int nb = n_per_row / QK_K; @@ -2576,12 +1301,6 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 } } -void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q4_K * restrict y = vy; - quantize_row_q4_K_ref(x, y, k); -} - static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2787,12 +1506,6 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 } } -void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q5_K * restrict y = vy; - quantize_row_q5_K_ref(x, y, k); -} - static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3005,12 +1718,6 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 } } -void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q6_K * restrict y = vy; - quantize_row_q6_K_ref(x, y, k); -} - static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3413,33 +2120,20 @@ void quantize_row_tq2_0_ref(const float * restrict x, block_tq2_0 * restrict y, } } -void quantize_row_tq1_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_tq1_0 * restrict y = vy; - quantize_row_tq1_0_ref(x, y, k); -} - -void quantize_row_tq2_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_tq2_0 * restrict y = vy; - quantize_row_tq2_0_ref(x, y, k); -} - size_t quantize_tq1_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { (void)quant_weights; // not used const size_t row_size = ggml_row_size(GGML_TYPE_TQ1_0, n_per_row); - quantize_row_tq1_0(src, dst, (int64_t)nrow*n_per_row); + quantize_row_tq1_0_ref(src, dst, (int64_t)nrow*n_per_row); return nrow * row_size; } size_t quantize_tq2_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { (void)quant_weights; // not used const size_t row_size = ggml_row_size(GGML_TYPE_TQ2_0, n_per_row); - quantize_row_tq2_0(src, dst, (int64_t)nrow*n_per_row); + quantize_row_tq2_0_ref(src, dst, (int64_t)nrow*n_per_row); return nrow * row_size; } - void dequantize_row_tq1_0(const block_tq1_0 * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int64_t nb = k / QK_K; @@ -3832,9179 +2526,6 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int6 } } -void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q8_K_ref(x, y, k); -} - -//===================================== Dot products ================================= - -// -// Helper functions -// -#if __AVX__ || __AVX2__ || __AVX512F__ - -// shuffles to pick the required scales in dot products -static inline __m256i get_scale_shuffle_q3k(int i) { - static const uint8_t k_shuffle[128] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, - }; - return _mm256_loadu_si256((const __m256i*)k_shuffle + i); -} -static inline __m256i get_scale_shuffle_k4(int i) { - static const uint8_t k_shuffle[256] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, - 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, - 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, - 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, - 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 - }; - return _mm256_loadu_si256((const __m256i*)k_shuffle + i); -} -static inline __m128i get_scale_shuffle(int i) { - static const uint8_t k_shuffle[128] = { - 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, - 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, - 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, - 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, - 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, - 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, - 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 - }; - return _mm_loadu_si128((const __m128i*)k_shuffle + i); -} -#elif defined(__loongarch_asx) -// shuffles to pick the required scales in dot products -static inline __m256i get_scale_shuffle_q3k(int i) { - static const uint8_t k_shuffle[128] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, - }; - return __lasx_xvld((const __m256i*)k_shuffle + i, 0); -} -static inline __m256i get_scale_shuffle_k4(int i) { - static const uint8_t k_shuffle[256] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, - 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, - 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, - 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, - 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 - }; - return __lasx_xvld((const __m256i*)k_shuffle + i, 0); -} -static inline __m128i get_scale_shuffle(int i) { - static const uint8_t k_shuffle[128] = { - 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, - 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, - 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, - 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, - 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, - 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, - 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 - }; - return __lsx_vld((const __m128i*)k_shuffle + i, 0); -} -#endif - -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q4_0 * restrict vx0 = vx; - const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); - const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q4_0 * restrict b_x0 = &vx0[i]; - const block_q4_0 * restrict b_x1 = &vx1[i]; - const block_q8_0 * restrict b_y0 = &vy0[i]; - const block_q8_0 * restrict b_y1 = &vy1[i]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - const int8x16_t s8b = vdupq_n_s8(0x8); - - const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); - const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // sub 8 - const int8x16_t x0_l = vsubq_s8(v0_0l, s8b); - const int8x16_t x0_h = vsubq_s8(v0_0h, s8b); - const int8x16_t x1_l = vsubq_s8(v0_1l, s8b); - const int8x16_t x1_h = vsubq_s8(v0_1h, s8b); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; - - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - - vst1_f32(s, vget_low_f32(sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - -#if defined(__ARM_FEATURE_SVE) - svfloat32_t sumv0 = svdup_n_f32(0.0f); - svfloat32_t sumv1 = svdup_n_f32(0.0f); - - const int vector_length = ggml_cpu_get_sve_cnt()*8; - - // VLA Implementation using switch case - switch (vector_length) { - case 128: - { - // predicate for activating higher lanes for 4 float32 elements - const svbool_t ph4 = svptrue_pat_b32(SV_VL4); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); - const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx0r, 0x0F)); - const svint8_t qx0h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx0r, 0x04)); - const svint8_t qx1l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx1r, 0x0F)); - const svint8_t qx1h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx1r, 0x04)); - - // sub 8 - const svint8_t qx0ls = svsub_n_s8_x(svptrue_b8(), qx0h, 8); - const svint8_t qx0hs = svsub_n_s8_x(svptrue_b8(), qx0l, 8); - const svint8_t qx1ls = svsub_n_s8_x(svptrue_b8(), qx1h, 8); - const svint8_t qx1hs = svsub_n_s8_x(svptrue_b8(), qx1l, 8); - - // load y - const svint8_t qy0h = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy0l = svld1_s8(svptrue_b8(), y0->qs + 16); - const svint8_t qy1h = svld1_s8(svptrue_b8(), y1->qs); - const svint8_t qy1l = svld1_s8(svptrue_b8(), y1->qs + 16); - - // dot product - sumv0 = svmla_n_f32_x(ph4, sumv0, svcvt_f32_s32_x(ph4, svadd_x(ph4, - svdot_s32(svdup_n_s32(0), qx0ls, qy0l), - svdot_s32(svdup_n_s32(0), qx0hs, qy0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(ph4, sumv1, svcvt_f32_s32_x(ph4, svadd_x(ph4, - svdot_s32(svdup_n_s32(0), qx1ls, qy1l), - svdot_s32(svdup_n_s32(0), qx1hs, qy1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 256: - { - // predicate for activating higher lanes for 16 int8 elements - const svbool_t ph16 = svptrue_pat_b8(SV_VL16); - // predicate for activating lower lanes for 16 int8 elements - const svbool_t pl16 = svnot_b_z(svptrue_b8(), ph16); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); - const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); - const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); - - // sub 8 - const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); - const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); - - // load y - const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); - - // dot product - sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 512: - { - // predicate for activating higher lanes for 32 int8 elements - const svbool_t ph32 = svptrue_pat_b8(SV_VL32); - - // predicate for activating higher lanes for 16 int8 elements - const svbool_t ph16 = svptrue_pat_b8(SV_VL16); - // predicate for activating lower lanes for 16 int8 elements from first 32 int8 activated lanes - const svbool_t pl16 = svnot_b_z(ph32, ph16); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(ph32, x0->qs); - const svuint8_t qx1r = svld1rq_u8(ph32, x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); - const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); - - // sub 8 - const svint8_t qx0s = svsub_n_s8_x(ph32, qx0, 8); - const svint8_t qx1s = svsub_n_s8_x(ph32, qx1, 8); - - // load y - const svint8_t qy0 = svld1_s8(ph32, y0->qs); - const svint8_t qy1 = svld1_s8(ph32, y1->qs); - - // dot product - sumv0 = svmla_n_f32_x(ph32, sumv0, svcvt_f32_s32_x(ph32, - svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(ph32, sumv1, svcvt_f32_s32_x(ph32, - svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(ph32, svadd_f32_x(ph32, sumv0, sumv1)); - } break; - default: - assert(false && "Unsupported vector length"); - break; - } - -#elif defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - const int8x16_t s8b = vdupq_n_s8(0x8); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // sub 8 - const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); - const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); - const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); - const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - // dot product into int32x4_t - const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); - const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = _mm256_set1_epi8( 8 ); - qx = _mm256_sub_epi8( qx, off ); - - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#elif defined(__AVX__) - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); - - const __m128i q4b_1_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_1), _mm_set1_epi8(8)); - const __m128i q4b_1_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_1, 4)), _mm_set1_epi8(8)); - const __m128i q4b_2_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_2), _mm_set1_epi8(8)); - const __m128i q4b_2_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_2, 4)), _mm_set1_epi8(8)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); - accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); -#elif defined(__SSSE3__) - // set constants - const __m128i lowMask = _mm_set1_epi8(0xF); - const __m128i off = _mm_set1_epi8(8); - - // Initialize accumulator with zeros - __m128 acc_0 = _mm_setzero_ps(); - __m128 acc_1 = _mm_setzero_ps(); - __m128 acc_2 = _mm_setzero_ps(); - __m128 acc_3 = _mm_setzero_ps(); - - for (; ib + 1 < nb; ib += 2) { - _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs); - - __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); - __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); - bx_0 = _mm_sub_epi8(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); - __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16)); - bx_1 = _mm_sub_epi8(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); - - const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - - __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); - __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - bx_2 = _mm_sub_epi8(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); - __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16)); - bx_3 = _mm_sub_epi8(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = _mm_cvtepi32_ps(i32_0); - __m128 p1 = _mm_cvtepi32_ps(i32_1); - __m128 p2 = _mm_cvtepi32_ps(i32_2); - __m128 p3 = _mm_cvtepi32_ps(i32_3); - - // Apply the scale - __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); - __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); - __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); - __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); - - // Acummulate - acc_0 = _mm_add_ps(p0_d, acc_0); - acc_1 = _mm_add_ps(p1_d, acc_1); - acc_2 = _mm_add_ps(p2_d, acc_2); - acc_3 = _mm_add_ps(p3_d, acc_3); - } - - sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (; ib < nb; ++ib) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - // subtract offset - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector signed char v8 = vec_splats((signed char)0x8); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 8 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed char q4x0 = vec_and(qxs, lowMask); - vector signed char q4x1 = vec_sr(qxs, v4); - - q4x0 = vec_sub(q4x0, v8); - q4x1 = vec_sub(q4x1, v8); - - vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi0 = vec_sum4s(qv1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = __lasx_xvreplgr2vr_b( 8 ); - qx = __lasx_xvsub_b( qx, off ); - - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = __lasx_xvfmadd_s( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#elif defined(__loongarch_sx) - // set constants - const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); - const __m128i off = __lsx_vreplgr2vr_b(8); - - // Initialize accumulator with zeros - __m128 acc_0 = __lsx_vldi(0); - __m128 acc_1 = __lsx_vldi(0); - __m128 acc_2 = __lsx_vldi(0); - __m128 acc_3 = __lsx_vldi(0); - - for (; ib + 1 < nb; ib += 2) { - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0); - - __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); - __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0); - bx_0 = __lsx_vsub_b(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); - __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0); - bx_1 = __lsx_vsub_b(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); - //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); - - const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0); - - __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); - __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0); - bx_2 = __lsx_vsub_b(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); - __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0); - bx_3 = __lsx_vsub_b(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = __lsx_vffint_s_w(i32_0); - __m128 p1 = __lsx_vffint_s_w(i32_1); - __m128 p2 = __lsx_vffint_s_w(i32_2); - __m128 p3 = __lsx_vffint_s_w(i32_3); - - // Apply the scale - __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); - __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); - __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); - __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); - - // Acummulate - acc_0 = __lsx_vfadd_s(p0_d, acc_0); - acc_1 = __lsx_vfadd_s(p1_d, acc_1); - acc_2 = __lsx_vfadd_s(p2_d, acc_2); - acc_3 = __lsx_vfadd_s(p3_d, acc_3); - } - - sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); -#endif - for (; ib < nb; ++ib) { - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[ib].qs[j] & 0x0F) - 8; - const int v1 = (x[ib].qs[j] >> 4) - 8; - - sumi0 += (v0 * y[ib].qs[j]); - sumi1 += (v1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); - } - - *s = sumf; -} - -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_1; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q4_1 * restrict vx0 = vx; - const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); - const block_q8_1 * restrict vy0 = vy; - const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t summs0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q4_1 * restrict b_x0 = &vx0[i]; - const block_q4_1 * restrict b_x1 = &vx1[i]; - const block_q8_1 * restrict b_y0 = &vy0[i]; - const block_q8_1 * restrict b_y1 = &vy1[i]; - - float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; - summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); - const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); - - // 4-bit -> 8-bit - const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - // mmla into int32x4_t - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - sumv2 = vaddq_f32(sumv2, summs0); - - vst1_f32(s, vget_low_f32 (sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - - // TODO: add WASM SIMD -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs = 0; - - for (; ib + 1 < nb; ib += 2) { - const block_q4_1 * restrict x0 = &x[ib + 0]; - const block_q4_1 * restrict x1 = &x[ib + 1]; - const block_q8_1 * restrict y0 = &y[ib + 0]; - const block_q8_1 * restrict y1 = &y[ib + 1]; - - summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - // dot product into int32x4_t - const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); - const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - // Main loop - for (; ib < nb; ++ib) { - const float d0 = GGML_FP16_TO_FP32(x[ib].d); - const float d1 = GGML_FP16_TO_FP32(y[ib].d); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - const __m256 d0v = _mm256_set1_ps( d0 ); - const __m256 d1v = _mm256_set1_ps( d1 ); - - // Compute combined scales - const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); - - // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i qx = bytes_from_nibbles_32(x[ib].qs); - const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs ); - - const __m256 xy = mul_sum_us8_pairs_float(qx, qy); - - // Accumulate d0*d1*x*y -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d0d1, xy, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); -#endif - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (; ib < nb; ++ib) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); - vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f}; - vsumf0 = vec_madd(vxmin, vys, vsumf0); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask); - vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_msum(q8y0, q4x0, vsumi0); - vsumi0 = vec_msum(q8y1, q4x1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0; - - // Main loop - for (; ib < nb; ++ib) { - const float d0 = GGML_FP16_TO_FP32(x[ib].d); - const float d1 = GGML_FP16_TO_FP32(y[ib].d); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); - const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); - - // Compute combined scales - const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); - - // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i qx = bytes_from_nibbles_32(x[ib].qs); - const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0); - - const __m256 xy = mul_sum_us8_pairs_float(qx, qy); - - // Accumulate d0*d1*x*y - acc = __lasx_xvfmadd_s( d0d1, xy, acc ); - } - - sumf = hsum_float_8(acc) + summs; -#endif - for (; ib < nb; ++ib) { - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[ib].qs[j] & 0x0F); - const int v1 = (x[ib].qs[j] >> 4); - - sumi0 += (v0 * y[ib].qs[j]); - sumi1 += (v1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - - *s = sumf; -} - -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - int ib = 0; - float sumf = 0; - - assert(n % qk == 0); - assert(qk == QK5_0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - for (; ib + 1 < nb; ib += 2) { - const block_q5_0 * restrict x0 = &x[ib]; - const block_q5_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - // extract the 5th bit via lookup table ((!b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_1[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_1[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (; ib < nb; ++ib) { - const block_q5_0 * restrict x0 = &x[ib]; - const block_q8_0 * restrict y0 = &y[ib]; - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_1[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); - const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( - wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); - } - - sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); - qx = _mm256_or_si256(qx, bxhi); - - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps(d, q, acc); - } - - sumf = hsum_float_8(acc); -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8((char)0xF0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - - __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); - const __m256i bxhi = bytes_from_bits_32(x[ib].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_andnot_si128(bxhil, mask); - bxhih = _mm_andnot_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx_0); - __m128i bxh = _mm256_extractf128_si256(bx_0, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx_0 = MM256_SET_M128I(bxh, bxl); - - const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0); - - /* Multiply q with scale and accumulate */ - acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); - } - - sumf = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // These temporary registers are for masking and shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); - - vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); - vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (; ib < nb; ++ib) { - memcpy(&qh, x[ib].qh, sizeof(uint32_t)); - - // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - - // ((qh & (1u << (j + 16))) >> (j + 12)); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); - vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v4 = vec_splats((unsigned char)4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])}; - vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])}; - - vector signed char qh0 = (vector signed char)aux64x2_0; - vector signed char qh1 = (vector signed char)aux64x2_1; - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - - vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); - vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl( 16, y[ib].qs); - - vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); - - qv0 = vec_add(qv0, qv1); - - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); - qx = __lasx_xvor_v(qx, bxhi); - - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = __lasx_xvfmadd_s(d, q, acc); - } - - sumf = hsum_float_8(acc); -#endif - for (; ib < nb; ++ib) { - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); - - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); - - const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16); - const int32_t x1 = (int8_t)(((x[ib].qs[j] >> 4) | xh_1) - 16); - - sumi0 += (x0 * y[ib].qs[j]); - sumi1 += (x1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; - } - - *s = sumf; -} - -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_1; - const int nb = n / qk; - - int ib = 0; - float sumf = 0; - - assert(n % qk == 0); - assert(qk == QK5_1); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs0 = 0.0f; - float summs1 = 0.0f; - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - for (; ib + 1 < nb; ib += 2) { - const block_q5_1 * restrict x0 = &x[ib]; - const block_q5_1 * restrict x1 = &x[ib + 1]; - const block_q8_1 * restrict y0 = &y[ib]; - const block_q8_1 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); - summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); - - // extract the 5th bit via lookup table ((b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_0[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_0[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit - const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - float summs = 0.0f; - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (; ib < nb; ++ib) { - const block_q5_1 * restrict x0 = &x[ib]; - const block_q8_1 * restrict y0 = &y[ib]; - - summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_0[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit - const v128_t v0lf = wasm_v128_or(v0l, qhl); - const v128_t v0hf = wasm_v128_or(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, - wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); - } - - sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); - qx = _mm256_or_si256(qx, bxhi); - - const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_us8_pairs_float(qx, qy); - - acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8(0x10); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); - const __m256i bxhi = bytes_from_bits_32(x[ib].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_and_si128(bxhil, mask); - bxhih = _mm_and_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx_0); - __m128i bxh = _mm256_extractf128_si256(bx_0, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx_0 = MM256_SET_M128I(bxh, bxl); - - const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); - - acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // temporary registers for shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (; ib < nb; ++ib) { - memcpy(&qh, x[ib].qh, sizeof(uint32_t)); - - // load qh - vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); - - // ((qh >> (j + 0)) << 4) & 0x10; - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); - - // ((qh >> (j + 12)) ) & 0x10; - vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); - vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f}; - vsumf0 = vec_madd(vxmin, vys, vsumf0); - - vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])}; - vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])}; - - vector signed char qh0 = (vector signed char)aux64x2_0; - vector signed char qh1 = (vector signed char)aux64x2_1; - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - - vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0); - vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl( 16, y[ib].qs); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_msum(q8y0, q5x0, vsumi0); - vsumi0 = vec_msum(q8y1, q5x1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); - qx = __lasx_xvor_v(qx, bxhi); - - const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_us8_pairs_float(qx, qy); - - acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); - } - - sumf = hsum_float_8(acc) + summs; -#endif - for (; ib < nb; ++ib) { - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); - - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; - - const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0; - const int32_t x1 = (x[ib].qs[j] >> 4) | xh_1; - - sumi0 += (x0 * y[ib].qs[j]); - sumi1 += (x1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - - *s = sumf; -} - -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q8_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q8_0 * restrict vx0 = vx; - const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); - const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q8_0 * restrict b_x0 = &vx0[i]; - const block_q8_0 * restrict b_y0 = &vy0[i]; - - const block_q8_0 * restrict b_x1 = &vx1[i]; - const block_q8_0 * restrict b_y1 = &vy1[i]; - - const int8x16_t x0_l = vld1q_s8(b_x0->qs); - const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16); - const int8x16_t x1_l = vld1q_s8(b_x1->qs); - const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - - vst1_f32(s, vget_low_f32(sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - -#if defined(__ARM_FEATURE_SVE) - svfloat32_t sumv0 = svdup_n_f32(0.0f); - svfloat32_t sumv1 = svdup_n_f32(0.0f); - - const int vector_length = ggml_cpu_get_sve_cnt()*8; - - //VLA Implemenation for SVE - switch (vector_length) { - case 128: - { - // predicate for activating lanes for 16 Int8 elements - const svbool_t ph16 = svptrue_pat_b8 (SV_VL16); - const svbool_t pl16 = svptrue_pat_b32(SV_VL4); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svint8_t qx0_0 = svld1_s8(ph16, x0->qs); - const svint8_t qx0_1 = svld1_s8(ph16, x0->qs+16); - const svint8_t qx1_0 = svld1_s8(ph16, x1->qs); - const svint8_t qx1_1 = svld1_s8(ph16, x1->qs+16); - - // load y - const svint8_t qy0_0 = svld1_s8(ph16, y0->qs); - const svint8_t qy0_1 = svld1_s8(ph16, y0->qs+16); - const svint8_t qy1_0 = svld1_s8(ph16, y1->qs); - const svint8_t qy1_1 = svld1_s8(ph16, y1->qs+16); - - sumv0 = svmla_n_f32_x(pl16, sumv0, svcvt_f32_s32_x(pl16, svadd_x(pl16, - svdot_s32(svdup_n_s32(0), qx0_0, qy0_0), - svdot_s32(svdup_n_s32(0), qx0_1, qy0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(pl16, sumv1, svcvt_f32_s32_x(pl16, svadd_x(pl16, - svdot_s32(svdup_n_s32(0), qx1_0, qy1_0), - svdot_s32(svdup_n_s32(0), qx1_1, qy1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(pl16, svadd_f32_x(pl16, sumv0, sumv1)); - } break; - case 256: - { - //printf("sve256"); - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); - const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); - - // load y - const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); - - sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 512: - { - // predicate for activating high 256 bit - const svbool_t ph32 = svptrue_pat_b8(SV_VL32); - // predicate for activating low 256 bit - const svbool_t pl32 = svnot_b_z(svptrue_b8(), ph32); - - // predicate for activating high lanes for 8 float32 elements - const svbool_t ph8 = svptrue_pat_b32(SV_VL8); - // predicate for activating low lanes for 8 float32 elements - const svbool_t pl8 = svnot_b_z(svptrue_b32(), ph8); - - svfloat32_t sumv00 = svdup_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - //load 32 int8_t in first half of vector and put another 32 int8_t in second vector lower bits - // and add them to make one 64 element vector - // load x - const svint8_t qx_32 = svld1_s8(ph32, x0->qs); - svint8_t qx_64 = svld1_s8(pl32, x0->qs + 2); - - qx_64 = svadd_s8_x(svptrue_b8(), qx_32, qx_64); - - // load y - const svint8_t qy_32 = svld1_s8(ph32, y0->qs); - svint8_t qy_64 = svld1_s8(pl32, y0->qs + 2); - - qy_64 = svadd_s8_x(svptrue_b8(), qy_32, qy_64); - - // scale creation - const float32_t deq1 = GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d); - const float32_t deq2 = GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d); - - // duplicate deq1 in first half of vector and deq2 in second half of vector - const svfloat32_t temp = svdup_f32_m(svdup_f32_z(ph8, deq1), pl8, deq2); - - const svfloat32_t sumvt = svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx_64, qy_64)); - - sumv00 = svmla_f32_m(svptrue_b32(), sumv00, sumvt, temp); - } - - sumf = svaddv_f32(svptrue_b32(), sumv00); - break; - } - default: - assert(false && "Unsupported vector length"); - break; - } -#elif defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const int8x16_t x0_0 = vld1q_s8(x0->qs); - const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); - const int8x16_t x1_0 = vld1q_s8(x1->qs); - const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); - - // load y - const int8x16_t y0_0 = vld1q_s8(y0->qs); - const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); - const int8x16_t y1_0 = vld1q_s8(y1->qs); - const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), - ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), - ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - // Compute combined scale for the block - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs); - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - // Multiply q with scale and accumulate -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d, q, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc ); -#endif - } - - sumf = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk); - - for (; ib < nb; ++ib) { - // load elements - vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl); - vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl); - - vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl); - - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); - vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); - - sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); - } -#elif defined(__POWER9_VECTOR__) - const vector signed int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 8 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char q8x0 = vec_xl( 0, x[ib].qs); - vector signed char q8x1 = vec_xl(16, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed short qv0 = vec_mule(q8x0, q8y0); - vector signed short qv1 = vec_mulo(q8x0, q8y0); - vector signed short qv2 = vec_mule(q8x1, q8y1); - vector signed short qv3 = vec_mulo(q8x1, q8y1); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi1 = vec_sum4s(qv1, vsumi1); - vsumi0 = vec_sum4s(qv2, vsumi0); - vsumi1 = vec_sum4s(qv3, vsumi1); - - vsumi0 = vec_add(vsumi0, vsumi1); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - // Compute combined scale for the block - const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0); - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - // Multiply q with scale and accumulate - acc = __lasx_xvfmadd_s( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#endif - for (; ib < nb; ++ib) { - int sumi = 0; - - for (int j = 0; j < qk; j++) { - sumi += x[ib].qs[j]*y[ib].qs[j]; - } - - sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); - } - - *s = sumf; -} - -void ggml_vec_dot_tq1_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq1_0 * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - float sumf = 0.0f; - - uint8_t k_shift[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; - - const uint8x16_t shift = vld1q_u8(k_shift); - - for (int i = 0; i < nb; ++i) { -#if defined(__ARM_FEATURE_DOTPROD) - int32x4_t sumi0 = vdupq_n_s32(0); - int32x4_t sumi1 = vdupq_n_s32(0); -#else - int16x8_t sumi0 = vdupq_n_s16(0); - int16x8_t sumi1 = vdupq_n_s16(0); -#endif - - // first 32 bytes of 5 elements - { - uint8x16_t qx0 = vld1q_u8(x[i].qs + 0); - uint8x16_t qx1 = vld1q_u8(x[i].qs + 16); - uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(3)); - uint8x16_t qx3 = vmulq_u8(qx1, vdupq_n_u8(3)); - uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(9)); - uint8x16_t qx5 = vmulq_u8(qx1, vdupq_n_u8(9)); - uint8x16_t qx6 = vmulq_u8(qx0, vdupq_n_u8(27)); - uint8x16_t qx7 = vmulq_u8(qx1, vdupq_n_u8(27)); - uint8x16_t qx8 = vmulq_u8(qx0, vdupq_n_u8(81)); - uint8x16_t qx9 = vmulq_u8(qx1, vdupq_n_u8(81)); - - // multiply by 3 and keep the 2 bits above 8 bits - int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); - int8x16_t sqx6 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx6, vshrq_n_u8(qx6, 1)), 6)); - int8x16_t sqx7 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx7, vshrq_n_u8(qx7, 1)), 6)); - int8x16_t sqx8 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx8, vshrq_n_u8(qx8, 1)), 6)); - int8x16_t sqx9 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx9, vshrq_n_u8(qx9, 1)), 6)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + 0); - const int8x16_t qy1 = vld1q_s8(y[i].qs + 16); - const int8x16_t qy2 = vld1q_s8(y[i].qs + 32); - const int8x16_t qy3 = vld1q_s8(y[i].qs + 48); - const int8x16_t qy4 = vld1q_s8(y[i].qs + 64); - const int8x16_t qy5 = vld1q_s8(y[i].qs + 80); - const int8x16_t qy6 = vld1q_s8(y[i].qs + 96); - const int8x16_t qy7 = vld1q_s8(y[i].qs + 112); - const int8x16_t qy8 = vld1q_s8(y[i].qs + 128); - const int8x16_t qy9 = vld1q_s8(y[i].qs + 144); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); - sumi0 = vdotq_s32(sumi0, sqx6, qy6); - sumi1 = vdotq_s32(sumi1, sqx7, qy7); - sumi0 = vdotq_s32(sumi0, sqx8, qy8); - sumi1 = vdotq_s32(sumi1, sqx9, qy9); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx8), vget_low_s8(qy8)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx8), vget_high_s8(qy8)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx9), vget_low_s8(qy9)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx9), vget_high_s8(qy9)); -#endif - } - - // last 16 bytes of 5-element, along with the 4 bytes of 4 elements - { - uint8x16_t qx0 = vld1q_u8(x[i].qs + 32); - uint8x16_t qx1 = vmulq_u8(qx0, vdupq_n_u8(3)); - uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(9)); - uint8x16_t qx3 = vmulq_u8(qx0, vdupq_n_u8(27)); - uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(81)); - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned - uint8x16_t qx5 = vreinterpretq_u8_u32(vdupq_n_u32(qh)); - qx5 = vmulq_u8(qx5, shift); - - // multiply by 3 and keep the 2 bits above 8 bits - int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + 160); - const int8x16_t qy1 = vld1q_s8(y[i].qs + 176); - const int8x16_t qy2 = vld1q_s8(y[i].qs + 192); - const int8x16_t qy3 = vld1q_s8(y[i].qs + 208); - const int8x16_t qy4 = vld1q_s8(y[i].qs + 224); - const int8x16_t qy5 = vld1q_s8(y[i].qs + 240); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); -#endif - } - - const int16x8_t ysum0 = vld1q_s16(y[i].bsums); - const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vaddq_s32(sumi0, sumi1); - sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); - - sumf += d * (float) vaddvq_s32(sumi0); -#else - sumi0 = vaddq_s16(sumi0, sumi1); - sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); - - sumf += d * (float) vaddlvq_s16(sumi0); -#endif - } - - *s = sumf; - -#elif defined(__AVX2__) - __m256 sumf = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - // 16-bit sums - __m256i sumi0 = _mm256_setzero_si256(); - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - - // first 32 bytes of 5 elements - { - __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs)); - // 8-bit multiplies with shifts, masks and adds - __m256i qx1 = _mm256_add_epi8(qx0, _mm256_add_epi8(qx0, qx0)); // 1 * 3 - __m256i qx2 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx0, 3), _mm256_set1_epi8(-8)), qx0); // 1 * 9 - __m256i qx3 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx1, 3), _mm256_set1_epi8(-8)), qx1); // 3 * 9 - __m256i qx4 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx2, 3), _mm256_set1_epi8(-8)), qx2); // 9 * 9 - - // TODO: can _mm256_mulhi_epu16 be faster even if 16-bits? - - // Cancel the +1 from avg so that it behaves like a halving add - qx0 = _mm256_subs_epu8(qx0, _mm256_set1_epi8(1)); - qx1 = _mm256_subs_epu8(qx1, _mm256_set1_epi8(1)); - qx2 = _mm256_subs_epu8(qx2, _mm256_set1_epi8(1)); - qx3 = _mm256_subs_epu8(qx3, _mm256_set1_epi8(1)); - qx4 = _mm256_subs_epu8(qx4, _mm256_set1_epi8(1)); - // Multiply by 3 and get the top 2 bits - qx0 = _mm256_avg_epu8(qx0, _mm256_avg_epu8(qx0, _mm256_setzero_si256())); - qx1 = _mm256_avg_epu8(qx1, _mm256_avg_epu8(qx1, _mm256_setzero_si256())); - qx2 = _mm256_avg_epu8(qx2, _mm256_avg_epu8(qx2, _mm256_setzero_si256())); - qx3 = _mm256_avg_epu8(qx3, _mm256_avg_epu8(qx3, _mm256_setzero_si256())); - qx4 = _mm256_avg_epu8(qx4, _mm256_avg_epu8(qx4, _mm256_setzero_si256())); - qx0 = _mm256_and_si256(_mm256_srli_epi16(qx0, 6), _mm256_set1_epi8(3)); - qx1 = _mm256_and_si256(_mm256_srli_epi16(qx1, 6), _mm256_set1_epi8(3)); - qx2 = _mm256_and_si256(_mm256_srli_epi16(qx2, 6), _mm256_set1_epi8(3)); - qx3 = _mm256_and_si256(_mm256_srli_epi16(qx3, 6), _mm256_set1_epi8(3)); - qx4 = _mm256_and_si256(_mm256_srli_epi16(qx4, 6), _mm256_set1_epi8(3)); - - const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 0)); - const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 32)); - const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 64)); - const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 96)); - const __m256i qy4 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 128)); - - qx0 = _mm256_maddubs_epi16(qx0, qy0); - qx1 = _mm256_maddubs_epi16(qx1, qy1); - qx2 = _mm256_maddubs_epi16(qx2, qy2); - qx3 = _mm256_maddubs_epi16(qx3, qy3); - qx4 = _mm256_maddubs_epi16(qx4, qy4); - - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); - sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); - sumi2 = _mm256_add_epi16(sumi2, qx4); - } - - // last 16 bytes of 5-element, along with the 4 bytes of 4 elements - { - __m128i qx0 = _mm_loadu_si128((const __m128i *) (x[i].qs + 32)); - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned - __m256i qx5_l = _mm256_cvtepu8_epi16(_mm_set1_epi32(qh)); - __m128i qx1 = _mm_add_epi8(qx0, _mm_add_epi8(qx0, qx0)); // 1 * 3 - __m128i qx2 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx0, 3), _mm_set1_epi8(-8)), qx0); // 1 * 9 - __m128i qx3 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx1, 3), _mm_set1_epi8(-8)), qx1); // 3 * 9 - __m128i qx4 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx2, 3), _mm_set1_epi8(-8)), qx2); // 9 * 9 - __m256i qx01 = MM256_SET_M128I(qx1, qx0); - __m256i qx23 = MM256_SET_M128I(qx3, qx2); - - // avx2 does not have 8-bit multiplies, so 16-bit it is. - qx5_l = _mm256_mullo_epi16(qx5_l, _mm256_set_epi16(27, 27, 27, 27, 9, 9, 9, 9, 3, 3, 3, 3, 1, 1, 1, 1)); - qx5_l = _mm256_and_si256(qx5_l, _mm256_set1_epi16(0xFF)); - __m128i qx5 = _mm_packus_epi16(_mm256_castsi256_si128(qx5_l), _mm256_extracti128_si256(qx5_l, 1)); - - __m256i qx45 = MM256_SET_M128I(qx5, qx4); - - // Cancel the +1 from avg so that it behaves like a halving add - qx01 = _mm256_subs_epu8(qx01, _mm256_set1_epi8(1)); - qx23 = _mm256_subs_epu8(qx23, _mm256_set1_epi8(1)); - qx45 = _mm256_subs_epu8(qx45, _mm256_set1_epi8(1)); - // Multiply by 3 and get the top 2 bits - qx01 = _mm256_avg_epu8(qx01, _mm256_avg_epu8(qx01, _mm256_setzero_si256())); - qx23 = _mm256_avg_epu8(qx23, _mm256_avg_epu8(qx23, _mm256_setzero_si256())); - qx45 = _mm256_avg_epu8(qx45, _mm256_avg_epu8(qx45, _mm256_setzero_si256())); - qx01 = _mm256_and_si256(_mm256_srli_epi16(qx01, 6), _mm256_set1_epi8(3)); - qx23 = _mm256_and_si256(_mm256_srli_epi16(qx23, 6), _mm256_set1_epi8(3)); - qx45 = _mm256_and_si256(_mm256_srli_epi16(qx45, 6), _mm256_set1_epi8(3)); - - const __m256i qy01 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 160)); - const __m256i qy23 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 192)); - const __m256i qy45 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 224)); - - qx01 = _mm256_maddubs_epi16(qx01, qy01); - qx23 = _mm256_maddubs_epi16(qx23, qy23); - qx45 = _mm256_maddubs_epi16(qx45, qy45); - - sumi0 = _mm256_add_epi16(sumi0, qx01); - sumi1 = _mm256_add_epi16(sumi1, qx23); - sumi2 = _mm256_add_epi16(sumi2, qx45); - } - - const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); - - sumi0 = _mm256_sub_epi16(sumi0, ysum); - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(sumi1, sumi2)); - sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); - - sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); - } - - *s = hsum_float_8(sumf); - -#else - const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243}; - - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - int sum = 0; - - for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) { - for (size_t l = 0; l < 5; ++l) { - for (size_t m = 0; m < 32; ++m) { - uint8_t q = x[i].qs[j + m] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[j*5 + l*32 + m]; - } - } - } - for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) { - for (size_t l = 0; l < 5; ++l) { - for (size_t m = 0; m < 16; ++m) { - uint8_t q = x[i].qs[j + m] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[j*5 + l*16 + m]; - } - } - } - - for (size_t l = 0; l < 4; ++l) { - for (size_t j = 0; j < sizeof(x->qh); ++j) { - uint8_t q = x[i].qh[j] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j]; - } - } - - sumf += (float) sum * (GGML_FP16_TO_FP32(x[i].d) * y[i].d); - } - - *s = sumf; -#endif -} - -void ggml_vec_dot_tq2_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq2_0 * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - float sumf = 0.0f; - - const uint8x16_t m3 = vdupq_n_u8(3); - - for (int i = 0; i < nb; ++i) { -#if defined(__ARM_FEATURE_DOTPROD) - int32x4_t sumi0 = vdupq_n_s32(0); - int32x4_t sumi1 = vdupq_n_s32(0); -#else - int16x8_t sumi0 = vdupq_n_s16(0); - int16x8_t sumi1 = vdupq_n_s16(0); -#endif - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - uint8x16_t qx0 = vld1q_u8(x[i].qs + j); - uint8x16_t qx1 = vld1q_u8(x[i].qs + j + 16); - uint8x16_t qx2 = vshrq_n_u8(qx0, 2); - uint8x16_t qx3 = vshrq_n_u8(qx1, 2); - uint8x16_t qx4 = vshrq_n_u8(qx0, 4); - uint8x16_t qx5 = vshrq_n_u8(qx1, 4); - uint8x16_t qx6 = vshrq_n_u8(qx0, 6); - uint8x16_t qx7 = vshrq_n_u8(qx1, 6); - - int8x16_t sqx0 = vreinterpretq_s8_u8(vandq_u8(qx0, m3)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vandq_u8(qx1, m3)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vandq_u8(qx2, m3)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vandq_u8(qx3, m3)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vandq_u8(qx4, m3)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vandq_u8(qx5, m3)); - int8x16_t sqx6 = vreinterpretq_s8_u8(vandq_u8(qx6, m3)); - int8x16_t sqx7 = vreinterpretq_s8_u8(vandq_u8(qx7, m3)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + j*4 + 0); - const int8x16_t qy1 = vld1q_s8(y[i].qs + j*4 + 16); - const int8x16_t qy2 = vld1q_s8(y[i].qs + j*4 + 32); - const int8x16_t qy3 = vld1q_s8(y[i].qs + j*4 + 48); - const int8x16_t qy4 = vld1q_s8(y[i].qs + j*4 + 64); - const int8x16_t qy5 = vld1q_s8(y[i].qs + j*4 + 80); - const int8x16_t qy6 = vld1q_s8(y[i].qs + j*4 + 96); - const int8x16_t qy7 = vld1q_s8(y[i].qs + j*4 + 112); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); - sumi0 = vdotq_s32(sumi0, sqx6, qy6); - sumi1 = vdotq_s32(sumi1, sqx7, qy7); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); -#endif - } - - const int16x8_t ysum0 = vld1q_s16(y[i].bsums); - const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vaddq_s32(sumi0, sumi1); - sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); - - sumf += d * (float) vaddvq_s32(sumi0); -#else - sumi0 = vaddq_s16(sumi0, sumi1); - sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); - - sumf += d * (float) vaddlvq_s16(sumi0); -#endif - } - - *s = sumf; - -#elif defined(__AVX2__) - __m256 sumf = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - // 16-bit sums, because 256*127 still fits - __m256i sumi0 = _mm256_setzero_si256(); - __m256i sumi1 = _mm256_setzero_si256(); - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs + j)); - __m256i qx1 = _mm256_srli_epi16(qx0, 2); - __m256i qx2 = _mm256_srli_epi16(qx0, 4); - __m256i qx3 = _mm256_srli_epi16(qx0, 6); - - // 0, 1, 2 (should not be 3) - qx0 = _mm256_and_si256(qx0, _mm256_set1_epi8(3)); - qx1 = _mm256_and_si256(qx1, _mm256_set1_epi8(3)); - qx2 = _mm256_and_si256(qx2, _mm256_set1_epi8(3)); - qx3 = _mm256_and_si256(qx3, _mm256_set1_epi8(3)); - - const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 0)); - const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 32)); - const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 64)); - const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 96)); - - qx0 = _mm256_maddubs_epi16(qx0, qy0); - qx1 = _mm256_maddubs_epi16(qx1, qy1); - qx2 = _mm256_maddubs_epi16(qx2, qy2); - qx3 = _mm256_maddubs_epi16(qx3, qy3); - - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); - sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); - } - - const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); - - sumi0 = _mm256_add_epi16(sumi0, sumi1); - sumi0 = _mm256_sub_epi16(sumi0, ysum); - sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); - - sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); - } - - *s = hsum_float_8(sumf); - -#else - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - int32_t sumi = 0; - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - for (size_t l = 0; l < 4; ++l) { - for (size_t k = 0; k < 32; ++k) { - sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1); - } - } - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - sumf += (float) sumi * d; - } - - *s = sumf; -#endif -} - -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q2_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); - const uint8x16_t m4 = vdupq_n_u8(0xF); - - const int32x4_t vzero = vdupq_n_s32(0); - - ggml_int8x16x2_t q2bytes; - uint8_t aux[16]; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint8_t * restrict sc = x[i].scales; - - const uint8x16_t mins_and_scales = vld1q_u8(sc); - const uint8x16_t scales = vandq_u8(mins_and_scales, m4); - vst1q_u8(aux, scales); - - const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); - const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); - const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}}; - const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), - vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); - const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), - vmull_s16(vget_high_s16(mins16.val[1]), vget_high_s16(q8sums.val[1]))); - sum += dmin * vaddvq_s32(vaddq_s32(s0, s1)); - - int isum = 0; - int is = 0; - -// We use this macro instead of a function call because for some reason -// the code runs 2-3% slower, even if the function is declared inline -#define MULTIPLY_ACCUM_WITH_SCALE(index)\ - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; - -#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ - MULTIPLY_ACCUM_WITH_SCALE((index)); - - for (int j = 0; j < QK_K/128; ++j) { - const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; - - ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); - - MULTIPLY_ACCUM_WITH_SCALE(0); - - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6); - - is += 8; - } - - sum += d * isum; - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m128i m4 = _mm_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); - const __m128i scales8 = _mm_and_si128(mins_and_scales, m4); - const __m128i mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); - const __m256i mins = _mm256_cvtepi8_epi16(mins8); - const __m256i prod = _mm256_madd_epi16(mins, _mm256_loadu_si256((const __m256i*)y[i].bsums)); - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc); - - const __m256i all_scales = _mm256_cvtepi8_epi16(scales8); - const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); - const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); - const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; - - __m256i sumi = _mm256_setzero_si256(); - - for (int j = 0; j < QK_K/128; ++j) { - - const __m256i q2bits = _mm256_loadu_si256((const __m256i*)q2); q2 += 32; - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i q2_0 = _mm256_and_si256(q2bits, m3); - const __m256i q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3); - const __m256i q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3); - const __m256i q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3); - - __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); - __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); - __m256i p2 = _mm256_maddubs_epi16(q2_2, q8_2); - __m256i p3 = _mm256_maddubs_epi16(q2_3, q8_3); - - p0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(0)), p0); - p1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(1)), p1); - p2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(2)), p2); - p3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(3)), p3); - - p0 = _mm256_add_epi32(p0, p1); - p2 = _mm256_add_epi32(p2, p3); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2)); - } - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(0x3); - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(0x2); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // load mins and scales from block_q2_K.scales[QK_K/16] - const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); - const __m128i scales16 = _mm_and_si128(mins_and_scales, m4); - const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); - const __m128i mins_0 = _mm_cvtepi8_epi16(mins16); - const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16)); - - // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2 - const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0])); - const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8])); - - // sumf += -dmin * summs in 32bits*8 - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc); - - const __m128i scales_0 = _mm_cvtepi8_epi16(scales16); - const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16)); - const __m128i scales[2] = { scales_0, scales_1 }; - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - for (int j = 0; j < QK_K/128; ++j) { - - // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K] - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - // load 2bits*16*8 from block_q2_K.qs[QK_K/4] - __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; - const __m128i q2_0 = _mm_and_si128(q2bits, m3); - const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; - const __m128i q2_1 = _mm_and_si128(q2bits, m3); - const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - - // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8 - __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0); - __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1); - __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2); - __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3); - __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4); - __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5); - __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6); - __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7); - - // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8 - __m128i shuffle = _mm_set1_epi16(0x0100); - p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0); - shuffle = _mm_add_epi16(shuffle, m2); - p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1); - shuffle = _mm_add_epi16(shuffle, m2); - p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2); - shuffle = _mm_add_epi16(shuffle, m2); - p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3); - shuffle = _mm_add_epi16(shuffle, m2); - p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4); - shuffle = _mm_add_epi16(shuffle, m2); - p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5); - shuffle = _mm_add_epi16(shuffle, m2); - p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6); - shuffle = _mm_add_epi16(shuffle, m2); - p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7); - - p0 = _mm_add_epi32(p0, p1); - p2 = _mm_add_epi32(p2, p3); - p4 = _mm_add_epi32(p4, p5); - p6 = _mm_add_epi32(p6, p7); - - // isum in 32bits*4*2 - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6)); - } - - // sumf += dall * isum - dmin * summs in 32bits - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - uint8_t temp_01[32] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - size_t vl = 16; - - vuint8m1_t scales = __riscv_vle8_v_u8m1(sc, vl); - vuint8m1_t aux = __riscv_vand_vx_u8m1(scales, 0x0F, vl); - - vint16m1_t q8sums = __riscv_vle16_v_i16m1(y[i].bsums, vl); - - vuint8mf2_t scales_2 = __riscv_vle8_v_u8mf2(sc, vl); - vuint8mf2_t mins8 = __riscv_vsrl_vx_u8mf2(scales_2, 0x4, vl); - vint16m1_t mins = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(mins8, vl)); - vint32m2_t prod = __riscv_vwmul_vv_i32m2(q8sums, mins, vl); - vint32m1_t vsums = __riscv_vredsum_vs_i32m2_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - - sumf += dmin * __riscv_vmv_x_s_i32m1_i32(vsums); - - vl = 32; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t v_b = __riscv_vle8_v_u8m1(temp_01, vl); - - uint8_t is=0; - int isum=0; - - for (int j = 0; j < QK_K/128; ++j) { - // load Q2 - vuint8m1_t q2_x = __riscv_vle8_v_u8m1(q2, vl); - - vuint8m1_t q2_0 = __riscv_vand_vx_u8m1(q2_x, 0x03, vl); - vuint8m1_t q2_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x2, vl), 0x03 , vl); - vuint8m1_t q2_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x4, vl), 0x03 , vl); - vuint8m1_t q2_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x6, vl), 0x03 , vl); - - // duplicate scale elements for product - vuint8m1_t sc0 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 0+is, vl), vl); - vuint8m1_t sc1 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 2+is, vl), vl); - vuint8m1_t sc2 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 4+is, vl), vl); - vuint8m1_t sc3 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 6+is, vl), vl); - - vint16m2_t p0 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_0, sc0, vl)); - vint16m2_t p1 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_1, sc1, vl)); - vint16m2_t p2 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_2, sc2, vl)); - vint16m2_t p3 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_3, sc3, vl)); - - // load Q8 - vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); - vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8+64, vl); - vint8m1_t q8_3 = __riscv_vle8_v_i8m1(q8+96, vl); - - vint32m4_t s0 = __riscv_vwmul_vv_i32m4(p0, __riscv_vwcvt_x_x_v_i16m2(q8_0, vl), vl); - vint32m4_t s1 = __riscv_vwmul_vv_i32m4(p1, __riscv_vwcvt_x_x_v_i16m2(q8_1, vl), vl); - vint32m4_t s2 = __riscv_vwmul_vv_i32m4(p2, __riscv_vwcvt_x_x_v_i16m2(q8_2, vl), vl); - vint32m4_t s3 = __riscv_vwmul_vv_i32m4(p3, __riscv_vwcvt_x_x_v_i16m2(q8_3, vl), vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s0, s1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s2, s3, vl), isum0, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(isum1); - - q2+=32; q8+=128; is=8; - - } - - sumf += dall * isum; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowScaleMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); - vector signed char vscales = vec_and(q2xmins, lowScaleMask); - - q2xmins = vec_sr(q2xmins, v4); - vector signed short q2xmins0 = vec_unpackh(q2xmins); - vector signed short q2xmins1 = vec_unpackl(q2xmins); - - vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); - vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); - vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); - vector signed char qxs1 = (vector signed char)vec_xl(16, q2); - q2 += 32; - - vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask); - vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask); - vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask); - vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask); - vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask); - vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask); - vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask); - vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y02 = vec_xl( 64, q8); - vector signed char q8y12 = vec_xl( 80, q8); - vector signed char q8y03 = vec_xl( 96, q8); - vector signed char q8y13 = vec_xl(112, q8); - q8 += 128; - - vector signed int qv0 = vec_msum(q8y00, q2x00, v0); - vector signed int qv1 = vec_msum(q8y01, q2x01, v0); - vector signed int qv2 = vec_msum(q8y02, q2x02, v0); - vector signed int qv3 = vec_msum(q8y03, q2x03, v0); - vector signed int qv4 = vec_msum(q8y10, q2x10, v0); - vector signed int qv5 = vec_msum(q8y11, q2x11, v0); - vector signed int qv6 = vec_msum(q8y12, q2x12, v0); - vector signed int qv7 = vec_msum(q8y13, q2x13, v0); - - vector signed short vscales_07 = vec_unpackh(vscales); - vector signed int vscales_03 = vec_unpackh(vscales_07); - vector signed int vscales_47 = vec_unpackl(vscales_07); - vector signed int vs0 = vec_splat(vscales_03, 0); - vector signed int vs1 = vec_splat(vscales_03, 1); - vector signed int vs2 = vec_splat(vscales_03, 2); - vector signed int vs3 = vec_splat(vscales_03, 3); - vector signed int vs4 = vec_splat(vscales_47, 0); - vector signed int vs5 = vec_splat(vscales_47, 1); - vector signed int vs6 = vec_splat(vscales_47, 2); - vector signed int vs7 = vec_splat(vscales_47, 3); - vscales = vec_sld(vscales, vscales, 8); - - vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1); - vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2); - vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3); - vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4); - vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5); - vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6); - vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m128i m4 = __lsx_vreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); - const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); - const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); - const __m256i mins = lasx_ext8_16(mins8); - const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); - - const __m256i all_scales = lasx_ext8_16(scales8); - const __m128i l_scales = lasx_extracti128(all_scales, 0); - const __m128i h_scales = lasx_extracti128(all_scales, 1); - const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; - - __m256i sumi = __lasx_xvldi(0); - - for (int j = 0; j < QK_K/128; ++j) { - - const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); - const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); - const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); - const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); - - __m256i p0 = lasx_maddubs_h(q2_0, q8_0); - __m256i p1 = lasx_maddubs_h(q2_1, q8_1); - __m256i p2 = lasx_maddubs_h(q2_2, q8_2); - __m256i p3 = lasx_maddubs_h(q2_3, q8_3); - - p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); - p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); - p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); - p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); - - p0 = __lasx_xvadd_w(p0, p1); - p2 = __lasx_xvadd_w(p2, p3); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); - } - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#else - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - int summs = 0; - for (int j = 0; j < 16; ++j) { - summs += y[i].bsums[j] * (sc[j] >> 4); - } - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - int isum = 0; - int is = 0; - int d; - for (int k = 0; k < QK_K/128; ++k) { - int shift = 0; - for (int j = 0; j < 4; ++j) { - d = sc[is++] & 0xF; - int isuml = 0; - for (int l = 0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); - isum += d * isuml; - d = sc[is++] & 0xF; - isuml = 0; - for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); - isum += d * isuml; - shift += 2; - q8 += 32; - } - q2 += 32; - } - sumf += dall * isum - dmin * summs; - } - *s = sumf; -#endif -} - -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const uint32_t kmask1 = 0x03030303; - const uint32_t kmask2 = 0x0f0f0f0f; - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - - uint32_t aux[3]; - uint32_t utmp[4]; - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m0 = vdupq_n_u8(1); - const uint8x16_t m1 = vshlq_n_u8(m0, 1); - const uint8x16_t m2 = vshlq_n_u8(m0, 2); - const uint8x16_t m3 = vshlq_n_u8(m0, 3); - const int8_t m32 = 32; - - ggml_int8x16x4_t q3bytes; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - - ggml_uint8x16x4_t q3h; - - int32_t isum = 0; - - // Set up scales - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - - int8_t * scale = (int8_t *)utmp; - for (int j = 0; j < 16; ++j) scale[j] -= m32; - - for (int j = 0; j < QK_K/128; ++j) { - - const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; - const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; - const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; - - q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); - q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); - q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); - q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; - - scale += 4; - - q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); - q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); - q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); - q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; - - scale += 4; - - if (j == 0) { - qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); - qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); - } - - } - sum += d * isum; - - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m256i mone = _mm256_set1_epi8(1); - const __m128i m32 = _mm_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t aux[3]; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // Set up scales - memcpy(aux, x[i].scales, 12); - __m128i scales128 = _mm_set_epi32( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = _mm_sub_epi8(scales128, m32); - const __m256i all_scales = _mm256_cvtepi8_epi16(scales128); - const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); - const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); - const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; - - // high bit - const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask); - - // integer accumulator - __m256i sumi = _mm256_setzero_si256(); - - int bit = 0; - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits - const __m256i q3bits = _mm256_loadu_si256((const __m256i*)q3); q3 += 32; - - // prepare low and high bits - const __m256i q3l_0 = _mm256_and_si256(q3bits, m3); - const __m256i q3h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3); - const __m256i q3h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3); - const __m256i q3h_2 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3); - const __m256i q3h_3 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); - __m256i q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2); - __m256i q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3); - - __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); - __m256i p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2); - __m256i p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - p16_2 = _mm256_sub_epi16(p16_2, q8s_2); - p16_3 = _mm256_sub_epi16(p16_3, q8s_3); - - // multiply with scales - p16_0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); - p16_2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); - p16_3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); - - // accumulate - p16_0 = _mm256_add_epi32(p16_0, p16_1); - p16_2 = _mm256_add_epi32(p16_2, p16_3); - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2)); - - } - - // multiply with block scale and accumulate - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - const __m128i mone = _mm_set1_epi8(1); - const __m128i m32 = _mm_set1_epi8(32); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - const uint32_t *aux; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // Set up scales - aux = (const uint32_t *)x[i].scales; - __m128i scales128 = _mm_set_epi32( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = _mm_sub_epi8(scales128, m32); - const __m128i scales_0 = _mm_cvtepi8_epi16(scales128); - const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128)); - const __m128i scales[2] = { scales_0, scales_1 }; - - // high bit *128*2 from block_q3_K.hmask[QK_K/8] - const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]); - const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]); - - // integer accumulator - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4] - const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; - const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; - - // prepare low and high bits - const int bit = j << 2; - - const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); - const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); - const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); - const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2); - - const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3); - const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3); - const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2); - const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2); - - const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3); - const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3); - const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2); - const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2); - - const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3); - const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3); - const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2); - const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2); - - // load Q8 quants from block_q8_K.qs[QK_K] - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0); - __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1); - __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2); - __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3); - __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4); - __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5); - __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6); - __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7); - - __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1); - __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2); - __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3); - __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4); - __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5); - __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6); - __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - p16_4 = _mm_sub_epi16(p16_4, q8s_4); - p16_5 = _mm_sub_epi16(p16_5, q8s_5); - p16_6 = _mm_sub_epi16(p16_6, q8s_6); - p16_7 = _mm_sub_epi16(p16_7, q8s_7); - - // multiply with scales - __m128i shuffle = _mm_set1_epi16(0x0100); - p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0); - shuffle = _mm_add_epi16(shuffle, m2); - p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1); - shuffle = _mm_add_epi16(shuffle, m2); - p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2); - shuffle = _mm_add_epi16(shuffle, m2); - p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3); - shuffle = _mm_add_epi16(shuffle, m2); - p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4); - shuffle = _mm_add_epi16(shuffle, m2); - p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5); - shuffle = _mm_add_epi16(shuffle, m2); - p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6); - shuffle = _mm_add_epi16(shuffle, m2); - p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7); - - // accumulate - p16_0 = _mm_add_epi32(p16_0, p16_1); - p16_2 = _mm_add_epi32(p16_2, p16_3); - p16_4 = _mm_add_epi32(p16_4, p16_5); - p16_6 = _mm_add_epi32(p16_6, p16_7); - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6)); - - } - - // multiply with block scale and accumulate - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - uint32_t aux[3]; - uint32_t utmp[4]; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - - int8_t * scale = (int8_t *)utmp; - for (int j = 0; j < 16; ++j) scale[j] -= 32; - - - size_t vl = 32; - uint8_t m = 1; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t vqh = __riscv_vle8_v_u8m1(qh, vl); - - int sum_t = 0; - - for (int j = 0; j < QK_K; j += 128) { - - vl = 32; - - // load Q3 - vuint8m1_t q3_x = __riscv_vle8_v_u8m1(q3, vl); - - vint8m1_t q3_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q3_x, 0x03, vl)); - vint8m1_t q3_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x2, vl), 0x03 , vl)); - vint8m1_t q3_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x4, vl), 0x03 , vl)); - vint8m1_t q3_3 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x6, vl), 0x03 , vl)); - - // compute mask for subtraction - vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl); - vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl); - vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl); - vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl); - vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl); - m <<= 1; - - // load Q8 and take product with Q3 - vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); - vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - - vl = 16; - - // retrieve lane to multiply with scale - vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); - vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); - vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); - vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); - vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); - vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); - vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); - vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); - vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); - vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); - - sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - - q3 += 32; q8 += 128; scale += 8; - - } - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - sumf += d*sum_t; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowMask1 = vec_splats((int8_t)0xf); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector signed char v1 = vec_splats((signed char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - UNUSED(kmask1); - UNUSED(kmask2); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(u0, lowMask1); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2)); - vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4); - vector signed char u31 = vec_and(u3, lowMask2); - - u1 = vec_or(u1, u30); - u2 = vec_or(vec_sr(u0, v4), u31); - - vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2); - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); - vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); - - vscales = vec_sub(vscales, off); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); - vector signed char qxs1 = (vector signed char)vec_xl(16, q3); - q3 += 32; - - //the low 2 bits - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); - vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); - vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); - - //the 3rd bit - vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); - vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); - vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); - vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); - vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); - vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); - vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); - vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); - qxhs0 = vec_sr(qxhs0, v4); - qxhs1 = vec_sr(qxhs1, v4); - - vector signed char q3x00 = vec_sub(qxs00, qxh00); - vector signed char q3x01 = vec_sub(qxs01, qxh01); - vector signed char q3x02 = vec_sub(qxs02, qxh02); - vector signed char q3x03 = vec_sub(qxs03, qxh03); - vector signed char q3x10 = vec_sub(qxs10, qxh10); - vector signed char q3x11 = vec_sub(qxs11, qxh11); - vector signed char q3x12 = vec_sub(qxs12, qxh12); - vector signed char q3x13 = vec_sub(qxs13, qxh13); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y02 = vec_xl( 64, q8); - vector signed char q8y12 = vec_xl( 80, q8); - vector signed char q8y03 = vec_xl( 96, q8); - vector signed char q8y13 = vec_xl(112, q8); - q8 += 128; - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - vector signed short vs4 = vec_splat(vscales_h, 4); - vector signed short vs5 = vec_splat(vscales_h, 5); - vector signed short vs6 = vec_splat(vscales_h, 6); - vector signed short vs7 = vec_splat(vscales_h, 7); - vscales = vec_sld(vscales, vscales, 8); - - vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); - vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); - vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); - vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); - vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); - vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); - - vsumi0 = vec_msum(qv00, vs0, vsumi0); - vsumi1 = vec_msum(qv01, vs2, vsumi1); - vsumi2 = vec_msum(qv02, vs4, vsumi2); - vsumi3 = vec_msum(qv03, vs6, vsumi3); - vsumi4 = vec_msum(qv10, vs1, vsumi4); - vsumi5 = vec_msum(qv11, vs3, vsumi5); - vsumi6 = vec_msum(qv12, vs5, vsumi6); - vsumi7 = vec_msum(qv13, vs7, vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - const __m128i m32 = __lsx_vreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint32_t aux[3]; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - // Set up scales - memcpy(aux, x[i].scales, 12); - __m128i scales128 = lsx_set_w( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = __lsx_vsub_b(scales128, m32); - const __m256i all_scales = lasx_ext8_16(scales128); - const __m128i l_scales = lasx_extracti128(all_scales, 0); - const __m128i h_scales = lasx_extracti128(all_scales, 1); - const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; - - // high bit - const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); - - // integer accumulator - __m256i sumi = __lasx_xvldi(0); - - int bit = 0; - int is = 0; - __m256i xvbit; - - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits - const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; - - xvbit = __lasx_xvreplgr2vr_h(bit); - // prepare low and high bits - const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); - const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); - const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); - const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); - const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - // load Q8 quants - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); - __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); - __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); - __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); - - __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); - __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); - __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - p16_2 = __lasx_xvsub_h(p16_2, q8s_2); - p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - - // multiply with scales - p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); - p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); - p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); - p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); - - // accumulate - p16_0 = __lasx_xvadd_w(p16_0, p16_1); - p16_2 = __lasx_xvadd_w(p16_2, p16_3); - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); - } - // multiply with block scale and accumulate - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME - } - - *s = hsum_float_8(acc); - -#else - // scalar version - // This function is written like this so the compiler can manage to vectorize most of it - // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the - // manually vectorized version above. Every other version I tried would run at least 4 times slower. - // The ideal situation would be if we could just write the code once, and the compiler would - // automatically produce the best possible set of machine instructions, instead of us having to manually - // write vectorized versions for AVX, ARM_NEON, etc. - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - uint32_t auxs[4]; - const int8_t * scales = (const int8_t*)auxs; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - q3 += 32; - } - a = aux8; - - memcpy(auxs, x[i].scales, 12); - uint32_t tmp = auxs[2]; - auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); - auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); - auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); - auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); - for (int j = 0; j < QK_K/16; ++j) { - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; - -#endif - -} - -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - static const uint32_t kmask1 = 0x3f3f3f3f; - static const uint32_t kmask2 = 0x0f0f0f0f; - static const uint32_t kmask3 = 0x03030303; - - uint32_t utmp[4]; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x2_t q4bytes; - ggml_int8x16x2_t q8bytes; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); - - memcpy(utmp, x[i].scales, 12); - - uint32x2_t mins8 = { 0 }; - mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0); - mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1); - - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[0] &= kmask1; - - const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8))); - const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), - vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); - sumf -= dmin * vaddvq_s32(prod); - - const uint8_t * scales = (const uint8_t *)utmp; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - int32_t sumi1 = 0; - int32_t sumi2 = 0; - - for (int j = 0; j < QK_K/64; ++j) { - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; - - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - sumi1 += vaddvq_s32(p1) * scales[2*j+0]; - - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - - sumi2 += vaddvq_s32(p2) * scales[2*j+1]; - } - - sumf += d * (sumi1 + sumi2); - - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - __m128 acc_m = _mm_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); - const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); - const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); - acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m); - - const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); - const __m256i scales = MM256_SET_M128I(sc128, sc128); - - __m256i sumi = _mm256_setzero_si256(); - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4l = _mm256_and_si256(q4bits, m4); - const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); - - const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); - p16l = _mm256_madd_epi16(scale_l, p16l); - - const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); - p16h = _mm256_madd_epi16(scale_h, p16h); - const __m256i sumj = _mm256_add_epi32(p16l, p16h); - - sumi = _mm256_add_epi32(sumi, sumj); - } - - __m256 vd = _mm256_set1_ps(d); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); - - } - - acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); - acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); - - *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(0x2); - - __m256 acc = _mm256_setzero_ps(); - __m128 acc_m = _mm_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); - const __m128i scales = _mm_cvtepu8_epi16(utmps); - const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); - - const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); - const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); - const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); - const __m128i prod = _mm_madd_epi16(mins, q8s); - acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - __m128i shuffle = _mm_set1_epi16(0x0100); - for (int j = 0; j < QK_K/64; ++j) { - - const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - - __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4l_0 = _mm_and_si128(q4bits, m4); - const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); - q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4l_1 = _mm_and_si128(q4bits, m4); - const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); - - const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0); - p16l = _mm_madd_epi16(scale_l, p16l); - sumi_0 = _mm_add_epi32(sumi_0, p16l); - const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - p16l = _mm_maddubs_epi16(q4l_1, q8l_1); - p16l = _mm_madd_epi16(scale_l, p16l); - sumi_1 = _mm_add_epi32(sumi_1, p16l); - - const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0); - p16h = _mm_madd_epi16(scale_h, p16h); - sumi_0 = _mm_add_epi32(sumi_0, p16h); - const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - p16h = _mm_maddubs_epi16(q4h_1, q8h_1); - p16h = _mm_madd_epi16(scale_h, p16h); - sumi_1 = _mm_add_epi32(sumi_1, p16h); - - } - - __m256 vd = _mm256_set1_ps(d); - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); - - } - - acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); - acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); - - *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); - -#elif defined __riscv_v_intrinsic - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - size_t vl = 8; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); - vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); - vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); - vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); - vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); - - vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - vl = 32; - - int32_t sum_1 = 0; - int32_t sum_2 = 0; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - for (int j = 0; j < QK_K/64; ++j) { - // load Q4 - vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); - - // load Q8 and multiply it with lower Q4 nibble - vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q4_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); - vint16m2_t qv_0 = __riscv_vwmul_vv_i16m2(q4_0, q8_0, vl); - vint16m1_t vs_0 = __riscv_vredsum_vs_i16m2_i16m1(qv_0, vzero, vl); - - sum_1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[2*j+0]; - - // load Q8 and multiply it with upper Q4 nibble - vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); - vint8m1_t q4_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); - vint16m2_t qv_1 = __riscv_vwmul_vv_i16m2(q4_1, q8_1, vl); - vint16m1_t vs_1 = __riscv_vredsum_vs_i16m2_i16m1(qv_1, vzero, vl); - - sum_2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[2*j+1]; - - q4 += 32; q8 += 64; - - } - - sumf += d*(sum_1 + sum_2); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed char lowMask1 = vec_splats((int8_t)0x3f); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((uint8_t)2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - UNUSED(kmask1); - UNUSED(kmask2); - UNUSED(kmask3); - UNUSED(utmp); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = vec_sr(u2, v4); - - vector signed char u30 = u1; - vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); - - u1 = vec_and(u0, lowMask1); - u2 = vec_or(u30, u31); - - vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); - - vector signed short vscales = vec_unpackh(utmps); - vector signed short q4xmins = vec_unpackl(utmps); - vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); - vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); - - vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); - vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); - vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); - vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; j+=2) { - __builtin_prefetch(q4, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); - vector signed char qxs1 = (vector signed char)vec_xl(16, q4); - vector signed char qxs2 = (vector signed char)vec_xl(32, q4); - vector signed char qxs3 = (vector signed char)vec_xl(48, q4); - q4 += 64; - - vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask); - vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4); - vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask); - vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4); - vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask); - vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4); - vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask); - vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y20 = vec_xl( 64, q8); - vector signed char q8y30 = vec_xl( 80, q8); - vector signed char q8y21 = vec_xl( 96, q8); - vector signed char q8y31 = vec_xl(112, q8); - q8 += 128; - - vector signed int qv00 = vec_msum(q8y00, q4x00, v0); - vector signed int qv01 = vec_msum(q8y01, q4x01, v0); - vector signed int qv10 = vec_msum(q8y10, q4x10, v0); - vector signed int qv11 = vec_msum(q8y11, q4x11, v0); - vector signed int qv20 = vec_msum(q8y20, q4x20, v0); - vector signed int qv21 = vec_msum(q8y21, q4x21, v0); - vector signed int qv30 = vec_msum(q8y30, q4x30, v0); - vector signed int qv31 = vec_msum(q8y31, q4x31, v0); - - vector signed int vscales_h = vec_unpackh(vscales); - vector signed int vs0 = vec_splat(vscales_h, 0); - vector signed int vs1 = vec_splat(vscales_h, 1); - vector signed int vs2 = vec_splat(vscales_h, 2); - vector signed int vs3 = vec_splat(vscales_h, 3); - vscales = vec_sld(vscales, vscales, 8); - - vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1); - vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2); - vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3); - - vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1); - vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2); - vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - GGML_UNUSED(kmask1); - GGML_UNUSED(kmask2); - GGML_UNUSED(kmask3); - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - __m128 acc_m = (__m128)__lsx_vldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); - const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); - const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); - acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); - - const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); - const __m256i scales = lasx_insertf128(sc128, sc128); - - __m256i sumi = __lasx_xvldi(0); - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4l = __lasx_xvand_v(q4bits, m4); - const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); - - const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - __m256i p16l = lasx_maddubs_h(q4l, q8l); - p16l = lasx_madd_h(scale_l, p16l); - - const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - __m256i p16h = lasx_maddubs_h(q4h, q8h); - p16h = lasx_madd_h(scale_h, p16h); - const __m256i sumj = __lasx_xvadd_w(p16l, p16h); - - sumi = __lasx_xvadd_w(sumi, sumj); - } - - __m256 vd = __lasx_xvreplfr2vr_s(d); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); - - } - - acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); - __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); - acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); - - - ft_union fi; - fi.i = __lsx_vpickve2gr_w(acc_m, 0); - *s = hsum_float_8(acc) + fi.f ; -#else - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - a += 32; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - a += 32; q4 += 32; - } - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - static const uint32_t kmask1 = 0x3f3f3f3f; - static const uint32_t kmask2 = 0x0f0f0f0f; - static const uint32_t kmask3 = 0x03030303; - - uint32_t utmp[4]; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const uint8x16_t mone = vdupq_n_u8(1); - const uint8x16_t mtwo = vdupq_n_u8(2); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q5bytes; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8x8_t mins8 = vld1_u8((const uint8_t*)utmp + 8); - const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(mins8)); - const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), - vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); - int32_t sumi_mins = vaddvq_s32(prod); - - const uint8_t * scales = (const uint8_t *)utmp; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - - ggml_uint8x16x4_t q5h; - - int32_t sumi = 0; - - for (int j = 0; j < QK_K/64; ++j) { - - const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); - q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); - q5h.val[2] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[0]), 3); - q5h.val[3] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[1]), 3); - qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 2); - qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 2); - - q5bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[0], m4b), q5h.val[0])); - q5bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[1], m4b), q5h.val[1])); - q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); - q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); - - sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; - sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; - } - - sumf += d * sumi - dmin * sumi_mins; - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m128i mzero = _mm_setzero_si128(); - const __m256i mone = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); - const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); - const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); - const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); - summs += dmin * _mm_extract_epi32(hsum, 0); - - const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); - const __m256i scales = MM256_SET_M128I(sc128, sc128); - - const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh); - __m256i hmask = mone; - - __m256i sumi = _mm256_setzero_si256(); - - int bit = 0; - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); q5 += 32; - - const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); - const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); - const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); - hmask = _mm256_slli_epi16(hmask, 1); - - const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); - const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); - const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); - hmask = _mm256_slli_epi16(hmask, 1); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - __m256i p16_0 = _mm256_maddubs_epi16(q5_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q5_1, q8_1); - - p16_0 = _mm256_madd_epi16(scale_0, p16_0); - p16_1 = _mm256_madd_epi16(scale_1, p16_1); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - - } - - __m256 vd = _mm256_set1_ps(d); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i mzero = _mm_setzero_si128(); - const __m128i mone = _mm_set1_epi8(1); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); - const __m128i scales = _mm_cvtepu8_epi16(utmps); - const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); - - const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); - const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); - const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); - const __m128i prod = _mm_madd_epi16(mins, q8s); - const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); - summs += dmin * _mm_extract_epi32(hsum, 0); - - const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]); - const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]); - __m128i hmask = mone; - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - int bit = 0; - - __m128i shuffle = _mm_set1_epi16(0x0100); - for (int j = 0; j < QK_K/64; ++j) { - - const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - - const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; - const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; - - __m128i q5l_0 = _mm_and_si128(q5bits_0, m4); - __m128i q5l_1 = _mm_and_si128(q5bits_1, m4); - __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); - __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); - __m128i q5_0 = _mm_add_epi8(q5l_0, q5h_0); - __m128i q5_1 = _mm_add_epi8(q5l_1, q5h_1); - hmask = _mm_slli_epi16(hmask, 1); - - __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1); - p16_0 = _mm_madd_epi16(scale_0, p16_0); - p16_1 = _mm_madd_epi16(scale_0, p16_1); - - q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4); - q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4); - q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); - q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); - q5_0 = _mm_add_epi8(q5l_0, q5h_0); - q5_1 = _mm_add_epi8(q5l_1, q5h_1); - hmask = _mm_slli_epi16(hmask, 1); - - q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0); - __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1); - p16_2 = _mm_madd_epi16(scale_1, p16_2); - p16_3 = _mm_madd_epi16(scale_1, p16_3); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - - } - - __m256 vd = _mm256_set1_ps(d); - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __riscv_v_intrinsic - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - float sumf = 0; - float sums = 0.0; - - size_t vl; - - for (int i = 0; i < nb; ++i) { - - vl = 8; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - - vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); - vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); - vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); - vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); - vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); - - vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); - - vl = 32; - int32_t aux32 = 0; - int is = 0; - - uint8_t m = 1; - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t vqh = __riscv_vle8_v_u8m1(hm, vl); - - for (int j = 0; j < QK_K/64; ++j) { - // load Q5 and Q8 - vuint8m1_t q5_x = __riscv_vle8_v_u8m1(q5, vl); - vint8m1_t q8_y1 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q8_y2 = __riscv_vle8_v_i8m1(q8+32, vl); - - // compute mask for addition - vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl)); - vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl); - vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl); - m <<= 1; - - vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl)); - vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl); - vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl); - m <<= 1; - - vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl); - vint16m2_t v1 = __riscv_vwmul_vv_i16m2(q5_m2, q8_y2, vl); - - vint32m4_t vs1 = __riscv_vwmul_vx_i32m4(v0, scales[is++], vl); - vint32m4_t vs2 = __riscv_vwmul_vx_i32m4(v1, scales[is++], vl); - - vint32m1_t vacc1 = __riscv_vredsum_vs_i32m4_i32m1(vs1, vzero, vl); - vint32m1_t vacc2 = __riscv_vredsum_vs_i32m4_i32m1(vs2, vzero, vl); - - aux32 += __riscv_vmv_x_s_i32m1_i32(vacc1) + __riscv_vmv_x_s_i32m1_i32(vacc2); - q5 += 32; q8 += 64; - - } - - vfloat32m1_t vaux = __riscv_vfmul_vf_f32m1(__riscv_vfmv_v_f_f32m1(aux32, 1), d, 1); - sums += __riscv_vfmv_f_s_f32m1_f32(vaux); - - } - - *s = sumf+sums; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed char lowMask1 = vec_splats((int8_t)0x3f); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v1 = vec_splats((unsigned char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - UNUSED(kmask1); - UNUSED(kmask2); - UNUSED(kmask3); - UNUSED(utmp); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = vec_sr(u2, v4); - - vector signed char u30 = u1; - vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); - - u1 = vec_and(u0, lowMask1); - u2 = vec_or(u30, u31); - - vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - vector signed short vscales = vec_unpackh(utmps); - - vector signed short q5xmins = vec_unpackl(utmps); - vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); - vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); - - vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); - vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); - vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); - vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); - vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; ++j) { - __builtin_prefetch(q5, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); - vector signed char qxs1 = (vector signed char)vec_xl(16, q5); - q5 += 32; - - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - - vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); - vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); - vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); - vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); - qxhs0 = vec_sr(qxhs0, v2); - qxhs1 = vec_sr(qxhs1, v2); - - vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00); - vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01); - vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10); - vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl(16, q8); - vector signed char q8y01 = vec_xl(32, q8); - vector signed char q8y11 = vec_xl(48, q8); - q8 += 64; - - vector signed int qv00 = vec_msum(q8y00, q5x00, v0); - vector signed int qv01 = vec_msum(q8y01, q5x01, v0); - vector signed int qv10 = vec_msum(q8y10, q5x10, v0); - vector signed int qv11 = vec_msum(q8y11, q5x11, v0); - - vector signed int vscales_h = vec_unpackh(vscales); - vector signed int vs0 = vec_splat(vscales_h, 0); - vector signed int vs1 = vec_splat(vscales_h, 1); - vscales = vec_sld(vscales, vscales, 12); - - vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1); - vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2); - vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - GGML_UNUSED(kmask1); - GGML_UNUSED(kmask2); - GGML_UNUSED(kmask3); - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m128i mzero = __lsx_vldi(0); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); - const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); - const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); - const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); - summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check - - const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); - const __m256i scales = lasx_insertf128(sc128, sc128); - - const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); - __m256i hmask = mone; - - __m256i sumi = __lasx_xvldi(0); - - int bit = 0; - __m256i xvbit; - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; - - xvbit = __lasx_xvreplgr2vr_h(bit++); - const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); - const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); - hmask = __lasx_xvslli_h(hmask, 1); - - xvbit = __lasx_xvreplgr2vr_h(bit++); - const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); - const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); - hmask = __lasx_xvslli_h(hmask, 1); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); - - p16_0 = lasx_madd_h(scale_0, p16_0); - p16_1 = lasx_madd_h(scale_1, p16_1); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - - } - - __m256 vd = __lasx_xvreplfr2vr_s(d); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#else - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - q4 += 32; - } - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q6_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - float sum = 0; - - const uint8x16_t m4b = vdupq_n_u8(0xF); - const int32x4_t vzero = vdupq_n_s32(0); - //const int8x16_t m32s = vdupq_n_s8(32); - - const uint8x16_t mone = vdupq_n_u8(3); - - ggml_int8x16x4_t q6bytes; - ggml_uint8x16x4_t q6h; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); - const int8x16_t scales = vld1q_s8(scale); - const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}}; - - const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), - vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), - vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[1]), vget_low_s16 (q6scales.val[1])), - vmull_s16(vget_high_s16(q8sums.val[1]), vget_high_s16(q6scales.val[1])))); - int32_t isum_mins = vaddvq_s32(prod); - - int32_t isum = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; - ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; - ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); - uint8x16_t shifted = vshrq_n_u8(qhbits.val[0], 2); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 2); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); - //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); - //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])), m32s); - //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])), m32s); - q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])); - q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])); - q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); - q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - - scale += 4; - - q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - shifted = vshrq_n_u8(qhbits.val[0], 4); - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 4); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[0], 6); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 6); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])), m32s); - //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])), m32s); - //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])), m32s); - //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])), m32s); - q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])); - q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])); - q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); - q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - scale += 4; - } - //sum += isum * d_all * y[i].d; - sum += d_all * y[i].d * (isum - 32 * isum_mins); - - } - *s = sum; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i m2 = _mm256_set1_epi8(3); - const __m256i m32s = _mm256_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); - - __m256i sumi = _mm256_setzero_si256(); - - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); - const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); - const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); - const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); - is += 4; - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32; - - const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4); - const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4); - const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4); - const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4); - - const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); - const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1); - const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2); - const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); - __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2); - __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3); - - __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); - __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2); - __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - p16_2 = _mm256_sub_epi16(p16_2, q8s_2); - p16_3 = _mm256_sub_epi16(p16_3, q8s_3); - - p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); - p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2); - p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3)); - - } - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m3 = _mm_set1_epi8(3); - const __m128i m32s = _mm_set1_epi8(32); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - __m128i shuffle = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16; - const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16; - - const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4); - const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4); - const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 2), m3), 4); - const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 2), m3), 4); - const __m128i q4h_4 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 4), m3), 4); - const __m128i q4h_5 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 4), m3), 4); - const __m128i q4h_6 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 6), m3), 4); - const __m128i q4h_7 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 6), m3), 4); - - const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - - const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m4), q4h_0); - const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m4), q4h_1); - const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m4), q4h_2); - const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m4), q4h_3); - const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m4), q4h_4); - const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m4), q4h_5); - const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m4), q4h_6); - const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m4), q4h_7); - - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - __m128i q8s_0 = _mm_maddubs_epi16(m32s, q8_0); - __m128i q8s_1 = _mm_maddubs_epi16(m32s, q8_1); - __m128i q8s_2 = _mm_maddubs_epi16(m32s, q8_2); - __m128i q8s_3 = _mm_maddubs_epi16(m32s, q8_3); - __m128i q8s_4 = _mm_maddubs_epi16(m32s, q8_4); - __m128i q8s_5 = _mm_maddubs_epi16(m32s, q8_5); - __m128i q8s_6 = _mm_maddubs_epi16(m32s, q8_6); - __m128i q8s_7 = _mm_maddubs_epi16(m32s, q8_7); - - __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1); - __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2); - __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3); - __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4); - __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5); - __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6); - __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - p16_4 = _mm_sub_epi16(p16_4, q8s_4); - p16_5 = _mm_sub_epi16(p16_5, q8s_5); - p16_6 = _mm_sub_epi16(p16_6, q8s_6); - p16_7 = _mm_sub_epi16(p16_7, q8s_7); - - const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_2 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_3 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - - p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); - p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); - p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); - p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4); - p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_2, scale_2)), p16_5); - p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6); - p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_3, scale_3)), p16_7); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7)); - - } - - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - size_t vl; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - int sum_t = 0; - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - vl = 32; - - // load qh - vuint8m1_t qh_x = __riscv_vle8_v_u8m1(qh, vl); - - // load Q6 - vuint8m1_t q6_0 = __riscv_vle8_v_u8m1(q6, vl); - vuint8m1_t q6_1 = __riscv_vle8_v_u8m1(q6+32, vl); - - vuint8m1_t q6a_0 = __riscv_vand_vx_u8m1(q6_0, 0x0F, vl); - vuint8m1_t q6a_1 = __riscv_vand_vx_u8m1(q6_1, 0x0F, vl); - vuint8m1_t q6s_0 = __riscv_vsrl_vx_u8m1(q6_0, 0x04, vl); - vuint8m1_t q6s_1 = __riscv_vsrl_vx_u8m1(q6_1, 0x04, vl); - - vuint8m1_t qh_0 = __riscv_vand_vx_u8m1(qh_x, 0x03, vl); - vuint8m1_t qh_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x2, vl), 0x03 , vl); - vuint8m1_t qh_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x4, vl), 0x03 , vl); - vuint8m1_t qh_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x6, vl), 0x03 , vl); - - vuint8m1_t qhi_0 = __riscv_vor_vv_u8m1(q6a_0, __riscv_vsll_vx_u8m1(qh_0, 0x04, vl), vl); - vuint8m1_t qhi_1 = __riscv_vor_vv_u8m1(q6a_1, __riscv_vsll_vx_u8m1(qh_1, 0x04, vl), vl); - vuint8m1_t qhi_2 = __riscv_vor_vv_u8m1(q6s_0, __riscv_vsll_vx_u8m1(qh_2, 0x04, vl), vl); - vuint8m1_t qhi_3 = __riscv_vor_vv_u8m1(q6s_1, __riscv_vsll_vx_u8m1(qh_3, 0x04, vl), vl); - - vint8m1_t a_0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_0), 32, vl); - vint8m1_t a_1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_1), 32, vl); - vint8m1_t a_2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_2), 32, vl); - vint8m1_t a_3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_3), 32, vl); - - // load Q8 and take product - vint16m2_t va_q_0 = __riscv_vwmul_vv_i16m2(a_0, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m2_t va_q_1 = __riscv_vwmul_vv_i16m2(a_1, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m2_t va_q_2 = __riscv_vwmul_vv_i16m2(a_2, __riscv_vle8_v_i8m1(q8+64, vl), vl); - vint16m2_t va_q_3 = __riscv_vwmul_vv_i16m2(a_3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - - vl = 16; - - vint32m2_t vaux_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 0), scale[is+0], vl); - vint32m2_t vaux_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 1), scale[is+1], vl); - vint32m2_t vaux_2 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 0), scale[is+2], vl); - vint32m2_t vaux_3 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 1), scale[is+3], vl); - vint32m2_t vaux_4 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 0), scale[is+4], vl); - vint32m2_t vaux_5 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 1), scale[is+5], vl); - vint32m2_t vaux_6 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 0), scale[is+6], vl); - vint32m2_t vaux_7 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 1), scale[is+7], vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_0, vaux_1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_2, vaux_3, vl), isum0, vl); - vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_4, vaux_5, vl), isum1, vl); - vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_6, vaux_7, vl), isum2, vl); - - sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - - q6 += 64; qh += 32; q8 += 128; is=8; - - } - - sumf += d * sum_t; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict qs = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q6, 0, 0); - __builtin_prefetch(qh, 0, 0); - __builtin_prefetch(q8, 0, 0); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); - vector signed char qxs1 = (vector signed char)vec_xl(16, q6); - vector signed char qxs2 = (vector signed char)vec_xl(32, q6); - vector signed char qxs3 = (vector signed char)vec_xl(48, q6); - q6 += 64; - - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - vector signed char qxs20 = vec_and(qxs2, lowMask); - vector signed char qxs21 = vec_sr(qxs2, v4); - vector signed char qxs30 = vec_and(qxs3, lowMask); - vector signed char qxs31 = vec_sr(qxs3, v4); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); - vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); - qh += 32; - - vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); - vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); - vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); - vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); - vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); - vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); - vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); - vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); - - vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); - vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); - vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); - vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); - vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); - vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); - vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); - vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y20 = vec_xl( 32, q8); - vector signed char q8y30 = vec_xl( 48, q8); - vector signed char q8y01 = vec_xl( 64, q8); - vector signed char q8y11 = vec_xl( 80, q8); - vector signed char q8y21 = vec_xl( 96, q8); - vector signed char q8y31 = vec_xl(112, q8); - q8 += 128; - - vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); - vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); - vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); - vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); - vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); - vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); - - vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); - qs += 8; - - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); - vector signed short vs2 = vec_splat(vscales, 2); - vector signed short vs3 = vec_splat(vscales, 3); - vector signed short vs4 = vec_splat(vscales, 4); - vector signed short vs5 = vec_splat(vscales, 5); - vector signed short vs6 = vec_splat(vscales, 6); - vector signed short vs7 = vec_splat(vscales, 7); - - vsumi0 = vec_msum(qv00, vs0, vsumi0); - vsumi1 = vec_msum(qv01, vs4, vsumi1); - vsumi2 = vec_msum(qv10, vs1, vsumi2); - vsumi3 = vec_msum(qv11, vs5, vsumi3); - vsumi4 = vec_msum(qv20, vs2, vsumi4); - vsumi5 = vec_msum(qv21, vs6, vsumi5); - vsumi6 = vec_msum(qv30, vs3, vsumi6); - vsumi7 = vec_msum(qv31, vs7, vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i m2 = __lasx_xvreplgr2vr_b(3); - const __m256i m32s = __lasx_xvreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); - - __m256i sumi = __lasx_xvldi(0); - - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); - const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); - const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); - const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); - is += 4; - - const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; - - const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); - const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); - const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); - const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); - - const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); - const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); - const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); - const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); - __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); - __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); - __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); - - __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); - __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); - __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - p16_2 = __lasx_xvsub_h(p16_2, q8s_2); - p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - - p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); - p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); - p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); - p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); - } - - acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) { - a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - } - a += 128; - q4 += 64; - qh += 32; - } - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/16; ++j) { - int scale = x[i].scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#if defined (__AVX__) || defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) -static const int8_t keven_signs_q2xs[1024] = { - 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, - 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, - 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, - 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, - 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, - 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, - 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, - 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, - 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, - 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, - 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, - 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, - 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, - 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, - 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, - 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, - 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, - 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, - 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, - 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, - 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, - 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, - 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, - 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, - 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, - 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, - 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, - 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, - 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, - 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, - 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, - 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -}; -#endif - -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_xxs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - ggml_int8x16x4_t q2u; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - float sumf1 = 0, sumf2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); - q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); - q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 8])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 9]))); - q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[10])), vld1_s8((const void *)(iq2xxs_grid + aux8[11]))); - q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); - q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); - q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 7) & 127)))); - q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 21) & 127)))); - q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); - q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); - q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); - q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]), q2u.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]), q2u.val[3], q8b.val[3]); - sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[1] >> 28)); - sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[3] >> 28)); - } - sumf += d*(sumf1 + sumf2); - } - *s = 0.25f * sumf; - -#elif defined(__AVX2__) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], - signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - const __m128i q2_1_0 = _mm_set_epi64x(iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2xxs_grid[aux8[3]], iq2xxs_grid[aux8[2]]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]]); - const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); - const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127]); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - const vector int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - memcpy(aux32, q2, 4*sizeof(uint32_t)); - q2 += 8; - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; - - vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; - vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; - vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; - vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; - - vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); - vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); - vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); - vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = aux32[1] >> 28; - const uint16_t ls1 = aux32[3] >> 28; - - vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - - const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], - signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); - const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); - -#else - - uint32_t aux32[2]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - memcpy(aux32, q2, 2*sizeof(uint32_t)); - q2 += 4; - const uint32_t ls = 2*(aux32[1] >> 28) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); - const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls; - } - sumf += d * bsum; - } - *s = 0.125f * sumf; -#endif -} - -void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_xs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - ggml_int8x16x4_t q2u; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - int32x4x4_t scales32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint8x8_t scales8 = vld1_u8(x[i].scales); - const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf)); - const uint8x8_t scales_h = vshr_n_u8(scales8, 4); - uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h)); - scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1)); - const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales)); - const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales)); - scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1))); - scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1))); - scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2))); - scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); - int32x4_t sumi = vdupq_n_s32(0); - for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); - q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); - q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); - q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511)))); - q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9)))); - q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9)))); - q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9)))); - q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9)))); - q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); - q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); - q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); - q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); - const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]); - const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]); - const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]); - const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]); - const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4)); - sumi = vmlaq_s32(sumi, p, scales32.val[ib64]); - q2 += 8; - } - sumf += d*vaddvq_s32(sumi); - } - *s = 0.125f * sumf; - -#elif defined(__AVX2__) - - const __m256i mone = _mm256_set1_epi8(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); - const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); - const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); - const __m256i m511 = _mm256_set1_epi16(511); - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = _mm_set1_epi64x(aux64); - stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); - const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2); q2 += 16; - aux_gindex = _mm256_and_si256(q2_data, m511); - - const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9); - const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13); - const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper); - - const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); - const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits); - - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], - iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], - iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); - const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], - iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); - const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], - iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - - const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); - const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); - const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); - const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); - - __m256i signs; - signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone)); - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const __m256i dot3 = _mm256_maddubs_epi16(q2_3, q8s_3); - const __m256i dot4 = _mm256_maddubs_epi16(q2_4, q8s_4); - - const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); - const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); - const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2))); - const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3))); - - sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); - sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4)); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const __m128i mone = _mm_set1_epi8(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i bit_selector_mask_0 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes); - const __m128i bit_selector_mask_1 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes + 1); - const __m128i block_sign_shuffle_1_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1); - const __m128i block_sign_shuffle_1_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1 + 1); - const __m128i block_sign_shuffle_2_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2); - const __m128i block_sign_shuffle_2_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2 + 1); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper_0 = _mm_loadu_si128((const __m128i*)k_bit_helper); - const __m128i bit_helper_1 = _mm_loadu_si128((const __m128i*)k_bit_helper + 1); - const __m128i m511 = _mm_set1_epi16(511); - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = _mm_set1_epi64x(aux64); - stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); - const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m128i q2_data_0 = _mm_loadu_si128((const __m128i*)q2); - const __m128i q2_data_1 = _mm_loadu_si128((const __m128i*)q2 + 1); q2 += 16; - aux_gindex = MM256_SET_M128I(_mm_and_si128(q2_data_1, m511), _mm_and_si128(q2_data_0, m511)); - - const __m128i partial_sign_bits_0 = _mm_srli_epi16(q2_data_0, 9); - const __m128i partial_sign_bits_1 = _mm_srli_epi16(q2_data_1, 9); - const __m128i partial_sign_bits_upper_0 = _mm_srli_epi16(q2_data_0, 13); - const __m128i partial_sign_bits_upper_1 = _mm_srli_epi16(q2_data_1, 13); - const __m128i partial_sign_bits_for_counting_0 = _mm_xor_si128(partial_sign_bits_0, partial_sign_bits_upper_0); - const __m128i partial_sign_bits_for_counting_1 = _mm_xor_si128(partial_sign_bits_1, partial_sign_bits_upper_1); - - const __m128i odd_bits_0 = _mm_shuffle_epi8(bit_helper_0, partial_sign_bits_for_counting_0); - const __m128i odd_bits_1 = _mm_shuffle_epi8(bit_helper_1, partial_sign_bits_for_counting_1); - const __m128i full_sign_bits_0 = _mm_or_si128(partial_sign_bits_0, odd_bits_0); - const __m128i full_sign_bits_1 = _mm_or_si128(partial_sign_bits_1, odd_bits_1); - - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_3_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_3_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_4_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_4_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i q2_1_0 = _mm_set_epi64x(iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]]); - const __m128i q2_3_0 = _mm_set_epi64x(iq2xs_grid[gindex[9]], iq2xs_grid[gindex[8]]); - const __m128i q2_3_1 = _mm_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]]); - const __m128i q2_4_0 = _mm_set_epi64x(iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - const __m128i q2_4_1 = _mm_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]]); - - // AVX2 full_signs_1 is full_sign_bits_0 here - // AVX2 full_signs_2 is full_sign_bits_1 here - __m128i signs_0, signs_1; - signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_3_0 = _mm_sign_epi8(q8_3_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_3_1 = _mm_sign_epi8(q8_3_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_4_0 = _mm_sign_epi8(q8_4_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_4_1 = _mm_sign_epi8(q8_4_1, _mm_or_si128(signs_1, mone)); - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const __m128i dot3_0 = _mm_maddubs_epi16(q2_3_0, q8s_3_0); - const __m128i dot3_1 = _mm_maddubs_epi16(q2_3_1, q8s_3_1); - const __m128i dot4_0 = _mm_maddubs_epi16(q2_4_0, q8s_4_0); - const __m128i dot4_1 = _mm_maddubs_epi16(q2_4_1, q8s_4_1); - - __m128i sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0)); - const __m128i sc1_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc1_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1)); - const __m128i sc2_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc2_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2)); - const __m128i sc3_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc3_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3)); - const __m128i sc4_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc4_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot1_0, sc1_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot1_1, sc1_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot2_0, sc2_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot2_1, sc2_1)); - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot3_0, sc3_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot3_1, sc3_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot4_0, sc4_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot4_1, sc4_1)); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__loongarch_asx) - - const __m256i mone = __lasx_xvreplgr2vr_b(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); - const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); - const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); - const __m256i m511 = __lasx_xvreplgr2vr_h(511); - const __m128i m4 = __lsx_vreplgr2vr_b(0xf); - const __m128i m1 = __lsx_vreplgr2vr_b(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = __lsx_vreplgr2vr_d(aux64); - stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); - const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); - - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; - aux_gindex = __lasx_xvand_v(q2_data, m511); - - const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); - const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); - const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); - - const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); - const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); - - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - - const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], - iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); - const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], - iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); - const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], - iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); - const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], - iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - - const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); - const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); - const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); - const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); - - __m256i signs; - signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); - - signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); - - signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); - - signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); - const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); - - const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); - const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); - const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); - const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); - - sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); - sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); - sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); - sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); -#elif defined(__POWER9_VECTOR__) - const vector int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint16_t * restrict q2 = x[i].qs; - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; ++j) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; - - vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; - vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; - vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; - vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; - q2 += 8; - - vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); - vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); - vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); - vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); - const uint16_t ls3 = (uint16_t)(sc[1] >> 4); - sc += 2; - - vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); - vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); - vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - - vsumi0 = vec_msum(qv0, vscales0, vsumi0); - vsumi1 = vec_msum(qv1, vscales1, vsumi1); - vsumi2 = vec_msum(qv2, vscales2, vsumi2); - vsumi3 = vec_msum(qv3, vscales3, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); -#else - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1; - const uint16_t ls2 = 2*(sc[ib32] >> 4) + 1; - int32_t sumi = 0; - for (int l = 0; l < 2; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls1; - sumi = 0; - for (int l = 2; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls2; - q2 += 4; - } - sumf += d * bsum; - } - *s = 0.125f * sumf; -#endif -} - -void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); - const uint8x16_t m1 = vdupq_n_u8(1); - const int32x4_t vzero = vdupq_n_s32(0); - - uint8x16x2_t vs; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - int sumi1 = 0, sumi2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); - q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); - q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); - q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); - qs += 8; - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); - - q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); - q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); - - signs += 4; - - q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); - q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); - - const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); - const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); - const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); - const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); - - sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); - sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); - sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); - sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); - } - sumf += d*(sumi1 + sumi2); - } - - *s = 0.125f * sumf; - -#elif defined(__AVX2__) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); - const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); - - uint64_t aux64; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); - const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], - iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], - iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - qs += 8; - - __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); - - aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 - - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); - const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); - const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); - const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); - - uint64_t aux64; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); - const __m128i scales16_0 = _mm_cvtepi8_epi16(scales8); - const __m128i scales16_1 = _mm_cvtepi8_epi16(_mm_srli_si128(scales8, 8)); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q2_1_0 = _mm_set_epi64x(iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)]); - qs += 8; - - __m128i aux128_0 = _mm_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); - __m128i aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); - const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); - - aux128_0 = _mm_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); - aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); - const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); - - signs += 4; - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 0))); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 1))); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 0))); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 1))); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector unsigned char mask0 = vec_xl( 0, k_mask1); - const vector unsigned char mask1 = vec_xl(16, k_mask1); - const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q2 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; - q2 += 8; - qh += 2; - - vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); - vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); - signs += 4; - - vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); - vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); - vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); - vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); - - vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); - vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); - vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); - vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); - - vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); - vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); - vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); - vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); - const uint16_t ls3 = (uint16_t)(sc[1] >> 4); - sc += 2; - - vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); - vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); - vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - - vsumi0 = vec_msum(qv0, vscales0, vsumi0); - vsumi1 = vec_msum(qv1, vscales1, vsumi1); - vsumi2 = vec_msum(qv2, vscales2, vsumi2); - vsumi3 = vec_msum(qv3, vscales3, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - - const __m128i m4 = __lsx_vreplgr2vr_b(0xf); - const __m128i m1 = __lsx_vreplgr2vr_b(1); - - const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); - const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); - uint64_t aux64; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - __m128i tmp1; - memcpy(&aux64, x[i].scales, 8); - tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); - tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); - const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); - const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 - - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], - iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], - iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - qs += 8; - - __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); - - aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 - - const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); - const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); - -#else - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint8_t * signs = qs + QK_K/8; - - int bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); - int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); - int sumi1 = 0, sumi2 = 0; - for (int l = 0; l < 2; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - for (int l = 2; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); - for (int j = 0; j < 8; ++j) { - sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += ls1 * sumi1 + ls2 * sumi2; - qs += 4; - signs += 4; - } - - sumf += d * bsum; - } - - *s = 0.125f * sumf; - -#endif - -} - -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq3_xxs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - ggml_int8x16x4_t q3s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - float sumf1 = 0, sumf2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); - const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); - const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); - const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); - const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); - q3 += 16; - q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); - q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); - q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); - q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); - q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); - q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); - q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); - q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); - sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); - sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); - } - sumf += d*(sumf1 + sumf2); - } - *s = 0.5f * sumf; - -#elif defined(__AVX2__) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], - signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.25f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q2_1_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - const __m128i q2_1_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); - q3 += 8; - const __m128i q2_2_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - const __m128i q2_2_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127]); - const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.25f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q3 = x[i].qs; - const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); - const int8_t * restrict q8 = y[i].qs; - -#pragma GCC unroll 1 - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; - vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; - vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; - vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; - q3 += 16; - - vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; - vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; - vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; - vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; - - vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); - vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); - vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); - vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(signs[0] >> 28); - const uint16_t ls1 = (uint16_t)(signs[1] >> 28); - signs += 2; - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.25f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - - const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], - signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); - const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.25f * hsum_float_8(accumf); - -#else - - uint32_t aux32; - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); - const uint32_t ls = 2*(aux32 >> 28) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - q3 += 8; - bsum += sumi * ls; - } - sumf += d * bsum; - } - *s = 0.25f * sumf; -#endif -} - -void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq3_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - typedef union { - uint16x8_t vec_index; - uint16_t index[8]; - } vec_index_t; - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; - - const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); - - const int16x8_t hshift = vld1q_s16(k_shift); - const uint16x8_t m256 = vdupq_n_u16(256); - const uint8x16_t m1 = vdupq_n_u8(1); - - uint8x16x2_t vs; - ggml_int8x16x4_t q3s; - ggml_int8x16x4_t q8b; - vec_index_t idx; - - uint32_t scales32[2]; - const uint8_t * scales8 = (const uint8_t *)scales32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(scales32, x[i].scales, 4); - scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; - scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; - - int sumi1 = 0, sumi2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; - idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); - const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); - const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); - idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); - const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); - const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); - - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); - vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - - q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); - q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); - vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - - signs += 4; - - q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); - q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); - - sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; - sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; - } - sumf += d*(sumi1 + sumi2); - } - *s = sumf; - -#elif defined(__AVX2__) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); - const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); - - const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); - const __m256i idx_mask = _mm256_set1_epi32(256); - - typedef union { - __m256i vec[2]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; - idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); - idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); - idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); - idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); - idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); - idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); - - // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. - //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); - //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); - const __m256i q2_1 = _mm256_set_epi32( - iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] - ); - const __m256i q2_2 = _mm256_set_epi32( - iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], - iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] - ); - - __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); - - aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = hsum_float_8(accumf); - -#elif defined(__AVX__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); - const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); - const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); - const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); - - const __m128i idx_mul_0 = _mm_set_epi32(32, 64, 128, 256); - const __m128i idx_mul_1 = _mm_set_epi32(2, 4, 8, 16); - const __m128i idx_mask = _mm_set1_epi32(256); - - typedef union { - __m128i vec[4]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i qs_tmp = _mm_loadu_si128((const __m128i *)qs); - const __m128i idx_l_0 = _mm_cvtepu8_epi16(qs_tmp); - const __m128i idx_l_1 = _mm_cvtepu8_epi16(_mm_srli_si128(qs_tmp, 8)); qs += 16; - idx.vec[0] = _mm_set1_epi32(qh[ib32+0]); - idx.vec[1] = idx.vec[0]; - idx.vec[2] = _mm_set1_epi32(qh[ib32+1]); - idx.vec[3] = idx.vec[2]; - - idx.vec[0] = _mm_and_si128(_mm_mullo_epi32(idx.vec[0], idx_mul_0), idx_mask); - idx.vec[1] = _mm_and_si128(_mm_mullo_epi32(idx.vec[1], idx_mul_1), idx_mask); - idx.vec[2] = _mm_and_si128(_mm_mullo_epi32(idx.vec[2], idx_mul_0), idx_mask); - idx.vec[3] = _mm_and_si128(_mm_mullo_epi32(idx.vec[3], idx_mul_1), idx_mask); - - idx.vec[0] = _mm_or_si128(idx.vec[0], _mm_cvtepi16_epi32(idx_l_0)); - idx.vec[1] = _mm_or_si128(idx.vec[1], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_0, 8))); - idx.vec[2] = _mm_or_si128(idx.vec[2], _mm_cvtepi16_epi32(idx_l_1)); - idx.vec[3] = _mm_or_si128(idx.vec[3], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_1, 8))); - - const __m128i q2_1_0 = _mm_set_epi32(iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]); - const __m128i q2_1_1 = _mm_set_epi32(iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]]); - const __m128i q2_2_0 = _mm_set_epi32(iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[9]], iq3s_grid[idx.index[8]]); - const __m128i q2_2_1 = _mm_set_epi32(iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]]); - - __m128i aux128_0 = _mm_set1_epi32(signs[0] | (signs[1] << 16)); - __m128i aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); - const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); - - aux128_0 = _mm_set1_epi32(signs[2] | (signs[3] << 16)); - aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); - const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); - - signs += 4; - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector unsigned char mask0 = vec_xl( 0, k_mask1); - const vector unsigned char mask1 = vec_xl(16, k_mask1); - const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], - iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; - vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], - iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; - vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], - iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; - vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], - iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; - q3 += 16; - qh += 2; - - vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); - vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); - signs += 4; - - vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); - vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); - vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); - vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); - - vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); - vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); - vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); - vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); - - vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); - vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); - vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); - vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - sc ++; - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); - const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); - - __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); - const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); - - typedef union { - __m256i vec[2]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; - idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); - idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); - idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); - idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); - idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); - idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); - - // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. - //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); - //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); - const __m256i q2_1 = lasx_set_w( - iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] - ); - const __m256i q2_2 = lasx_set_w( - iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], - iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] - ); - - __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); - - aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = hsum_float_8(accumf); - -#else - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint8_t * restrict signs = x[i].signs; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1; - const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - qs += 8; - signs += 4; - bsum += sumi * ls1; - sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - qs += 8; - signs += 4; - bsum += sumi * ls2; - } - sumf += d * bsum; - } - *s = sumf; -#endif -} - -#if defined(__AVX2__) -static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { - const __m256i ax = _mm256_sign_epi8(x, x); - const __m256i sy = _mm256_sign_epi8(y, x); - return _mm256_maddubs_epi16(ax, sy); -} -#elif defined(__loongarch_asx) -static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { - const __m256i ax = __lasx_xvsigncov_b(x, x); - const __m256i sy = __lasx_xvsigncov_b(x, y); - __m256i tmp1, tmp2, tmp3; - tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); - tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); - tmp3 = __lasx_xvadd_h(tmp1, tmp2); - return __lasx_xvsat_h(tmp3, 15); -} -#endif - -void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq1_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined __ARM_NEON - - ggml_int8x16x4_t q1b; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - int sumi1 = 0, sumi2 = 0, sumi3 = 0; - - for (int ib = 0; ib < QK_K/32; ib += 2) { - - q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700))))); - q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700))))); - q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700))))); - q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700))))); - qs += 8; - - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); - - const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - sumi1 += vaddvq_s32(p1) * ls1; - sumi2 += vaddvq_s32(p2) * ls2; - sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1) - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1); - - } - - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3); - } - - *s = sumf; - -#elif defined __AVX2__ - - __m256 accum = _mm256_setzero_ps(); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m256i sumi = _mm256_setzero_si256(); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], - iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); - const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], - iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); - qs += 8; - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2)); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum); - accum1 += d * sumi1; - - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#elif defined __AVX__ - __m256 accum = _mm256_setzero_ps(); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q1b_1_0 = _mm_set_epi64x(iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); - const __m128i q1b_1_1 = _mm_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)]); - const __m128i q1b_2_0 = _mm_set_epi64x(iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); - const __m128i q1b_2_1 = _mm_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)]); - qs += 8; - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); - const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); - const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); - const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(ls1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(ls1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(ls2)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(ls2)); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum); - accum1 += d * sumi1; - - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#elif defined(__POWER9_VECTOR__) - const vector unsigned char v0 = vec_splats((unsigned char)0x0); - const vector unsigned short vsign = vec_splats((unsigned short)0x8000); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi8 = vec_splats((int32_t)0); - - const uint8_t * restrict q1 = x[i].qs; - const uint16_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - const int16_t * restrict qs = y[i].bsums; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q1, 0, 1); - __builtin_prefetch(qh, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; - q1 += 8; - - vector signed char q1x0 = (vector signed char)aux64x2_0; - vector signed char q1x1 = (vector signed char)aux64x2_1; - vector signed char q1x2 = (vector signed char)aux64x2_2; - vector signed char q1x3 = (vector signed char)aux64x2_3; - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); - - const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); - const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - vector signed short vscales = vec_sld(vscales23, vscales01, 8); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - - vector signed short q8ysums = vec_xl_len(qs, 8); - qs += 4; - q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); - - vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); - qh += 2; - vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); - - vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); - - vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - - vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - __m256 accum = (__m256)__lasx_xvldi(0); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m256i sumi = __lasx_xvldi(0); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); - - __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); - - qs += 8; - const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - - __m256i tmp1, tmp5, tmp6; - tmp1 = __lasx_xvreplgr2vr_h(ls1); - tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); - tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); - const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); - - tmp1 = __lasx_xvreplgr2vr_h(ls2); - tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); - tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); - const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); - accum1 += d * sumi1; - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#else - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - int sumi = 0, sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ++ib) { - const int ls = 2*((qh[ib] >> 12) & 7) + 1; - const int delta = qh[ib] & 0x8000 ? -1 : 1; - int lsum = 0; - for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); - for (int j = 0; j < 8; ++j) { - lsum += q8[j] * grid[j]; - } - q8 += 8; - } - sumi += ls * lsum; - sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]); - qs += 4; - } - - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1); - } - - *s = sumf; - -#endif -} - -void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq1_m * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - iq1m_scale_t scale; - -#if defined __ARM_NEON - const int32x4_t mask = vdupq_n_s32(0x7); - const int32x4_t mone = vdupq_n_s32(1); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t deltas; - deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1)); - deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1)); - deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1)); - deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1)); - - ggml_int8x16x4_t q1b; - ggml_int8x16x4_t q8b; - - uint32_t aux32; - const uint8_t * aux8 = (const uint8_t *)&aux32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - int32x4_t sumi1 = mzero; - int32x4_t sumi2 = mzero; - - for (int ib = 0; ib < QK_K/32; ib += 2) { - - q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700))))); - q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700))))); - q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700))))); - q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700))))); - - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1])); - const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3])); - const int32x4_t p12 = vpaddq_s32(p1, p2); - - const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that - aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202); - - const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1])); - const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); - const int32x4_t p34 = vpaddq_s32(p3, p4); - - int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); - - scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); - - sumi1 = vmlaq_s32(sumi1, scales_4, p12); - sumi2 = vmlaq_s32(sumi2, scales_4, p34); - - qs += 8; qh += 4; - - } - - sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i mask = _mm256_set1_epi16(0x7); - const __m256i mone = _mm256_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m256i q1b_1 = _mm256_set_epi64x( - iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)], - iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)] - ); - const __m256i q1b_2 = _mm256_set_epi64x( - iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)], - iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)] - ); - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - - const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - - const __m256i dot3 = mul_add_epi8(delta1, q8b_1); - const __m256i dot4 = mul_add_epi8(delta2, q8b_2); - - __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); - __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); - - scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); - scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); - const __m256i p1 = _mm256_madd_epi16(dot1, scale1); - const __m256i p2 = _mm256_madd_epi16(dot2, scale2); - const __m256i p3 = _mm256_madd_epi16(dot3, scale1); - const __m256i p4 = _mm256_madd_epi16(dot4, scale2); - - sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4)); - - qs += 8; qh += 4; - } - - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); - - accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); - accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); - } - - *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); - -#elif defined __AVX__ - const __m128i mask = _mm_set1_epi16(0x7); - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q1b_1_0 = _mm_set_epi64x( - iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]); - const __m128i q1b_1_1 = _mm_set_epi64x( - iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)]); - const __m128i q1b_2_0 = _mm_set_epi64x( - iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]); - const __m128i q1b_2_1 = _mm_set_epi64x( - iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)]); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); - const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); - const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); - const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); - - const __m128i delta1_0 = _mm_set_epi64x(qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta1_1 = _mm_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta2_0 = _mm_set_epi64x(qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta2_1 = _mm_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - - const __m128i dot3_0 = mul_add_epi8_sse(delta1_0, q8b_1_0); - const __m128i dot3_1 = mul_add_epi8_sse(delta1_1, q8b_1_1); - const __m128i dot4_0 = mul_add_epi8_sse(delta2_0, q8b_2_0); - const __m128i dot4_1 = mul_add_epi8_sse(delta2_1, q8b_2_1); - - __m128i scale1_0 = _mm_set1_epi16(sc[ib/2] >> 0); - __m128i scale1_1 = _mm_set1_epi16(sc[ib/2] >> 3); - __m128i scale2_0 = _mm_set1_epi16(sc[ib/2] >> 6); - __m128i scale2_1 = _mm_set1_epi16(sc[ib/2] >> 9); - - scale1_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_0, mask), 1), mone); - scale1_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_1, mask), 1), mone); - scale2_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_0, mask), 1), mone); - scale2_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_1, mask), 1), mone); - const __m128i p1_0 = _mm_madd_epi16(dot1_0, scale1_0); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, scale1_1); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, scale2_0); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, scale2_1); - const __m128i p3_0 = _mm_madd_epi16(dot3_0, scale1_0); - const __m128i p3_1 = _mm_madd_epi16(dot3_1, scale1_1); - const __m128i p4_0 = _mm_madd_epi16(dot4_0, scale2_0); - const __m128i p4_1 = _mm_madd_epi16(dot4_1, scale2_1); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_add_epi32(p3_0, p4_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_add_epi32(p3_1, p4_1)); - - qs += 8; qh += 4; - } - - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); - - accum1 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi2_1, sumi2_0))), accum2); - } - - *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); - -#else - - int sum1[2], sum2[2], delta[4]; - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - int sumi1 = 0, sumi2 = 0; - for (int ib = 0; ib < QK_K/32; ++ib) { - delta[0] = qh[0] & 0x08 ? -1 : 1; - delta[1] = qh[0] & 0x80 ? -1 : 1; - delta[2] = qh[1] & 0x08 ? -1 : 1; - delta[3] = qh[1] & 0x80 ? -1 : 1; - sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0; - for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700))); - int lsum1 = 0, lsum2 = 0; - for (int j = 0; j < 8; ++j) { - lsum1 += q8[j] * grid[j]; - lsum2 += q8[j]; - } - q8 += 8; - sum1[l/2] += lsum1; - sum2[l/2] += lsum2*delta[l]; - } - - const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; - const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; - - sumi1 += sum1[0] * ls1 + sum1[1] * ls2; - sumi2 += sum2[0] * ls1 + sum2[1] * ls2; - qs += 4; - qh += 2; - } - - sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); - } - - *s = sumf; - -#endif -} - -void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - assert(n % QK4_NL == 0); - static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); - - const block_iq4_nl * restrict x = vx; - const block_q8_0 * restrict y = vy; - - const int nb = n / QK4_NL; - - int ib = 0; - float sumf = 0; - -#if defined __ARM_NEON - const int8x16_t values = vld1q_s8(kvalues_iq4nl); - const uint8x16_t m4b = vdupq_n_u8(0x0f); - uint8x16x2_t q4bits; - int8x16x4_t q4b; - int8x16x4_t q8b; - int32x4_t prod_1, prod_2; - - for (; ib + 1 < nb; ib += 2) { - - q4bits.val[0] = vld1q_u8(x[ib + 0].qs); - q4bits.val[1] = vld1q_u8(x[ib + 1].qs); - q8b.val[0] = vld1q_s8(y[ib + 0].qs); - q8b.val[1] = vld1q_s8(y[ib + 0].qs + 16); - q8b.val[2] = vld1q_s8(y[ib + 1].qs); - q8b.val[3] = vld1q_s8(y[ib + 1].qs + 16); - - q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); - q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); - q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); - q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); - - prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); - prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); - - sumf += - GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) + - GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2); - } - -#elif defined __AVX2__ - - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - const __m256i mone = _mm256_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs); - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs); - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs); - const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); - const __m256i p_2 = _mm256_madd_epi16(p16_2, mone); - accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(p_1), accum1); - accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(p_2), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); - -#elif defined __AVX__ - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); - - const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); - const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); - const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); - const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); - accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - - const vector signed char values = vec_xl( 0, kvalues_iq4nl); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q4x0 = vec_and(qxs, lowMask); - vector signed char q4x1 = vec_sr(qxs, v4); - - q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); - q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi1 = vec_sum4s(qv1, vsumi1); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - } - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined (__loongarch_asx) - - const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); - const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); - const __m256i mone = __lasx_xvreplgr2vr_h(1); - - __m256 accum1 = (__m256)__lasx_xvldi(0); - __m256 accum2 = (__m256)__lasx_xvldi(0); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0); - const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0); - const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0); - const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0); - const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), - lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); - const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), - lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const __m256i p_1 = lasx_madd_h(p16_1, mone); - const __m256i p_2 = lasx_madd_h(p16_2, mone); - accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - __lasx_xvffint_s_w(p_1), accum1); - accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - __lasx_xvffint_s_w(p_2), accum2); - } - - sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); - -#endif - for (; ib < nb; ++ib) { - const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d); - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < QK4_NL/2; ++j) { - sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf]; - sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >> 4]; - } - sumf += d * (sumi1 + sumi2); - } - *s = sumf; -} - -void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - assert(n % QK_K == 0); - - const block_iq4_xs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined __ARM_NEON - const int8x16_t values = vld1q_s8(kvalues_iq4nl); - const uint8x16_t m4b = vdupq_n_u8(0x0f); - ggml_uint8x16x2_t q4bits; - ggml_int8x16x4_t q4b; - ggml_int8x16x4_t q8b; - int32x4_t prod_1, prod_2; - - float sumf = 0; - - for (int ibl = 0; ibl < nb; ++ibl) { - - const int8_t * q8 = y[ibl].qs; - const uint8_t * q4 = x[ibl].qs; - uint16_t h = x[ibl].scales_h; - - int sumi1 = 0, sumi2 = 0; - for (int ib = 0; ib < QK_K/64; ++ib) { - - q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); - q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); - q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); - q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); - - prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); - prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); - - int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; - int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; - h >>= 4; - sumi1 += vaddvq_s32(prod_1) * ls1; - sumi2 += vaddvq_s32(prod_2) * ls2; - - } - - sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - - __m256 accum = _mm256_setzero_ps(); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); - const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); - sumi1 = _mm256_add_epi32(p_1, sumi1); - sumi2 = _mm256_add_epi32(p_2, sumi2); - } - accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); - } - - *s = hsum_float_8(accum); - -#elif defined __AVX__ - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - - __m256 accum = _mm256_setzero_ps(); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)qs); qs += 16; - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)qs); qs += 16; - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); - const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); - const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); - const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, _mm_set1_epi16(ls1)); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, _mm_set1_epi16(ls1)); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, _mm_set1_epi16(ls2)); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, _mm_set1_epi16(ls2)); - sumi1_0 = _mm_add_epi32(p_1_0, sumi1_0); - sumi1_1 = _mm_add_epi32(p_1_1, sumi1_1); - sumi2_0 = _mm_add_epi32(p_2_0, sumi2_0); - sumi2_1 = _mm_add_epi32(p_2_1, sumi2_1); - } - __m128i sumi12_0 = _mm_add_epi32(sumi1_0, sumi2_0); - __m128i sumi12_1 = _mm_add_epi32(sumi1_1, sumi2_1); - accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - _mm256_cvtepi32_ps(MM256_SET_M128I(sumi12_1, sumi12_0))), accum); - } - - *s = hsum_float_8(accum); - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector signed char values = vec_xl( 0, kvalues_iq4nl); - - for (int ibl = 0; ibl < nb; ++ibl) { - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); - vector float vyd = vec_splats(y[ibl].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - uint16_t h = x[ibl].scales_h; - - const uint8_t * restrict q4 = x[ibl].qs; - const uint8_t * restrict sc = x[ibl].scales_l; - const int8_t * restrict q8 = y[ibl].qs; - - for (int ib = 0; ib < QK_K/64; ib ++ ) { - __builtin_prefetch(q4, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); - vector signed char qxs1 = (vector signed char)vec_xl(16, q4); - q4 += 32; - - vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); - vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); - vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); - vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); - - q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); - q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); - q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); - q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); - - const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); - const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); - h >>= 4; - sc ++; - - vector signed short vscales01 = vec_splats((int16_t)ls0); - vector signed short vscales23 = vec_splats((int16_t)ls1); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); - const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); - - __m256 accum = (__m256)__lasx_xvldi(0); - __m256i tmp1; - __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; - - mask_8f = __lsx_vreplgr2vr_b(0x8f); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - __m128i zero = __lsx_vldi(0); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; - const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; - const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp3 = __lsx_vand_v(tmp0, mask); - tmp3 = __lsx_vshuf_b(values128, zero, tmp3); - - tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp4 = __lsx_vand_v(tmp0, mask); - tmp4 = __lsx_vshuf_b(values128, zero, tmp4); - - const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); - - tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp3 = __lsx_vand_v(tmp0, mask); - tmp3 = __lsx_vshuf_b(values128, zero, tmp3); - - tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp4 = __lsx_vand_v(tmp0, mask); - tmp4 = __lsx_vshuf_b(values128, zero, tmp4); - - const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); - - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - __m256i tmp5, tmp6; - tmp1 = __lasx_xvreplgr2vr_h(ls1); - tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); - tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); - const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); - tmp1 = __lasx_xvreplgr2vr_h(ls2); - tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); - tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); - const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); - sumi1 = __lasx_xvadd_w(p_1, sumi1); - sumi2 = __lasx_xvadd_w(p_2, sumi2); - } - accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); - } - - *s = hsum_float_8(accum); - -#else - float sumf = 0; - for (int ibl = 0; ibl < nb; ++ibl) { - const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; - uint16_t h = x[ibl].scales_h; - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); - const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); - h >>= 4; - const float d1 = d4d8*(ls1 - 32); - const float d2 = d4d8*(ls2 - 32); - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 16; ++j) { - sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; - sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; - } - sumf += d1 * (sumi1 + sumi2); - qs += 16; - q8 += 32; - sumi1 = sumi2 = 0; - for (int j = 0; j < 16; ++j) { - sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; - sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; - } - sumf += d2 * (sumi1 + sumi2); - qs += 16; - q8 += 32; - } - } - *s = sumf; -#endif -} - // ================================ IQ2 quantization ============================================= typedef struct { @@ -14249,12 +3770,6 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq3_xxs); } -void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq3_xxs * restrict y = vy; - quantize_row_iq3_xxs_ref(x, y, k); -} - void quantize_row_iq3_xxs_ref(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_row_iq3_xxs_impl(256, x, y, k, NULL); @@ -14465,12 +3980,6 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t n return nrow * nblock * sizeof(block_iq3_s); } -void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq3_s * restrict y = vy; - quantize_row_iq3_s_ref(x, y, k); -} - void quantize_row_iq3_s_ref(const float * restrict x, block_iq3_s * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq3_s(x, y, 1, k, NULL); @@ -15194,7 +4703,8 @@ size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq4_nl); } -void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k) { +//void quantize_row_iq4_nl_ref(const float * restrict x, void * restrict vy, int64_t k) { +void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) { GGML_ASSERT(k%QK4_NL == 0); int64_t nblock = k/QK4_NL; uint8_t L[QK4_NL]; @@ -15202,18 +4712,13 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k uint16_t unused_h; uint8_t * unused_l = NULL; float scale; - block_iq4_nl * iq4 = (block_iq4_nl *)vy; + block_iq4_nl * iq4 = y; for (int ibl = 0; ibl < nblock; ++ibl) { quantize_row_iq4_nl_impl(QK4_NL, 32, x + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, &scale, weight, L, kvalues_iq4nl, NULL, -1); } } -void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) { - assert(k % QK4_NL == 0); - quantize_row_iq4_nl(x, y, k); -} - size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int64_t nblock = n_per_row/QK_K; @@ -15234,12 +4739,6 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq4_xs); } -void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq4_xs * restrict y = vy; - quantize_row_iq4_xs_ref(x, y, k); -} - void quantize_row_iq4_xs_ref(const float * restrict x, block_iq4_xs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq4_xs(x, y, 1, k, NULL); @@ -15432,11 +4931,7 @@ void quantize_row_iq2_s_ref(const float * restrict x, block_iq2_s * restrict y, quantize_iq2_s(x, y, 1, k, NULL); } -void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq2_s * restrict y = vy; - quantize_row_iq2_s_ref(x, y, k); -} +// =============================== data validation static bool validate_float(float f, size_t i) { if (isinf(f)) { diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h index df9c4b24ae..d09173e111 100644 --- a/ggml/src/ggml-quants.h +++ b/ggml/src/ggml-quants.h @@ -11,136 +11,89 @@ extern "C" { #endif +// NOTE: these functions are defined as GGML_API because they used by the CPU backend + // Quantization -void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k); -void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_tq2_0_ref(const float * GGML_RESTRICT x, block_tq2_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_tq2_0_ref(const float * GGML_RESTRICT x, block_tq2_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_s_ref (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k); -void quantize_row_iq2_s_ref (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k); - -void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_tq1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq3_s_ref (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq2_s_ref (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +//GGML_API void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_tq1_0(const block_tq1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_tq2_0(const block_tq2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_tq1_0(const block_tq1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_tq2_0(const block_tq2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); - -// Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +GGML_API void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") -size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_tq1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_tq2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_tq1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_tq2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -void iq2xs_init_impl(enum ggml_type type); -void iq2xs_free_impl(enum ggml_type type); -void iq3xs_init_impl(int grid_size); -void iq3xs_free_impl(int grid_size); +GGML_API void iq2xs_init_impl(enum ggml_type type); +GGML_API void iq2xs_free_impl(enum ggml_type type); +GGML_API void iq3xs_init_impl(int grid_size); +GGML_API void iq3xs_free_impl(int grid_size); #ifdef __cplusplus } diff --git a/ggml/src/ggml-rpc/CMakeLists.txt b/ggml/src/ggml-rpc/CMakeLists.txt new file mode 100644 index 0000000000..a2d6770eb0 --- /dev/null +++ b/ggml/src/ggml-rpc/CMakeLists.txt @@ -0,0 +1,11 @@ +message(STATUS "Using RPC backend") + +add_library(ggml-rpc + ggml-rpc.cpp) + +target_link_libraries(ggml-rpc PRIVATE ggml-base) +target_include_directories(ggml-rpc PRIVATE . ..) + +if (WIN32) + target_link_libraries(ggml-rpc PRIVATE ws2_32) +endif() diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp similarity index 74% rename from ggml/src/ggml-rpc.cpp rename to ggml/src/ggml-rpc/ggml-rpc.cpp index 13c7dd4364..47357daabd 100644 --- a/ggml/src/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -57,8 +57,9 @@ struct socket_t { } }; -// ggml_tensor is serialized into rpc_tensor +// all RPC structures must be packed #pragma pack(push, 1) +// ggml_tensor is serialized into rpc_tensor struct rpc_tensor { uint64_t id; uint32_t type; @@ -76,7 +77,6 @@ struct rpc_tensor { char padding[4]; }; -#pragma pack(pop) static_assert(sizeof(rpc_tensor) % 8 == 0, "rpc_tensor size must be multiple of 8"); @@ -96,6 +96,65 @@ enum rpc_cmd { RPC_CMD_COUNT, }; +struct rpc_msg_alloc_buffer_req { + uint64_t size; +}; + +struct rpc_msg_alloc_buffer_rsp { + uint64_t remote_ptr; + uint64_t remote_size; +}; + +struct rpc_msg_get_alignment_rsp { + uint64_t alignment; +}; + +struct rpc_msg_get_max_size_rsp { + uint64_t max_size; +}; + +struct rpc_msg_buffer_get_base_req { + uint64_t remote_ptr; +}; + +struct rpc_msg_buffer_get_base_rsp { + uint64_t base_ptr; +}; + +struct rpc_msg_free_buffer_req { + uint64_t remote_ptr; +}; + +struct rpc_msg_buffer_clear_req { + uint64_t remote_ptr; + uint8_t value; +}; + +struct rpc_msg_get_tensor_req { + rpc_tensor tensor; + uint64_t offset; + uint64_t size; +}; + +struct rpc_msg_copy_tensor_req { + rpc_tensor src; + rpc_tensor dst; +}; + +struct rpc_msg_copy_tensor_rsp { + uint8_t result; +}; + +struct rpc_msg_graph_compute_rsp { + uint8_t result; +}; + +struct rpc_msg_get_device_memory_rsp { + uint64_t free_mem; + uint64_t total_mem; +}; +#pragma pack(pop) + // RPC data structures static ggml_guid_t ggml_backend_rpc_guid() { @@ -119,7 +178,6 @@ struct ggml_backend_rpc_buffer_context { std::shared_ptr sock; std::unordered_map base_cache; uint64_t remote_ptr; - std::string name; }; // RPC helper functions @@ -240,6 +298,38 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) { return true; } +static bool send_msg(sockfd_t sockfd, const void * msg, size_t msg_size) { + if (!send_data(sockfd, &msg_size, sizeof(msg_size))) { + return false; + } + return send_data(sockfd, msg, msg_size); +} + +static bool recv_msg(sockfd_t sockfd, void * msg, size_t msg_size) { + uint64_t size; + if (!recv_data(sockfd, &size, sizeof(size))) { + return false; + } + if (size != msg_size) { + return false; + } + return recv_data(sockfd, msg, msg_size); +} + +static bool recv_msg(sockfd_t sockfd, std::vector & input) { + uint64_t size; + if (!recv_data(sockfd, &size, sizeof(size))) { + return false; + } + try { + input.resize(size); + } catch (const std::bad_alloc & e) { + fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", size); + return false; + } + return recv_data(sockfd, input.data(), size); +} + static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { size_t pos = endpoint.find(':'); if (pos == std::string::npos) { @@ -252,28 +342,27 @@ static bool parse_endpoint(const std::string & endpoint, std::string & host, int // RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | // RPC response: | response_size (8 bytes) | response_data (response_size bytes) | -static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const std::vector & input, std::vector & output) { +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size, void * output, size_t output_size) { uint8_t cmd_byte = cmd; if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { return false; } - uint64_t input_size = input.size(); if (!send_data(sock->fd, &input_size, sizeof(input_size))) { return false; } - if (!send_data(sock->fd, input.data(), input.size())) { + if (!send_data(sock->fd, input, input_size)) { return false; } - uint64_t output_size; - if (!recv_data(sock->fd, &output_size, sizeof(output_size))) { + // TODO: currently the output_size is always known, do we need support for commands with variable output size? + // even if we do, we can skip sending output_size from the server for commands with known output size + uint64_t out_size; + if (!recv_data(sock->fd, &out_size, sizeof(out_size))) { return false; } - if (output_size == 0) { - output.clear(); - return true; + if (out_size != output_size) { + return false; } - output.resize(output_size); - if (!recv_data(sock->fd, output.data(), output_size)) { + if (!recv_data(sock->fd, output, output_size)) { return false; } return true; @@ -319,21 +408,11 @@ static std::shared_ptr get_socket(const std::string & endpoint) { return sock; } -static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | remote_ptr (8 bytes) | - std::vector input(sizeof(uint64_t), 0); - uint64_t remote_ptr = ctx->remote_ptr; - memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, input, output); + rpc_msg_free_buffer_req request = {ctx->remote_ptr}; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, &request, sizeof(request), nullptr, 0); GGML_ASSERT(status); - GGML_ASSERT(output.empty()); delete ctx; } @@ -342,20 +421,13 @@ static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) { return ctx->base_cache[buffer]; } - // input serialization format: | remote_ptr (8 bytes) | - std::vector input(sizeof(uint64_t), 0); - uint64_t remote_ptr = ctx->remote_ptr; - memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, input, output); + rpc_msg_buffer_get_base_req request = {ctx->remote_ptr}; + rpc_msg_buffer_get_base_rsp response; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | base_ptr (8 bytes) | - uint64_t base_ptr; - memcpy(&base_ptr, output.data(), sizeof(base_ptr)); - void * base = reinterpret_cast(base_ptr); - ctx->base_cache[buffer] = base; - return base; + void * base_ptr = reinterpret_cast(response.base_ptr); + ctx->base_cache[buffer] = base_ptr; + return base_ptr; } static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { @@ -405,26 +477,18 @@ static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggm memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input, output); + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input.data(), input.size(), nullptr, 0); GGML_ASSERT(status); } static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | - int input_size = sizeof(rpc_tensor) + 2*sizeof(uint64_t); - std::vector input(input_size, 0); - rpc_tensor rpc_tensor = serialize_tensor(tensor); - memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); - memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); - memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), &size, sizeof(size)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_GET_TENSOR, input, output); + rpc_msg_get_tensor_req request; + request.tensor = serialize_tensor(tensor); + request.offset = offset; + request.size = size; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_GET_TENSOR, &request, sizeof(request), data, size); GGML_ASSERT(status); - GGML_ASSERT(output.size() == size); - // output serialization format: | data (size bytes) | - memcpy(data, output.data(), size); } static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { @@ -437,35 +501,23 @@ static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con return false; } ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | rpc_tensor src | rpc_tensor dst | - int input_size = 2*sizeof(rpc_tensor); - std::vector input(input_size, 0); - rpc_tensor rpc_src = serialize_tensor(src); - rpc_tensor rpc_dst = serialize_tensor(dst); - memcpy(input.data(), &rpc_src, sizeof(rpc_src)); - memcpy(input.data() + sizeof(rpc_src), &rpc_dst, sizeof(rpc_dst)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_COPY_TENSOR, input, output); + rpc_msg_copy_tensor_req request; + request.src = serialize_tensor(src); + request.dst = serialize_tensor(dst); + rpc_msg_copy_tensor_rsp response; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_COPY_TENSOR, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - // output serialization format: | result (1 byte) | - GGML_ASSERT(output.size() == 1); - return output[0]; + return response.result; } static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // serialization format: | bufptr (8 bytes) | value (1 byte) | - int input_size = sizeof(uint64_t) + sizeof(uint8_t); - std::vector input(input_size, 0); - memcpy(input.data(), &ctx->remote_ptr, sizeof(ctx->remote_ptr)); - memcpy(input.data() + sizeof(ctx->remote_ptr), &value, sizeof(value)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_CLEAR, input, output); + rpc_msg_buffer_clear_req request = {ctx->remote_ptr, value}; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_CLEAR, &request, sizeof(request), nullptr, 0); GGML_ASSERT(status); } static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { - /* .get_name = */ ggml_backend_rpc_buffer_get_name, /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, /* .get_base = */ ggml_backend_rpc_buffer_get_base, /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, @@ -484,25 +536,16 @@ static const char * ggml_backend_rpc_buffer_type_name(ggml_backend_buffer_type_t static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - // input serialization format: | size (8 bytes) | - int input_size = sizeof(uint64_t); - std::vector input(input_size, 0); - memcpy(input.data(), &size, sizeof(size)); - std::vector output; + rpc_msg_alloc_buffer_req request = {size}; + rpc_msg_alloc_buffer_rsp response; auto sock = get_socket(buft_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_ALLOC_BUFFER, input, output); + bool status = send_rpc_cmd(sock, RPC_CMD_ALLOC_BUFFER, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); - // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | - uint64_t remote_ptr; - memcpy(&remote_ptr, output.data(), sizeof(remote_ptr)); - size_t remote_size; - memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size)); - if (remote_ptr != 0) { + if (response.remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, - remote_size); + new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr}, + response.remote_size); return buffer; } else { return nullptr; @@ -510,16 +553,10 @@ static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_back } static size_t get_alignment(const std::shared_ptr & sock) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALIGNMENT, input, output); + rpc_msg_get_alignment_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALIGNMENT, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | alignment (8 bytes) | - uint64_t alignment; - memcpy(&alignment, output.data(), sizeof(alignment)); - return alignment; + return response.alignment; } static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -528,16 +565,10 @@ static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_typ } static size_t get_max_size(const std::shared_ptr & sock) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_MAX_SIZE, input, output); + rpc_msg_get_max_size_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_MAX_SIZE, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | max_size (8 bytes) | - uint64_t max_size; - memcpy(&max_size, output.data(), sizeof(max_size)); - return max_size; + return response.max_size; } static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { @@ -571,11 +602,6 @@ static void ggml_backend_rpc_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); -} - static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { UNUSED(backend); // this is no-op because we don't have any async operations @@ -622,18 +648,16 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; std::vector input; serialize_graph(cgraph, input); - std::vector output; + rpc_msg_graph_compute_rsp response; auto sock = get_socket(rpc_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input, output); + bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input.data(), input.size(), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 1); - return (enum ggml_status)output[0]; + return (enum ggml_status)response.result; } static ggml_backend_i ggml_backend_rpc_interface = { /* .get_name = */ ggml_backend_rpc_name, /* .free = */ ggml_backend_rpc_free, - /* .get_default_buffer_type = */ ggml_backend_rpc_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -643,14 +667,11 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_rpc_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; -GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { +ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { static std::mutex mutex; std::lock_guard lock(mutex); // NOTE: buffer types are allocated and never freed; this is by design @@ -697,27 +718,19 @@ ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { return backend; } -GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend) { +bool ggml_backend_is_rpc(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_rpc_guid()); } static void get_device_memory(const std::shared_ptr & sock, size_t * free, size_t * total) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_DEVICE_MEMORY, input, output); + rpc_msg_get_device_memory_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_DEVICE_MEMORY, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); - // output serialization format: | free (8 bytes) | total (8 bytes) | - uint64_t free_mem; - memcpy(&free_mem, output.data(), sizeof(free_mem)); - uint64_t total_mem; - memcpy(&total_mem, output.data() + sizeof(uint64_t), sizeof(total_mem)); - *free = free_mem; - *total = total_mem; + *free = response.free_mem; + *total = response.total_mem; } -GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { +void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { auto sock = get_socket(endpoint); if (sock == nullptr) { *free = 0; @@ -734,16 +747,16 @@ public: rpc_server(ggml_backend_t backend) : backend(backend) {} ~rpc_server(); - bool alloc_buffer(const std::vector & input, std::vector & output); - void get_alignment(std::vector & output); - void get_max_size(std::vector & output); - bool buffer_get_base(const std::vector & input, std::vector & output); - bool free_buffer(const std::vector & input); - bool buffer_clear(const std::vector & input); + void alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response); + void get_alignment(rpc_msg_get_alignment_rsp & response); + void get_max_size(rpc_msg_get_max_size_rsp & response); + bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response); + bool free_buffer(const rpc_msg_free_buffer_req & request); + bool buffer_clear(const rpc_msg_buffer_clear_req & request); bool set_tensor(const std::vector & input); - bool get_tensor(const std::vector & input, std::vector & output); - bool copy_tensor(const std::vector & input, std::vector & output); - bool graph_compute(const std::vector & input, std::vector & output); + bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response); + bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response); + bool graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response); private: ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor); @@ -757,80 +770,50 @@ private: std::unordered_set buffers; }; -bool rpc_server::alloc_buffer(const std::vector & input, std::vector & output) { - // input serialization format: | size (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t size; - memcpy(&size, input.data(), sizeof(size)); +void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); - uint64_t remote_ptr = 0; - uint64_t remote_size = 0; + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, request.size); + response.remote_ptr = 0; + response.remote_size = 0; if (buffer != nullptr) { - remote_ptr = reinterpret_cast(buffer); - remote_size = buffer->size; - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + response.remote_ptr = reinterpret_cast(buffer); + response.remote_size = buffer->size; + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size); buffers.insert(buffer); } else { - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, size); + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size); } - // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | - output.resize(2*sizeof(uint64_t), 0); - memcpy(output.data(), &remote_ptr, sizeof(remote_ptr)); - memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size)); - return true; } -void rpc_server::get_alignment(std::vector & output) { +void rpc_server::get_alignment(rpc_msg_get_alignment_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t alignment = ggml_backend_buft_get_alignment(buft); GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); - // output serialization format: | alignment (8 bytes) | - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &alignment, sizeof(alignment)); + response.alignment = alignment; } -void rpc_server::get_max_size(std::vector & output) { +void rpc_server::get_max_size(rpc_msg_get_max_size_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t max_size = ggml_backend_buft_get_max_size(buft); GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); - // output serialization format: | max_size (8 bytes) | - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &max_size, sizeof(max_size)); + response.max_size = max_size; } -bool rpc_server::buffer_get_base(const std::vector & input, std::vector & output) { - // input serialization format: | remote_ptr (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; } void * base = ggml_backend_buffer_get_base(buffer); - // output serialization format: | base_ptr (8 bytes) | - uint64_t base_ptr = reinterpret_cast(base); - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &base_ptr, sizeof(base_ptr)); + response.base_ptr = reinterpret_cast(base); return true; } -bool rpc_server::free_buffer(const std::vector & input) { - // input serialization format: | remote_ptr (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; @@ -840,22 +823,14 @@ bool rpc_server::free_buffer(const std::vector & input) { return true; } -bool rpc_server::buffer_clear(const std::vector & input) { - // input serialization format: | remote_ptr (8 bytes) | value (1 byte) | - if (input.size() != sizeof(uint64_t) + sizeof(uint8_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - uint8_t value; - memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; } - ggml_backend_buffer_clear(buffer, value); + ggml_backend_buffer_clear(buffer, request.value); return true; } @@ -930,74 +905,55 @@ bool rpc_server::set_tensor(const std::vector & input) { return true; } -bool rpc_server::get_tensor(const std::vector & input, std::vector & output) { - // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | - if (input.size() != sizeof(rpc_tensor) + 2*sizeof(uint64_t)) { - return false; - } - const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); - uint64_t offset; - memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); - uint64_t size; - memcpy(&size, input.data() + sizeof(rpc_tensor) + sizeof(offset), sizeof(size)); - +bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response) { struct ggml_init_params params { /*.mem_size =*/ ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; struct ggml_context * ctx = ggml_init(params); - ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); if (tensor == nullptr) { GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); ggml_free(ctx); return false; } - GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size); // sanitize tensor->data { const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer); const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); - if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); + if (request.tensor.data + request.offset < p0 || + request.tensor.data + request.offset >= p1 || + request.size > (p1 - request.tensor.data - request.offset)) { + GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); } } - // output serialization format: | data (size bytes) | - output.resize(size, 0); - ggml_backend_tensor_get(tensor, output.data(), offset, size); + response.resize(request.size, 0); + ggml_backend_tensor_get(tensor, response.data(), request.offset, request.size); ggml_free(ctx); return true; } -bool rpc_server::copy_tensor(const std::vector & input, std::vector & output) { - // serialization format: | rpc_tensor src | rpc_tensor dst | - if (input.size() != 2*sizeof(rpc_tensor)) { - return false; - } - const rpc_tensor * rpc_src = (const rpc_tensor *)input.data(); - const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src)); - +bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response) { struct ggml_init_params params { /*.mem_size =*/ 2*ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; struct ggml_context * ctx = ggml_init(params); - ggml_tensor * src = deserialize_tensor(ctx, rpc_src); - ggml_tensor * dst = deserialize_tensor(ctx, rpc_dst); + ggml_tensor * src = deserialize_tensor(ctx, &request.src); + ggml_tensor * dst = deserialize_tensor(ctx, &request.dst); if (src == nullptr || dst == nullptr) { GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__); ggml_free(ctx); return false; } GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); - bool result = ggml_backend_buffer_copy_tensor(src, dst); - // output serialization format: | result (1 byte) | - output.resize(1, 0); - output[0] = result; + response.result = ggml_backend_buffer_copy_tensor(src, dst); ggml_free(ctx); return true; } @@ -1026,7 +982,7 @@ ggml_tensor * rpc_server::create_node(uint64_t id, return result; } -bool rpc_server::graph_compute(const std::vector & input, std::vector & output) { +bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response) { // serialization format: // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | if (input.size() < sizeof(uint32_t)) { @@ -1066,9 +1022,7 @@ bool rpc_server::graph_compute(const std::vector & input, std::vectornodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map); } ggml_status status = ggml_backend_graph_compute(backend, graph); - // output serialization format: | status (1 byte) | - output.resize(1, 0); - output[0] = status; + response.result = status; ggml_free(ctx); return true; } @@ -1091,85 +1045,153 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre fprintf(stderr, "Unknown command: %d\n", cmd); break; } - std::vector input; - std::vector output; - uint64_t input_size; - if (!recv_data(sockfd, &input_size, sizeof(input_size))) { - break; - } - try { - input.resize(input_size); - } catch (const std::bad_alloc & e) { - fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", input_size); - break; - } - if (!recv_data(sockfd, input.data(), input_size)) { - break; - } - bool ok = true; switch (cmd) { case RPC_CMD_ALLOC_BUFFER: { - ok = server.alloc_buffer(input, output); + rpc_msg_alloc_buffer_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_alloc_buffer_rsp response; + server.alloc_buffer(request, response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_ALIGNMENT: { - server.get_alignment(output); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_alignment_rsp response; + server.get_alignment(response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_MAX_SIZE: { - server.get_max_size(output); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_max_size_rsp response; + server.get_max_size(response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_BUFFER_GET_BASE: { - ok = server.buffer_get_base(input, output); + rpc_msg_buffer_get_base_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_buffer_get_base_rsp response; + if (!server.buffer_get_base(request, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_FREE_BUFFER: { - ok = server.free_buffer(input); + rpc_msg_free_buffer_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + if (!server.free_buffer(request)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_BUFFER_CLEAR: { - ok = server.buffer_clear(input); + rpc_msg_buffer_clear_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + if (!server.buffer_clear(request)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_SET_TENSOR: { - ok = server.set_tensor(input); + std::vector input; + if (!recv_msg(sockfd, input)) { + return; + } + if (!server.set_tensor(input)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_GET_TENSOR: { - ok = server.get_tensor(input, output); + rpc_msg_get_tensor_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + std::vector response; + if (!server.get_tensor(request, response)) { + return; + } + if (!send_msg(sockfd, response.data(), response.size())) { + return; + } break; } case RPC_CMD_COPY_TENSOR: { - ok = server.copy_tensor(input, output); + rpc_msg_copy_tensor_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_copy_tensor_rsp response; + if (!server.copy_tensor(request, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GRAPH_COMPUTE: { - ok = server.graph_compute(input, output); + std::vector input; + if (!recv_msg(sockfd, input)) { + return; + } + rpc_msg_graph_compute_rsp response; + if (!server.graph_compute(input, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_DEVICE_MEMORY: { - // output serialization format: | free (8 bytes) | total (8 bytes) | - output.resize(2*sizeof(uint64_t), 0); - memcpy(output.data(), &free_mem, sizeof(free_mem)); - memcpy(output.data() + sizeof(uint64_t), &total_mem, sizeof(total_mem)); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_device_memory_rsp response; + response.free_mem = free_mem; + response.total_mem = total_mem; + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } default: { fprintf(stderr, "Unknown command: %d\n", cmd); - ok = false; + return; } } - if (!ok) { - break; - } - uint64_t output_size = output.size(); - if (!send_data(sockfd, &output_size, sizeof(output_size))) { - break; - } - if (!send_data(sockfd, output.data(), output_size)) { - break; - } } } @@ -1240,7 +1262,7 @@ static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) { // TODO: obtain value from the server - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; UNUSED(dev); } @@ -1274,13 +1296,6 @@ static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_b UNUSED(dev); } -static ggml_backend_buffer_t ggml_backend_rpc_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - UNUSED(dev); - UNUSED(max_tensor_size); -} - static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { UNUSED(dev); UNUSED(op); @@ -1306,7 +1321,7 @@ static const struct ggml_backend_device_i ggml_backend_rpc_device_i = { /* .init_backend = */ ggml_backend_rpc_device_init, /* .get_buffer_type = */ ggml_backend_rpc_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_rpc_device_buffer_from_ptr, + /* .buffer_from_host_ptr = */ NULL, /* .supports_op = */ ggml_backend_rpc_device_supports_op, /* .supports_buft = */ ggml_backend_rpc_device_supports_buft, /* .offload_op = */ NULL, diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt new file mode 100644 index 0000000000..d1d0ff83d6 --- /dev/null +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -0,0 +1,85 @@ +if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA|AMD)$") + message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL, NVIDIA, or AMD") +endif() + +check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL) + +if (DEFINED ENV{ONEAPI_ROOT}) + message(STATUS "Using oneAPI Release SYCL compiler (icpx).") +elseif(SUPPORTS_SYCL) + message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}. + If you expected the oneAPI Release compiler, please install oneAPI & source it, like: + source /opt/intel/oneapi/setvars.sh") +else() + message(FATAL_ERROR, "C++ compiler lacks SYCL support.") +endif() +message(STATUS "SYCL found") +#todo: AOT + +add_library(ggml-sycl + ggml-sycl.cpp + ../../include/ggml-sycl.h) + +target_link_libraries(ggml-sycl PRIVATE ggml-base) +target_include_directories(ggml-sycl PRIVATE . ..) + +if (GGML_SYCL_F16) + if (GGML_SYCL_TARGET STREQUAL "AMD") + message(WARNING "AMD target does not entirely support FP16 in the SYCL backend.") + endif() + add_compile_definitions(GGML_SYCL_F16) +endif() + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl") + +if (GGML_SYCL_TARGET STREQUAL "NVIDIA") + add_compile_definitions(GGML_SYCL_WARP_SIZE=32) +elseif (GGML_SYCL_TARGET STREQUAL "AMD") + # INFO: Allowed Sub_group_sizes are not consistent through all + # hip targets. For example, 64 is used for certain models, but the backend + # does not support it. + # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32) + add_compile_definitions(GGML_SYCL_WARP_SIZE=32) +else() + add_compile_definitions(GGML_SYCL_WARP_SIZE=16) +endif() + +file(GLOB GGML_HEADERS_SYCL "*.hpp") +file(GLOB GGML_SOURCES_SYCL "*.cpp") +target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL}) + +find_package(DNNL) +message("-- DNNL found:" ${DNNL_FOUND}) + +if (GGML_SYCL_TARGET STREQUAL "INTEL") + add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND}) +else() + add_compile_definitions(GGML_SYCL_DNNL=0) +endif() + +if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") + target_link_libraries(ggml-sycl PRIVATE DNNL::dnnl) +endif() + +if (WIN32) + find_package(IntelSYCL REQUIRED) + find_package(MKL REQUIRED) + target_link_libraries(ggml-sycl PRIVATE IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) +else() + if (GGML_SYCL_TARGET STREQUAL "INTEL") + target_link_libraries(ggml-sycl PRIVATE sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") + target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl) + elseif (GGML_SYCL_TARGET STREQUAL "AMD") + if (NOT GGML_SYCL_DEVICE_ARCH) + message(ERROR "Can't enable SYCL hip backend, GGML_SYCL_DEVICE_ARCH has not been set.") + endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=amdgcn-amd-amdhsa") + target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl) + endif() + + if (GGML_SYCL_DEVICE_ARCH) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH}") + endif() +endif() diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index d21b5f8dd2..85748a5b4c 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -26,5 +26,8 @@ #include "softmax.hpp" #include "tsembd.hpp" #include "im2col.hpp" +#include "wkv6.hpp" +#include "outprod.hpp" +#include "element_wise.hpp" #endif // GGML_SYCL_BACKEND_HPP diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp index cf5291b31f..97ab2003c7 100644 --- a/ggml/src/ggml-sycl/common.cpp +++ b/ggml/src/ggml-sycl/common.cpp @@ -62,3 +62,43 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block } return sycl_down_blk_size; } + +void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const ggml_sycl_op_flatten_t op) try { + const int64_t nrows0 = ggml_nrows(src0); + + const bool use_src1 = src1 != nullptr; + const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; + + GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + // dd = data device + float * src0_ddf = (float *) src0->data; + float * src1_ddf = use_src1 ? (float *) src1->data : nullptr; + float * dst_ddf = (float *) dst->data; + + ggml_sycl_pool_alloc src0_f(ctx.pool()); + ggml_sycl_pool_alloc src1_f(ctx.pool()); + ggml_sycl_pool_alloc dst_f(ctx.pool()); + + ggml_sycl_set_device(ctx.device); + queue_ptr main_stream = ctx.stream(); + // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device); + + // do the computation + op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + // print_ggml_tensor("tensor", dst); +} +catch (sycl::exception const &exc) { + + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index bc0faa867d..4549fa5e95 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -404,4 +404,262 @@ static __dpct_inline__ Tp* get_pointer(sycl::local_accessor acc) { int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size); +typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream); + +template +static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) / + ne3; + const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) % + ne3; + + if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + for (int i0 = i0s; i0 < ne0; + i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + } +} + +template +static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + const int i3 = i/(ne2*ne1*ne0); + const int i2 = (i/(ne1*ne0)) % ne2; + const int i1 = (i/ne0) % ne1; + const int i0 = i % ne0; + + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); +} + + +template +struct bin_bcast_sycl { + template + void operator()(ggml_backend_sycl_context & ctx, + const struct ggml_tensor *src0, + const struct ggml_tensor *src1, struct ggml_tensor *dst, + const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, + queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + int nr0 = ne10/ne0; + int nr1 = ne11/ne1; + int nr2 = ne12/ne2; + int nr3 = ne13/ne3; + + int nr[4] = { nr0, nr1, nr2, nr3 }; + + // collapse dimensions until first broadcast dimension + int64_t cne0[] = {ne0, ne1, ne2, ne3}; + int64_t cne1[] = {ne10, ne11, ne12, ne13}; + size_t cnb0[] = {nb0, nb1, nb2, nb3}; + size_t cnb1[] = {nb10, nb11, nb12, nb13}; + auto collapse = [](int64_t cne[]) { + cne[0] *= cne[1]; + cne[1] = cne[2]; + cne[2] = cne[3]; + cne[3] = 1; + }; + + auto collapse_nb = [](size_t cnb[], int64_t cne[]) { + cnb[1] *= cne[1]; + cnb[2] *= cne[2]; + cnb[3] *= cne[3]; + }; + + for (int i = 0; i < 4; i++) { + if (nr[i] != 1) { + break; + } + if (i > 0) { + collapse_nb(cnb0, cne0); + collapse_nb(cnb1, cne1); + collapse(cne0); + collapse(cne1); + } + } + { + int64_t ne0 = cne0[0]; + int64_t ne1 = cne0[1]; + int64_t ne2 = cne0[2]; + int64_t ne3 = cne0[3]; + + int64_t ne10 = cne1[0]; + int64_t ne11 = cne1[1]; + int64_t ne12 = cne1[2]; + int64_t ne13 = cne1[3]; + + size_t nb0 = cnb0[0]; + size_t nb1 = cnb0[1]; + size_t nb2 = cnb0[2]; + size_t nb3 = cnb0[3]; + + size_t nb10 = cnb1[0]; + size_t nb11 = cnb1[1]; + size_t nb12 = cnb1[2]; + size_t nb13 = cnb1[3]; + + size_t s0 = nb0 / sizeof(dst_t); + size_t s1 = nb1 / sizeof(dst_t); + size_t s2 = nb2 / sizeof(dst_t); + size_t s3 = nb3 / sizeof(dst_t); + + size_t s10 = nb10 / sizeof(src1_t); + size_t s11 = nb11 / sizeof(src1_t); + size_t s12 = nb12 / sizeof(src1_t); + size_t s13 = nb13 / sizeof(src1_t); + + GGML_ASSERT(s0 == 1); + GGML_ASSERT(s10 == 1); + + const int block_size = 128; + + int64_t hne0 = std::max(ne0/2LL, 1LL); + + sycl::range<3> block_dims(1, 1, 1); + block_dims[2] = std::min(hne0, block_size); + block_dims[1] = std::min( + ne1, block_size / (unsigned int)block_dims[2]); + block_dims[0] = std::min( + std::min( + ne2 * ne3, block_size / (unsigned int)block_dims[2] / + (unsigned int)block_dims[1]), + 64U); + + sycl::range<3> block_nums( + (ne2 * ne3 + block_dims[0] - 1) / block_dims[0], + (ne1 + block_dims[1] - 1) / block_dims[1], + (hne0 + block_dims[2] - 1) / block_dims[2]); + + if (block_nums[0] > 65535) { + // this is the maximum number of blocks in z direction, fallback to 1D grid kernel + int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * + sycl::range<3>(1, 1, block_size), + sycl::range<3>(1, 1, block_size)), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast_unravel( + src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, + ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12, + s13, item_ct1); + }); + } + } else { + /* + DPCT1049:16: The work-group size passed to the SYCL kernel may + exceed the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if + needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, + ne2, ne3, ne10, ne11, ne12, ne13, + s1, s2, s3, s11, s12, s13, + item_ct1); + }); + } + } + } +}; + +template +inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, + (sycl::half *)dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { + op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { + op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, + main_stream); + } else { + fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, + ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ABORT("fatal error"); + } +} + + +void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const ggml_sycl_op_flatten_t op); + #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp index 632eedb9d4..c90c452d87 100644 --- a/ggml/src/ggml-sycl/concat.cpp +++ b/ggml/src/ggml-sycl/concat.cpp @@ -106,6 +106,7 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); }); break; + // dim >=2 will be dispatched to the default path default: stream->parallel_for( sycl::nd_range<3>(gridDim * diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index fe4a8f744e..c2f28bb495 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -15,6 +15,7 @@ #include #include +#include #include #include @@ -1830,31 +1831,10 @@ namespace dpct : id); } - template - sycl::vec extract_and_sign_or_zero_extend4(T val) - { - return sycl::vec(val) - .template as, int8_t, uint8_t>, 4>>() - .template convert(); - } - - template - using dot_product_acc_t = - std::conditional_t && std::is_unsigned_v, - uint32_t, int32_t>; - template inline auto dp4a(T1 a, T2 b, T3 c) { - dot_product_acc_t res = c; - auto va = extract_and_sign_or_zero_extend4(a); - auto vb = extract_and_sign_or_zero_extend4(b); - res += va[0] * vb[0]; - res += va[1] * vb[1]; - res += va[2] * vb[2]; - res += va[3] * vb[3]; - return res; + return syclcompat::dp4a(a, b, c); } struct sub_sat diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp new file mode 100644 index 0000000000..e5cd736eba --- /dev/null +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -0,0 +1,1011 @@ +#include "common.hpp" +#include "element_wise.hpp" + +void acc_f32(const float * x, const float * y, float * dst, const int ne, + const int ne10, const int ne11, const int ne12, + const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= ne) { + return; + } + int src1_idx = i - offset; + int oz = src1_idx / nb2; + int oy = (src1_idx - (oz * nb2)) / nb1; + int ox = src1_idx % nb1; + if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { + dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; + } else { + dst[i] = x[i]; + } +} + +void gelu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + float xi = x[i]; + dst[i] = 0.5f * xi * + (1.0f + + sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi))); +} + +void silu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i])); +} + +void gelu_quick_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_QUICK_COEF = -1.702f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i]))); +} + +void tanh_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::tanh((float)(x[i])); +} + +void relu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0); +} + +void sigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = 1.0f / (1.0f + sycl::native::exp(-x[i])); +} + +void sqrt_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::sqrt(x[i]); +} + +void sin_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::sin(x[i]); +} + +void cos_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::cos(x[i]); +} + +void hardsigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +void hardswish_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +void exp_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::exp(x[i]); +} + +void log_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + float xi = x[i]; + if (xi <= 0) { + dst[i] = -INFINITY; + } else { + dst[i] = sycl::log(xi); + } +} + +void neg_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = -x[i]; +} + +void step_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] > 0.0f; +} + +void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0) + + sycl::fmin((float)(x[i]), 0.0f) * negative_slope; +} + +void sqr_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + +void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { + int index = item_ct1.get_local_id(0) + + item_ct1.get_group(0) * item_ct1.get_local_range(0); + if (index >= ne10 * ne11 * ne12 * ne13) { + return; + } + // operation + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); +} + +void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, + const sycl::nd_item<3> &item_ct1) { + int nidx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (nidx >= ne0) { + return; + } + + // operation + int offset_dst = nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + if (nidx < ne00 && item_ct1.get_group(1) < ne01 && + item_ct1.get_group(0) < ne02) { + int offset_src = nidx + item_ct1.get_group(1) * ne00 + + item_ct1.get_group(0) * ne00 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + dst[offset_dst] = 0.0f; + } +} + + + +void acc_f32_sycl(const float *x, const float *y, float *dst, + const int n_elements, const int ne10, const int ne11, + const int ne12, const int nb1, const int nb2, + const int offset, queue_ptr stream) { + int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, + item_ct1); + }); +} + +void gelu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_f32(x, dst, k, item_ct1); + }); +} + +void silu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + silu_f32(x, dst, k, item_ct1); + }); +} + +void gelu_quick_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_quick_f32(x, dst, k, item_ct1); + }); +} + +void tanh_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + tanh_f32(x, dst, k, item_ct1); + }); +} + +void relu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + relu_f32(x, dst, k, item_ct1); + }); +} + +void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardsigmoid_f32(x, dst, k, item_ct1); + }); +} + +void hardswish_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardswish_f32(x, dst, k, item_ct1); + }); +} + +void exp_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + exp_f32(x, dst, k, item_ct1); + }); +} + +void log_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + log_f32(x, dst, k, item_ct1); + }); +} + +void neg_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + neg_f32(x, dst, k, item_ct1); + }); +} + +void step_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + step_f32(x, dst, k, item_ct1); + }); +} + +void sigmoid_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIGMOID_BLOCK_SIZE - 1) / SYCL_SIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sigmoid_f32(x, dst, k, item_ct1); + }); +} + +void sqrt_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SQRT_BLOCK_SIZE - 1) / SYCL_SQRT_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sqrt_f32(x, dst, k, item_ct1); + }); +} + +void sin_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sin_f32(x, dst, k, item_ct1); + }); +} + +void cos_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cos_f32(x, dst, k, item_ct1); + }); +} + +void leaky_relu_f32_sycl(const float *x, float *dst, const int k, + const float negative_slope, + queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + leaky_relu_f32(x, dst, k, negative_slope, item_ct1); + }); +} + +void sqr_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sqr_f32(x, dst, k, item_ct1); + }); +} + +void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, queue_ptr stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; + sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); + stream->parallel_for( + sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), + [=](sycl::nd_item<1> item_ct1) { + upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); + }); +} + +void pad_f32_sycl(const float *x, float *dst, const int ne00, + const int ne01, const int ne02, const int ne0, + const int ne1, const int ne2, queue_ptr stream) { + int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; + sycl::range<3> gridDim(ne2, ne1, num_blocks); + stream->parallel_for( + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1); + }); +} + +inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} +inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + exp_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + log_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqrt_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sin_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + cos_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + step_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + neg_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; + + upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, + main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + + pad_f32_sycl(src0_dd, dst_dd, + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported + + int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 + int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 + // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused + int offset = dst->op_params[3] / 4; // offset in bytes + + acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); + + (void) dst; +} + +inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_sub(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + + +void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqrt); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sin); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_cos); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + + +void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_exp); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_log); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_neg); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_step); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + + + +void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sub); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp new file mode 100644 index 0000000000..8152edf583 --- /dev/null +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -0,0 +1,76 @@ +#ifndef GGML_SYCL_ELEMENTWISE_HPP +#define GGML_SYCL_ELEMENTWISE_HPP + +#include "common.hpp" + +static __dpct_inline__ float op_repeat(const float a, const float b) { + return b; + GGML_UNUSED(a); +} + +static __dpct_inline__ float op_add(const float a, const float b) { + return a + b; +} + +static __dpct_inline__ float op_sub(const float a, const float b) { + return a - b; +} + +static __dpct_inline__ float op_mul(const float a, const float b) { + return a * b; +} + +static __dpct_inline__ float op_div(const float a, const float b) { + return a / b; +} + + +void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +#endif // GGML_SYCL_ELEMENTWISE_HPP diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp similarity index 79% rename from ggml/src/ggml-sycl.cpp rename to ggml/src/ggml-sycl/ggml-sycl.cpp index 4d3f1c5ce0..255bc64c6b 100644 --- a/ggml/src/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -40,17 +40,313 @@ #include "ggml-sycl/presets.hpp" #include "ggml-sycl/gemm.hpp" -bool ggml_sycl_loaded(void); -void ggml_sycl_free_data(struct ggml_tensor * tensor); -void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); -void ggml_sycl_set_main_device(int main_device); -void ggml_sycl_set_mul_mat_q(bool mul_mat_q); -void ggml_sycl_get_device_description(int device, char * description, size_t description_size); -bool ggml_backend_is_sycl(ggml_backend_t backend); -int ggml_backend_sycl_get_device(ggml_backend_t backend); -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); -static inline int get_sycl_env(const char *env_name, int default_val); +static bool g_sycl_loaded = false; +static ggml_sycl_device_info ggml_sycl_init() { + ggml_sycl_device_info info = {}; + + info.device_count = dpct::dev_mgr::instance().device_count(); + if (info.device_count == 0) { + fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); + return info; + } + + GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); + + int64_t total_vram = 0; +#if defined(GGML_SYCL_FORCE_MMQ) + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); +#endif +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif + fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); + + for (int i = 0; i < info.device_count; ++i) { + info.devices[i].vmm = 0; + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(i)))); + + info.default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); + + info.devices[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + + info.max_work_group_sizes[i] = prop.get_max_work_group_size(); + } + + for (int id = 0; id < info.device_count; ++id) { + info.default_tensor_split[id] /= total_vram; + } + return info; +} + +const ggml_sycl_device_info & ggml_sycl_info() { + static ggml_sycl_device_info info = ggml_sycl_init(); + return info; +} + +void print_device_detail(int id, sycl::device &device, std::string device_type) { + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_device_info(prop, device))); + + std::string version; + version += std::to_string(prop.get_major_version()); + version += "."; + version += std::to_string(prop.get_minor_version()); + + device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); + std::string name = std::string(prop.get_name()); + name = std::regex_replace(name, std::regex("\\(R\\)"), ""); + name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); + + auto global_mem_size = prop.get_global_mem_size()/1000000; + + fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), + name.c_str(), version.c_str(), prop.get_max_compute_units(), + prop.get_max_work_group_size(), prop.get_max_sub_group_size(), + global_mem_size, device.get_info().c_str()); +} + +void ggml_backend_sycl_print_sycl_devices() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); + int device_count = dpct::dev_mgr::instance().device_count(); + std::map DeviceNums; + fprintf(stderr, "found %d SYCL devices:\n", device_count); + fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); + fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); + fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); + fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); + for (int id = 0; id < device_count; ++id) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + sycl::backend backend = device.get_backend(); + std::string backend_type = get_device_backend_and_type(device); + int type_id=DeviceNums[backend_type]++; + std::stringstream device_type; + device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; + print_device_detail(id, device, device_type.str()); + } +} + +static inline int get_sycl_env(const char *env_name, int default_val) { + char *user_device_string = getenv(env_name); + int user_number = default_val; + + unsigned n; + if (user_device_string != NULL && + sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number = default_val; + } + return user_number; +} + +static void ggml_check_sycl() try { + static bool initialized = false; + + if (!initialized) { + fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); + g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + + fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); + +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); +#endif + +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + + if (CHECK_TRY_ERROR(g_all_sycl_device_count = + dpct::dev_mgr::instance().device_count()) != 0) { + initialized = true; + g_sycl_loaded = false; + return; + } + GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); + ggml_backend_sycl_print_sycl_devices(); + initialized = true; + g_sycl_loaded = true; + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +inline void check_allow_gpu_index(const int device_index) { + if (device_index >= ggml_sycl_info().device_count) { + char error_buf[256]; + snprintf( + error_buf, + sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", + __func__, + device_index, + ggml_sycl_info().device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} + +GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len) try { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_gpu_list\n"); + for(int i=0;i=max_len) break; + id_list[i] = i; + } + return; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +// sycl buffer + +struct ggml_backend_sycl_buffer_context { + int device; + void * dev_ptr = nullptr; + queue_ptr stream; + std::string name; + + ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : + device(device), dev_ptr(dev_ptr), stream(stream) { + check_allow_gpu_index(device); + name = (GGML_SYCL_NAME + std::to_string(device)); + } + + + ~ggml_backend_sycl_buffer_context() { + if (dev_ptr != nullptr) { + ggml_sycl_set_device(device); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); + } + } +}; + +static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft); + +static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { + return buffer->buft->iface.get_name == ggml_backend_sycl_buffer_type_get_name; +} + +static void +ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_sycl_set_device(ctx->device); + + delete ctx; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + return ctx->dev_ptr; +} + +static void +ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + + if (tensor->view_src != NULL && tensor->view_offs == 0) { + assert(tensor->view_src->buffer->buft == buffer->buft); + tensor->backend = tensor->view_src->backend; + tensor->extra = tensor->view_src->extra; + return; + } + + + if (ggml_is_quantized(tensor->type)) { + // initialize padding to 0 to avoid possible NaN values + size_t original_size = ggml_nbytes(tensor); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + + if (padded_size > original_size && tensor->view_src == nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( + (char *)tensor->data + original_size, 0, + padded_size - original_size).wait())); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, + const void *data, size_t offset, + size_t size) try { + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); + char* host_buf = (char*)malloc(size); + memcpy(host_buf, data, size); + SYCL_CHECK( + CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) + .wait())); + free(host_buf); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, + void *data, size_t offset, + size_t size) try { + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); + + SYCL_CHECK(CHECK_TRY_ERROR( + stream.memcpy(data, (const char *)tensor->data + offset, size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, const void *ptr_src, size_t size) { @@ -60,6 +356,835 @@ void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, free(host_buf); } +static bool +ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *src, + ggml_tensor *dst) try { + if (ggml_backend_buffer_is_sycl(src->buffer)) { + ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; + + ggml_sycl_set_device(src_ctx->device); + /* + DPCT1009:198: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); + ggml_sycl_set_device(dst_ctx->device); + /* + DPCT1009:199: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); + /* + DPCT1009:200: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + + queue_ptr stream_dst = dst_ctx->stream; + queue_ptr stream_src = src_ctx->stream; + size_t size = ggml_nbytes(src); + + //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. + dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); + +//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove +#if 0 + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( + (char *)dst->data, (const char *)src->data, size).wait())); + + /* + DPCT1009:201: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); +#endif + return true; + } + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, + uint8_t value) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + queue_ptr stream = ctx->stream; + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memset(ctx->dev_ptr, value, buffer->size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { + /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, + /* .clear = */ ggml_backend_sycl_buffer_clear, + /* .reset = */ NULL, +}; + +// sycl buffer type +struct ggml_backend_sycl_buffer_type_context { + int device; + std::string name; + + // each buffer type has its own stream + queue_ptr stream = nullptr; +}; + +static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t +ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) try { + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_sycl_set_device(buft_ctx->device); + const queue_ptr stream = buft_ctx->stream; + size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 + + void * dev_ptr; + SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( + size, *stream))); + if (!dev_ptr) { + fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); + return nullptr; + } + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); + return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return dpct::get_current_device().get_max_mem_alloc_size(); + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); + int64_t ne0 = tensor->ne[0]; + + if (ggml_is_quantized(tensor->type)) { + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return size; + + GGML_UNUSED(buft); +} + +static const ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, + /* .is_host = */ NULL, +}; + +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); + + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); + + auto dev_count = ggml_backend_sycl_get_device_count(); + + if (device>=dev_count or device<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device, dev_count-1); + GGML_ASSERT(devicedevice; + if (device>=ggml_sycl_info().device_count or device<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device, ggml_sycl_info().device_count-1); + GGML_ASSERT(devicestream(i, 0)}, + }; + } + ggml_backend_sycl_buffer_type_initialized = true; + } + return &ggml_backend_sycl_buffer_types[device]; +} + +// sycl split buffer + +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { + int64_t min_compute_capability = INT_MAX; + int64_t max_compute_capability = INT_MIN; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > ggml_sycl_info().devices[i].cc) { + min_compute_capability = ggml_sycl_info().devices[i].cc; + } + if (max_compute_capability < ggml_sycl_info().devices[i].cc) { + max_compute_capability = ggml_sycl_info().devices[i].cc; + } + } + } + + switch(type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return 64; + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return 1; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_IQ3_S: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q6_K: + return 64; + default: + GGML_ABORT("fatal error"); + } +} + +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + if (id == ggml_sycl_info().device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + +static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); +} + +struct ggml_backend_sycl_split_buffer_type_context { + std::array tensor_split; +}; + +struct ggml_backend_sycl_split_buffer_context { + ~ggml_backend_sycl_split_buffer_context() try { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { + if (extra->events[i][is] != nullptr) { + /* + DPCT1009:206: SYCL uses exceptions to report errors and + does not use the error codes. The original code was + commented out and a warning string was inserted. You + need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[i][is]))); + } + } + if (extra->data_device[i] != nullptr) { + /* + DPCT1009:207: SYCL uses exceptions to report errors and does + not use the error codes. The original code was commented out + and a warning string was inserted. You need to rewrite this + code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( + extra->data_device[i], *(streams[i])))); + } + } + delete extra; + } + } + catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + std::vector tensor_extras; + std::vector streams; +}; + +static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + delete ctx; +} + +static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + GGML_UNUSED(buffer); +} + +static void +ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + ctx->streams.push_back(&(dpct::get_current_device().default_queue())); + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + char * buf; + /* + DPCT1009:208: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *stream))); + if (!buf) { + char err_buf[1024]; + snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); + throw std::runtime_error(err_buf); + } + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + /* + DPCT1009:209: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + extra->data_device[i] = buf; + + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { + /* + DPCT1009:210: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK( + CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); + } + } + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void +ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, const void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + /* + DPCT1009:211: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memcpy(extra->data_device[i], buf_host, original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void +ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + /* + DPCT1009:212: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memcpy(buf_host, extra->data_device[i], original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + GGML_UNUSED(buffer); + GGML_UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { + /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_split_buffer_clear, + /* .reset = */ NULL, +}; + +// sycl split buffer type + +static const char * ggml_backend_sycl_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Split"; + + GGML_UNUSED(buft); +} + +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->buft->iface.get_name == ggml_backend_sycl_split_buffer_type_get_name; +} + +static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); +} + +static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, +}; + +ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + static std::mutex mutex; + std::lock_guard lock(mutex); + + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); + ggml_check_sycl(); + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = ggml_sycl_info().default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), + /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + +// host buffer type + +static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Host"; + + GGML_UNUSED(buft); +} + +static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_sycl_host_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_sycl_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + // FIXME: this is a hack to avoid having to implement a new buffer type + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n"); + static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), + /* .context = */ nullptr, + }; + + return &ggml_backend_sycl_buffer_type_host; +} + +// buffer pool for sycl (legacy) +struct ggml_sycl_pool_leg : public ggml_sycl_pool { + static const int MAX_SYCL_BUFFERS = 256; + + int device; + queue_ptr qptr; + struct ggml_sycl_buffer { + void * ptr = nullptr; + size_t size = 0; + }; + + ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; + size_t pool_size = 0; + + explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : + qptr(qptr_), + device(device_) { + } + + ~ggml_sycl_pool_leg() { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer & b = buffer_pool[i]; + if (b.ptr != nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); + pool_size -= b.size; + } + } + GGML_ASSERT(pool_size == 0); + } + + void * alloc(size_t size, size_t * actual_size) override { +#ifdef DEBUG_sycl_MALLOC + int nnz = 0; + size_t max_size = 0; +#endif + size_t best_diff = 1ull << 36; + int ibest = -1; + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr != nullptr) { +#ifdef DEBUG_sycl_MALLOC + ++nnz; + if (b.size > max_size) max_size = b.size; +#endif + if (b.size >= size) { + size_t diff = b.size - size; + if (diff < best_diff) { + best_diff = diff; + ibest = i; + if (!best_diff) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + } + } + } + } + if (ibest >= 0) { + ggml_sycl_buffer& b = buffer_pool[ibest]; + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + void * ptr; + size_t look_ahead_size = (size_t) (1.05 * size); + + SYCL_CHECK( + CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( + look_ahead_size, *qptr))); + if (!ptr) { + fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); + return nullptr; + } + + *actual_size = look_ahead_size; + pool_size += look_ahead_size; + + #ifdef DEBUG_SYCL_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); + #endif + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); + return ptr; + } + + void free(void * ptr, size_t size) override { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); + pool_size -= size; + } +}; + +std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { + // TBD: NO VMM support + // if (ggml_sycl_info().devices[device].vmm) { + // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); + // } + return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); +} + +// TBD pool with virtual memory management +// struct ggml_sycl_pool_vmm : public ggml_sycl_pool + +/// kernels + typedef void (*cpy_kernel_t)(const char * cx, char * cdst); typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); typedef void (*ggml_sycl_op_mul_mat_t)( @@ -69,272 +1194,8 @@ typedef void (*ggml_sycl_op_mul_mat_t)( float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, const queue_ptr &stream); -typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream); -static __dpct_inline__ float op_repeat(const float a, const float b) { - return b; - GGML_UNUSED(a); -} -static __dpct_inline__ float op_add(const float a, const float b) { - return a + b; -} - -static __dpct_inline__ float op_mul(const float a, const float b) { - return a * b; -} - -static __dpct_inline__ float op_div(const float a, const float b) { - return a / b; -} - -template -static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13, - const sycl::nd_item<3> &item_ct1) { - const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1)); - const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0)) / - ne3; - const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0)) % - ne3; - - if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } - - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; - - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; - - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; - - for (int i0 = i0s; i0 < ne0; - i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); - } -} - -template -static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13, - const sycl::nd_item<3> &item_ct1) { - - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - const int i3 = i/(ne2*ne1*ne0); - const int i2 = (i/(ne1*ne0)) % ne2; - const int i1 = (i/ne0) % ne1; - const int i0 = i % ne0; - - if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } - - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; - - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; - - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; - - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); -} - -static void acc_f32(const float * x, const float * y, float * dst, const int ne, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= ne) { - return; - } - int src1_idx = i - offset; - int oz = src1_idx / nb2; - int oy = (src1_idx - (oz * nb2)) / nb1; - int ox = src1_idx % nb1; - if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { - dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; - } else { - dst[i] = x[i]; - } -} - -static void gelu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const float GELU_COEF_A = 0.044715f; - const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - - float xi = x[i]; - dst[i] = 0.5f * xi * - (1.0f + - sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi))); -} - -static void silu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i])); -} - -static void gelu_quick_f32(const float *x, float *dst, int k, - const sycl::nd_item<3> &item_ct1) { - const float GELU_QUICK_COEF = -1.702f; - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i]))); -} - -static void tanh_f32(const float *x, float *dst, int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = sycl::tanh((float)(x[i])); -} - -static void relu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = sycl::fmax((float)(x[i]), (float)0); -} - -static void hardsigmoid_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); -} - -static void hardswish_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); -} - -static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = sycl::fmax((float)(x[i]), (float)0) + - sycl::fmin((float)(x[i]), 0.0f) * negative_slope; -} - -static void sqr_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] * x[i]; -} - -static void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, - const int nb02, const int nb03, const int ne10, const int ne11, - const int ne12, const int ne13, const float sf0, const float sf1, - const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { - int index = item_ct1.get_local_id(0) + - item_ct1.get_group(0) * item_ct1.get_local_range(0); - if (index >= ne10 * ne11 * ne12 * ne13) { - return; - } - // operation - int i10 = index % ne10; - int i11 = (index / ne10) % ne11; - int i12 = (index / (ne10 * ne11)) % ne12; - int i13 = (index / (ne10 * ne11 * ne12)) % ne13; - - int i00 = i10 / sf0; - int i01 = i11 / sf1; - int i02 = i12 / sf2; - int i03 = i13 / sf3; - - dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); -} - -static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, - const sycl::nd_item<3> &item_ct1) { - int nidx = item_ct1.get_local_id(2) + - item_ct1.get_group(2) * item_ct1.get_local_range(2); - if (nidx >= ne0) { - return; - } - - // operation - int offset_dst = nidx + item_ct1.get_group(1) * ne0 + - item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); - if (nidx < ne00 && item_ct1.get_group(1) < ne01 && - item_ct1.get_group(0) < ne02) { - int offset_src = nidx + item_ct1.get_group(1) * ne00 + - item_ct1.get_group(0) * ne00 * ne01; - dst[offset_dst] = x[offset_src]; - } else { - dst[offset_dst] = 0.0f; - } -} template static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded, @@ -1023,297 +1884,6 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens (void) dst; } -template -struct bin_bcast_sycl { - template - void operator()(ggml_backend_sycl_context & ctx, - const struct ggml_tensor *src0, - const struct ggml_tensor *src1, struct ggml_tensor *dst, - const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, - queue_ptr stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - int nr0 = ne10/ne0; - int nr1 = ne11/ne1; - int nr2 = ne12/ne2; - int nr3 = ne13/ne3; - - int nr[4] = { nr0, nr1, nr2, nr3 }; - - // collapse dimensions until first broadcast dimension - int64_t cne0[] = {ne0, ne1, ne2, ne3}; - int64_t cne1[] = {ne10, ne11, ne12, ne13}; - size_t cnb0[] = {nb0, nb1, nb2, nb3}; - size_t cnb1[] = {nb10, nb11, nb12, nb13}; - auto collapse = [](int64_t cne[]) { - cne[0] *= cne[1]; - cne[1] = cne[2]; - cne[2] = cne[3]; - cne[3] = 1; - }; - - auto collapse_nb = [](size_t cnb[], int64_t cne[]) { - cnb[1] *= cne[1]; - cnb[2] *= cne[2]; - cnb[3] *= cne[3]; - }; - - for (int i = 0; i < 4; i++) { - if (nr[i] != 1) { - break; - } - if (i > 0) { - collapse_nb(cnb0, cne0); - collapse_nb(cnb1, cne1); - collapse(cne0); - collapse(cne1); - } - } - { - int64_t ne0 = cne0[0]; - int64_t ne1 = cne0[1]; - int64_t ne2 = cne0[2]; - int64_t ne3 = cne0[3]; - - int64_t ne10 = cne1[0]; - int64_t ne11 = cne1[1]; - int64_t ne12 = cne1[2]; - int64_t ne13 = cne1[3]; - - size_t nb0 = cnb0[0]; - size_t nb1 = cnb0[1]; - size_t nb2 = cnb0[2]; - size_t nb3 = cnb0[3]; - - size_t nb10 = cnb1[0]; - size_t nb11 = cnb1[1]; - size_t nb12 = cnb1[2]; - size_t nb13 = cnb1[3]; - - size_t s0 = nb0 / sizeof(dst_t); - size_t s1 = nb1 / sizeof(dst_t); - size_t s2 = nb2 / sizeof(dst_t); - size_t s3 = nb3 / sizeof(dst_t); - - size_t s10 = nb10 / sizeof(src1_t); - size_t s11 = nb11 / sizeof(src1_t); - size_t s12 = nb12 / sizeof(src1_t); - size_t s13 = nb13 / sizeof(src1_t); - - GGML_ASSERT(s0 == 1); - GGML_ASSERT(s10 == 1); - - const int block_size = 128; - - int64_t hne0 = std::max(ne0/2LL, 1LL); - - sycl::range<3> block_dims(1, 1, 1); - block_dims[2] = std::min(hne0, block_size); - block_dims[1] = std::min( - ne1, block_size / (unsigned int)block_dims[2]); - block_dims[0] = std::min( - std::min( - ne2 * ne3, block_size / (unsigned int)block_dims[2] / - (unsigned int)block_dims[1]), - 64U); - - sycl::range<3> block_nums( - (ne2 * ne3 + block_dims[0] - 1) / block_dims[0], - (ne1 + block_dims[1] - 1) / block_dims[1], - (hne0 + block_dims[2] - 1) / block_dims[2]); - - if (block_nums[0] > 65535) { - // this is the maximum number of blocks in z direction, fallback to 1D grid kernel - int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * - sycl::range<3>(1, 1, block_size), - sycl::range<3>(1, 1, block_size)), - [=](sycl::nd_item<3> item_ct1) { - k_bin_bcast_unravel( - src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, - ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12, - s13, item_ct1); - }); - } - } else { - /* - DPCT1049:16: The work-group size passed to the SYCL kernel may - exceed the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if - needed. - */ - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, - ne2, ne3, ne10, ne11, ne12, ne13, - s1, s2, s3, s11, s12, s13, - item_ct1); - }); - } - } - } -}; - -static void acc_f32_sycl(const float *x, const float *y, float *dst, - const int n_elements, const int ne10, const int ne11, - const int ne12, const int nb1, const int nb2, - const int offset, queue_ptr stream) { - int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, - item_ct1); - }); -} - -static void gelu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_f32(x, dst, k, item_ct1); - }); -} - -static void silu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - silu_f32(x, dst, k, item_ct1); - }); -} - -static void gelu_quick_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_quick_f32(x, dst, k, item_ct1); - }); -} - -static void tanh_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - tanh_f32(x, dst, k, item_ct1); - }); -} - -static void relu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - relu_f32(x, dst, k, item_ct1); - }); -} - -static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardsigmoid_f32(x, dst, k, item_ct1); - }); -} - -static void hardswish_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardswish_f32(x, dst, k, item_ct1); - }); -} - -static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, - const float negative_slope, - queue_ptr stream) { - const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - leaky_relu_f32(x, dst, k, negative_slope, item_ct1); - }); -} - -static void sqr_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sqr_f32(x, dst, k, item_ct1); - }); -} - -static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, - const int nb02, const int nb03, const int ne10, const int ne11, - const int ne12, const int ne13, const float sf0, const float sf1, - const float sf2, const float sf3, queue_ptr stream) { - int dst_size = ne10 * ne11 * ne12 * ne13; - int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; - sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), - [=](sycl::nd_item<1> item_ct1) { - upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); - }); -} - -static void pad_f32_sycl(const float *x, float *dst, const int ne00, - const int ne01, const int ne02, const int ne0, - const int ne1, const int ne2, queue_ptr stream) { - int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; - sycl::range<3> gridDim(ne2, ne1, num_blocks); - stream->parallel_for( - sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1); - }); -} static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, const int ky, const int kx_padded, @@ -1691,6 +2261,58 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, } } +static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols, + const int nrows, queue_ptr stream) { + const sycl::range<3> block_dims(1, 1, SYCL_ARGMAX_BLOCK_SIZE); + const sycl::range<3> block_nums(1, nrows, 1); + const size_t shared_mem = 256 * sizeof(float); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor shared_data( + sycl::range<1>(shared_mem/sizeof(float)), cgh); + sycl::local_accessor shared_indices( + sycl::range<1>(shared_mem/sizeof(float)), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + const int tid = item_ct1.get_local_id(2); + const int row = item_ct1.get_global_id(1); + + float max_val = -INFINITY; + int max_idx = -1; + + for (int col = tid; col < ncols; col += 256) { + float val = x[row * ncols + col]; + if (val > max_val) { + max_val = val; + max_idx = col; + } + } + + shared_data[tid] = max_val; + shared_indices[tid] = max_idx; + item_ct1.barrier(sycl::access::fence_space::local_space); + + for (int stride = 256/2; stride > 0; stride >>= 1) { + if (tid < stride) { + float val1 = shared_data[tid]; + float val2 = shared_data[tid + stride]; + if (val2 > val1) { + shared_data[tid] = val2; + shared_indices[tid] = shared_indices[tid + stride]; + } + } + item_ct1.barrier(sycl::access::fence_space::local_space); + } + + + if (tid == 0) { + dst[row] = shared_indices[0]; + } + }); + }); +} static void diag_mask_inf_f32_sycl(const float *x, float *dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, @@ -1706,296 +2328,6 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, }); } -static bool g_sycl_loaded = false; - -bool ggml_sycl_loaded(void) { - return g_sycl_loaded; -} - -void print_device_detail(int id, sycl::device &device, std::string device_type) { - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_device_info(prop, device))); - - std::string version; - version += std::to_string(prop.get_major_version()); - version += "."; - version += std::to_string(prop.get_minor_version()); - - device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); - std::string name = std::string(prop.get_name()); - name = std::regex_replace(name, std::regex("\\(R\\)"), ""); - name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); - - auto global_mem_size = prop.get_global_mem_size()/1000000; - - fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), - name.c_str(), version.c_str(), prop.get_max_compute_units(), - prop.get_max_work_group_size(), prop.get_max_sub_group_size(), - global_mem_size, device.get_info().c_str()); -} - -void ggml_backend_sycl_print_sycl_devices() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); - int device_count = dpct::dev_mgr::instance().device_count(); - std::map DeviceNums; - fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); - fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); - fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); - fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); - for (int id = 0; id < device_count; ++id) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - sycl::backend backend = device.get_backend(); - std::string backend_type = get_device_backend_and_type(device); - int type_id=DeviceNums[backend_type]++; - std::stringstream device_type; - device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; - print_device_detail(id, device, device_type.str()); - } -} - -static inline int get_sycl_env(const char *env_name, int default_val) { - char *user_device_string = getenv(env_name); - int user_number = default_val; - - unsigned n; - if (user_device_string != NULL && - sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number = default_val; - } - return user_number; -} - -static void ggml_check_sycl() try { - static bool initialized = false; - - if (!initialized) { - fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); - g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - - fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif - -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - - if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - initialized = true; - g_sycl_loaded = false; - return; - } - GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - ggml_backend_sycl_print_sycl_devices(); - initialized = true; - g_sycl_loaded = true; - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static ggml_sycl_device_info ggml_sycl_init() { - ggml_sycl_device_info info = {}; - - info.device_count = dpct::dev_mgr::instance().device_count(); - if (info.device_count == 0) { - fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); - return info; - } - - GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); - - int64_t total_vram = 0; -#if defined(GGML_SYCL_FORCE_MMQ) - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); -#endif -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif - fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); - - for (int i = 0; i < info.device_count; ++i) { - info.devices[i].vmm = 0; - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(i)))); - - info.default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); - - info.devices[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - - info.max_work_group_sizes[i] = prop.get_max_work_group_size(); - } - - for (int id = 0; id < info.device_count; ++id) { - info.default_tensor_split[id] /= total_vram; - } - return info; -} - -const ggml_sycl_device_info & ggml_sycl_info() { - static ggml_sycl_device_info info = ggml_sycl_init(); - return info; -} - -/* -device_index: device index from 0 to n (continue numbers). - It is used for device select/set in SYCL backend internal data structure. -*/ -inline void check_allow_gpu_index(const int device_index) { - if (device_index >= ggml_sycl_info().device_count) { - char error_buf[256]; - snprintf( - error_buf, - sizeof(error_buf), - "%s error: device_index:%d is out of range: [0-%d]", - __func__, - device_index, - ggml_sycl_info().device_count - 1); - fprintf(stderr, "%s\n", error_buf); - assert(false); - } -} - -// buffer pool for sycl (legacy) -struct ggml_sycl_pool_leg : public ggml_sycl_pool { - static const int MAX_SYCL_BUFFERS = 256; - - int device; - queue_ptr qptr; - struct ggml_sycl_buffer { - void * ptr = nullptr; - size_t size = 0; - }; - - ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; - size_t pool_size = 0; - - explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : - qptr(qptr_), - device(device_) { - } - - ~ggml_sycl_pool_leg() { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer & b = buffer_pool[i]; - if (b.ptr != nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); - pool_size -= b.size; - } - } - GGML_ASSERT(pool_size == 0); - } - - void * alloc(size_t size, size_t * actual_size) override { -#ifdef DEBUG_sycl_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_sycl_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } - } - } - } - if (ibest >= 0) { - ggml_sycl_buffer& b = buffer_pool[ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - - SYCL_CHECK( - CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( - look_ahead_size, *qptr))); - if (!ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); - return nullptr; - } - - *actual_size = look_ahead_size; - pool_size += look_ahead_size; - - #ifdef DEBUG_SYCL_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); - #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); - return ptr; - } - - void free(void * ptr, size_t size) override { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); - pool_size -= size; - } -}; - -std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { - // TBD: NO VMM support - // if (ggml_sycl_info().devices[device].vmm) { - // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); - // } - return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); -} - -// TBD pool with virtual memory management -// struct ggml_sycl_pool_vmm : public ggml_sycl_pool - static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, const struct ggml_tensor *src, int64_t i3, int64_t i2, @@ -2111,33 +2443,6 @@ static void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, const ggml_te } } -template -inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, - (sycl::half *)dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, - main_stream); - } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { - op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, - main_stream); - } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { - op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, - main_stream); - } else { - fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, - ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); - GGML_ABORT("fatal error"); - } -} static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -2151,278 +2456,6 @@ static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tens (void) src1_d; } -inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes - - acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); - - (void) dst; -} - -inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - const float sf0 = (float)dst->ne[0]/src0->ne[0]; - const float sf1 = (float)dst->ne[1]/src0->ne[1]; - const float sf2 = (float)dst->ne[2]/src0->ne[2]; - const float sf3 = (float)dst->ne[3]/src0->ne[3]; - - upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, - main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - - pad_f32_sycl(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { - int64_t min_compute_capability = INT_MAX; - int64_t max_compute_capability = INT_MIN; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { - if (min_compute_capability > ggml_sycl_info().devices[i].cc) { - min_compute_capability = ggml_sycl_info().devices[i].cc; - } - if (max_compute_capability < ggml_sycl_info().devices[i].cc) { - max_compute_capability = ggml_sycl_info().devices[i].cc; - } - } - } - - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ4_NL: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_IQ3_S: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q6_K: - return 64; - default: - GGML_ABORT("fatal error"); - } - -} inline void ggml_sycl_op_mul_mat_sycl( ggml_backend_sycl_context & ctx, @@ -2592,6 +2625,23 @@ static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tens (void) src1_dd; } +inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne = ggml_nelements(src0); + + sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -2632,6 +2682,25 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_ten (void) src1_dd; } +inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + argmax_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, @@ -2702,46 +2771,6 @@ inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tenso (void) src1_dd; } -static void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const ggml_sycl_op_flatten_t op) try { - const int64_t nrows0 = ggml_nrows(src0); - - const bool use_src1 = src1 != nullptr; - const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; - - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - - // dd = data device - float * src0_ddf = (float *) src0->data; - float * src1_ddf = use_src1 ? (float *) src1->data : nullptr; - float * dst_ddf = (float *) dst->data; - - ggml_sycl_pool_alloc src0_f(ctx.pool()); - ggml_sycl_pool_alloc src1_f(ctx.pool()); - ggml_sycl_pool_alloc dst_f(ctx.pool()); - - ggml_sycl_set_device(ctx.device); - queue_ptr main_stream = ctx.stream(); - // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device); - - // do the computation - op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); - // print_ggml_tensor("tensor", dst); -} -catch (sycl::exception const &exc) { - - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { static bool peer_access_enabled = false; @@ -2783,10 +2812,6 @@ static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { peer_access_enabled = enable_peer_access; } -struct ggml_backend_sycl_split_buffer_type_context { - std::array tensor_split; -}; - static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, @@ -3125,115 +3150,24 @@ static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tenso GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - - static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } +static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { @@ -3849,6 +3783,11 @@ static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col); } +static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum); +} + static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows); @@ -3859,18 +3798,17 @@ static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort); } +static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argmax); +} + static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { (void) src0; (void) src1; (void) dst; } -static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); -} - void ggml_sycl_set_main_device(const int main_device) try { if (dpct::get_current_device_id() == main_device) return; check_allow_gpu_index(main_device); @@ -3896,6 +3834,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens ggml_sycl_func_t func; switch (tensor->op) { + case GGML_OP_ARGMAX: + func = ggml_sycl_argmax; + break; case GGML_OP_CONV_TRANSPOSE_1D: func = ggml_sycl_op_conv_transpose_1d; break; @@ -3909,19 +3850,32 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens func = ggml_sycl_dup; break; case GGML_OP_ADD: + case GGML_OP_ADD1: // TODO: more efficient implementation func = ggml_sycl_add; break; + case GGML_OP_SUB: + func = ggml_sycl_sub; + break; case GGML_OP_ACC: func = ggml_sycl_acc; break; case GGML_OP_MUL: func = ggml_sycl_mul; break; + case GGML_OP_LOG: + func = ggml_sycl_log; + break; case GGML_OP_DIV: func = ggml_sycl_div; break; case GGML_OP_UNARY: switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_NEG: + func = ggml_sycl_neg; + break; + case GGML_UNARY_OP_STEP: + func = ggml_sycl_step; + break; case GGML_UNARY_OP_GELU: func = ggml_sycl_gelu; break; @@ -3937,12 +3891,18 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_UNARY_OP_RELU: func = ggml_sycl_relu; break; + case GGML_UNARY_OP_SIGMOID: + func = ggml_sycl_sigmoid; + break; case GGML_UNARY_OP_HARDSIGMOID: func = ggml_sycl_hardsigmoid; break; case GGML_UNARY_OP_HARDSWISH: func = ggml_sycl_hardswish; break; + case GGML_UNARY_OP_EXP: + func = ggml_sycl_exp; + break; default: return false; } @@ -3980,12 +3940,24 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens } func = ggml_sycl_mul_mat_id; break; + case GGML_OP_OUT_PROD: + func = ggml_sycl_op_out_prod; + break; case GGML_OP_SCALE: func = ggml_sycl_scale; break; case GGML_OP_SQR: func = ggml_sycl_sqr; break; + case GGML_OP_SQRT: + func = ggml_sycl_sqrt; + break; + case GGML_OP_SIN: + func = ggml_sycl_sin; + break; + case GGML_OP_COS: + func = ggml_sycl_cos; + break; case GGML_OP_CLAMP: func = ggml_sycl_clamp; break; @@ -4017,6 +3989,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_OP_POOL_2D: func = ggml_sycl_pool2d; break; + case GGML_OP_SUM: + func = ggml_sycl_sum; + break; case GGML_OP_SUM_ROWS: func = ggml_sycl_sum_rows; break; @@ -4026,6 +4001,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_OP_TIMESTEP_EMBEDDING: func = ggml_sycl_op_timestep_embedding; break; + case GGML_OP_RWKV_WKV6: + func = ggml_sycl_op_rwkv_wkv6; + break; default: return false; } @@ -4038,39 +4016,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens return true; } -GGML_API void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n"); - for(int i=0;i=max_len) break; - id_list[i] = i; - } - return; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -int ggml_sycl_get_device_count() try { - int device_count; - if (CHECK_TRY_ERROR(device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - return 0; - } - return device_count; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -GGML_API void ggml_sycl_get_device_description(int device, char *description, +GGML_API void ggml_backend_sycl_get_device_description(int device, char *description, size_t description_size) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n"); + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_description\n"); dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( prop, dpct::dev_mgr::instance().get_device(device)))); @@ -4108,801 +4056,9 @@ catch (sycl::exception const &exc) { //////////////////////////////////////////////////////////////////////////////// -// backend interface - -#define UNUSED GGML_UNUSED - -// sycl buffer - -struct ggml_backend_sycl_buffer_context { - int device; - void * dev_ptr = nullptr; - queue_ptr stream; - std::string name; - - ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : - device(device), dev_ptr(dev_ptr), stream(stream) { - check_allow_gpu_index(device); - name = (GGML_SYCL_NAME + std::to_string(device)); - } - - - ~ggml_backend_sycl_buffer_context() { - if (dev_ptr != nullptr) { - ggml_sycl_set_device(device); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); - } - } -}; - -static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - -static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name; -} - -static void -ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - ggml_sycl_set_device(ctx->device); - - delete ctx; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->dev_ptr; -} - -static void -ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - - if (tensor->view_src != NULL && tensor->view_offs == 0) { - assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; - return; - } - - - if (ggml_is_quantized(tensor->type)) { - // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - - if (padded_size > original_size && tensor->view_src == nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( - (char *)tensor->data + original_size, 0, - padded_size - original_size).wait())); - } - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, - const void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - char* host_buf = (char*)malloc(size); - memcpy(host_buf, data, size); - SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) - .wait())); - free(host_buf); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, - void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); - - SYCL_CHECK(CHECK_TRY_ERROR( - stream.memcpy(data, (const char *)tensor->data + offset, size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static bool -ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *src, - ggml_tensor *dst) try { - if (ggml_backend_buffer_is_sycl(src->buffer)) { - ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; - ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; - - ggml_sycl_set_device(src_ctx->device); - /* - DPCT1009:198: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); - ggml_sycl_set_device(dst_ctx->device); - /* - DPCT1009:199: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); - /* - DPCT1009:200: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - - queue_ptr stream_dst = dst_ctx->stream; - queue_ptr stream_src = src_ctx->stream; - size_t size = ggml_nbytes(src); - - //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. - dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); - -//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove -#if 0 - SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( - (char *)dst->data, (const char *)src->data, size).wait())); - - /* - DPCT1009:201: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); -#endif - return true; - } - return false; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, - uint8_t value) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - queue_ptr stream = ctx->stream; - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memset(ctx->dev_ptr, value, buffer->size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_get_name, - /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, - /* .clear = */ ggml_backend_sycl_buffer_clear, - /* .reset = */ NULL, -}; - -// sycl buffer type -struct ggml_backend_sycl_buffer_type_context { - int device; - std::string name; - - // each buffer type has its own stream - queue_ptr stream = nullptr; -}; - -static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { - ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} -static ggml_backend_buffer_t -ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) try { - ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_sycl_set_device(buft_ctx->device); - const queue_ptr stream = buft_ctx->stream; - size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 - - void * dev_ptr; - SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( - size, *stream))); - if (!dev_ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); - return nullptr; - } - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); - return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return dpct::get_current_device().get_max_mem_alloc_size(); - - UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; - - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return size; - - UNUSED(buft); -} - -static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, - /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, - /* .is_host = */ nullptr, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); - - if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, ggml_sycl_info().device_count-1); - GGML_ASSERT(devicedevice; - if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, ggml_sycl_info().device_count-1); - GGML_ASSERT(devicestream(i, 0)}, - }; - } - ggml_backend_sycl_buffer_type_initialized = true; - } - return &ggml_backend_sycl_buffer_types[device]; -} - -// sycl split buffer type -static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { - const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor->type, tensor_split); - - *row_low = id == 0 ? 0 : nrows*tensor_split[id]; - *row_low -= *row_low % rounding; - if (id == ggml_sycl_info().device_count - 1) { - *row_high = nrows; - } else { - *row_high = nrows*tensor_split[id + 1]; - *row_high -= *row_high % rounding; - } -} - -struct ggml_backend_sycl_split_buffer_context { - ~ggml_backend_sycl_split_buffer_context() try { - for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - if (extra->events[i][is] != nullptr) { - /* - DPCT1009:206: SYCL uses exceptions to report errors and - does not use the error codes. The original code was - commented out and a warning string was inserted. You - need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[i][is]))); - } - } - if (extra->data_device[i] != nullptr) { - /* - DPCT1009:207: SYCL uses exceptions to report errors and does - not use the error codes. The original code was commented out - and a warning string was inserted. You need to rewrite this - code. - */ - ggml_sycl_set_device(i); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( - extra->data_device[i], *(streams[i])))); - } - } - delete extra; - } - } - catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); - } - - std::vector tensor_extras; - std::vector streams; -}; - -static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Split"; - - UNUSED(buffer); -} - -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -} - -static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - delete ctx; -} - -static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { - // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced - return (void *)0x1000; - - UNUSED(buffer); -} - -static void -ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - - ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; - - ctx->tensor_extras.push_back(extra); - ctx->streams.push_back(&(dpct::get_current_device().default_queue())); - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - // FIXME: do not crash if cudaMalloc fails - // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - char * buf; - /* - DPCT1009:208: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - if (!buf) { - char err_buf[1024]; - snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); - throw std::runtime_error(err_buf); - } - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - /* - DPCT1009:209: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - extra->data_device[i] = buf; - - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - /* - DPCT1009:210: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK( - CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); - } - } - tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, const void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - const char * buf_host = (const char *)data + offset_split; - /* - DPCT1009:211: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(extra->data_device[i], buf_host, original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf_host = (char *)data + offset_split; - /* - DPCT1009:212: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(buf_host, extra->data_device[i], original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - UNUSED(buffer); - UNUSED(value); -} - -static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, - /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_sycl_split_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Split"; - - UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point - // instead, we allocate them for each tensor separately in init_tensor - // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, - // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. - ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); - - return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - UNUSED(buft); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; - - size_t total_size = 0; - - const int64_t ne0 = tensor->ne[0]; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - total_size += ggml_nbytes_split(tensor, nrows_split); - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return total_size; -} - -static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - UNUSED(buft); -} - -static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); - ggml_check_sycl(); - // FIXME: this is not thread safe - static std::map, struct ggml_backend_buffer_type> buft_map; - - std::array tensor_split_arr = {}; - - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); - if (all_zero) { - tensor_split_arr = ggml_sycl_info().default_tensor_split; - } else { - float split_sum = 0.0f; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] /= split_sum; - } - } - - auto it = buft_map.find(tensor_split_arr); - if (it != buft_map.end()) { - return &it->second; - } - - struct ggml_backend_buffer_type buft { - /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, - /* .device = */ nullptr, - /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, - }; - - auto result = buft_map.emplace(tensor_split_arr, buft); - return &result.first->second; -} - -// host buffer type - -static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Host"; - - UNUSED(buft); -} - -static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Host"; - - UNUSED(buffer); -} - -static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_sycl_host_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_sycl_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - // FIXME: this is a hack to avoid having to implement a new buffer type - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; - buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n"); - static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ nullptr, - /* .context = */ nullptr, - }; - - return &ggml_backend_sycl_buffer_type_host; -} - // backend -static const char * ggml_backend_sycl_name(ggml_backend_t backend) { +static const char * ggml_backend_sycl_get_name(ggml_backend_t backend) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; @@ -4916,12 +4072,6 @@ static void ggml_backend_sycl_free(ggml_backend_t backend) { delete backend; } - -static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - return ggml_backend_sycl_buffer_type(sycl_ctx->device); -} - static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, @@ -4931,8 +4081,8 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( - (char *)tensor->data + offset, data, size).wait())); + SYCL_CHECK(CHECK_TRY_ERROR( + (stream)->memcpy((char *)tensor->data + offset, data, size))); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -4987,7 +4137,7 @@ static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait())); - UNUSED(backend); + GGML_UNUSED(backend); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -5023,7 +4173,147 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_ return GGML_STATUS_SUCCESS; } -static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +static void ggml_backend_sycl_event_record(ggml_backend_t backend, ggml_backend_event_t event) +try +{ + ggml_backend_sycl_context *sycl_ctx = + (ggml_backend_sycl_context *)backend->context; + sycl::event *sycl_event = static_cast(event->context); + + const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0); + // Record the current state of the queue + SYCL_CHECK(CHECK_TRY_ERROR(*sycl_event = stream->ext_oneapi_submit_barrier())); +} +catch (sycl::exception const &exc) +{ + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try { + ggml_backend_sycl_context* sycl_ctx = static_cast(backend->context); + sycl::event* sycl_event = static_cast(event->context); + + if (ggml_backend_is_sycl(backend)) { + SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); + } else + GGML_ABORT("fatal error"); +} catch (sycl::exception const& exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static ggml_backend_i ggml_backend_sycl_interface = { + /* .get_name = */ ggml_backend_sycl_get_name, + /* .free = */ ggml_backend_sycl_free, + /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, + /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_sycl_cpy_tensor_async, + // // TODO: update for the new + // interface + /* .synchronize = */ ggml_backend_sycl_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_sycl_graph_compute, + /* .event_record = */ ggml_backend_sycl_event_record, + /* .event_wait = */ ggml_backend_sycl_event_wait, +}; + +static ggml_guid_t ggml_backend_sycl_guid() { + static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; + return &guid; +} + +bool ggml_backend_is_sycl(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); +} + +int ggml_backend_sycl_get_device_count() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); + return ggml_sycl_info().device_count; +} + + +// backend device + +struct ggml_backend_sycl_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_sycl_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_sycl_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + ggml_sycl_set_device(ctx->device); + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(ctx->device).get_memory_info(*free, *total))); +} + +static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_sycl_device_get_name(dev); + props->description = ggml_backend_sycl_device_get_description(dev); + props->type = ggml_backend_sycl_device_get_type(dev); + ggml_backend_sycl_device_get_memory(dev, &props->memory_free, &props->memory_total); + + bool host_buffer = getenv("GGML_SYCL_NO_PINNED") == nullptr; +#ifdef GGML_SYCL_NO_PEER_COPY + bool events = false; +#else + bool events = true; +#endif + + props->caps = { + /* .async = */ true, + /* .host_buffer = */ host_buffer, + /* .buffer_from_host_ptr = */ false, + /* .events = */ events, + }; +} + +static ggml_backend_t ggml_backend_sycl_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ggml_backend_sycl_init(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ggml_backend_sycl_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_host_buffer_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return ggml_backend_sycl_host_buffer_type(); +} + +static ggml_backend_buffer_t ggml_backend_sycl_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + GGML_UNUSED(dev); + GGML_UNUSED(ptr); + GGML_UNUSED(size); + GGML_UNUSED(max_tensor_size); + return nullptr; +} + +static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_CONV_TRANSPOSE_1D: { @@ -5036,13 +4326,17 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_EXP: return ggml_is_contiguous(op->src[0]); default: return false; @@ -5079,6 +4373,8 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten } return true; } break; + case GGML_OP_OUT_PROD: + return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1; case GGML_OP_GET_ROWS: { switch (op->src[0]->type) { @@ -5124,10 +4420,10 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - int dim = op->op_params[0]; - return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16 && dim == 2; + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_DUP: + case GGML_OP_ARGMAX: case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_REPEAT: @@ -5136,11 +4432,17 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_TRANSPOSE: case GGML_OP_NORM: case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_LOG: + case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_SIN: + case GGML_OP_COS: case GGML_OP_CLAMP: return true; case GGML_OP_CONT: @@ -5154,6 +4456,7 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten // TODO: add support for the new F32 operations return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_2D: + case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: @@ -5162,52 +4465,195 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_PAD: case GGML_OP_LEAKY_RELU: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_RWKV_WKV6: return true; default: return false; } - UNUSED(backend); + GGML_UNUSED(dev); } -static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) { - const int min_batch_size = 32; - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID; - GGML_UNUSED(backend); -} - -static bool ggml_backend_sycl_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_sycl_buffer_type_name) { +static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_sycl_buffer_type_get_name) { return false; } ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + ggml_backend_sycl_device_context * sycl_ctx = (ggml_backend_sycl_device_context *)dev->context; return buft_ctx->device == sycl_ctx->device; } -static ggml_backend_i ggml_backend_sycl_interface = { - /* .get_name = */ ggml_backend_sycl_name, - /* .free = */ ggml_backend_sycl_free, - /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, - /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface - /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_sycl_graph_compute, - /* .supports_op = */ ggml_backend_sycl_supports_op, - /* .supports_buft = */ ggml_backend_sycl_supports_buft, - /* .offload_op = */ ggml_backend_sycl_offload_op, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, +static int64_t get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return op->ne[1]; // this will increse the speed of prefill in test + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + return op->ne[2]; + default: + return ggml_nrows(op); + } +} + +static bool ggml_backend_sycl_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + const int min_batch_size = 32; + return get_op_batch_size(op) >= min_batch_size; + GGML_UNUSED(dev); +} + +static ggml_backend_event_t +ggml_backend_sycl_device_event_new(ggml_backend_dev_t dev) { + +#ifdef GGML_SYCL_NO_PEER_COPY + return nullptr; +#else + sycl::event *event_ptr = new sycl::event(); + + return new ggml_backend_event{ + /* .device = */ dev, + /* .context = */ event_ptr, + }; +#endif +} + +static void ggml_backend_sycl_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) try { + GGML_UNUSED(dev); + if (event == nullptr) { + return; + } + + if (event->context != nullptr) { + sycl::event *sycl_event = static_cast(event->context); + delete sycl_event; + event->context = nullptr; + } + + delete event; +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +static void ggml_backend_sycl_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) try { + GGML_UNUSED(dev); + + sycl::event *sycl_event = static_cast(event->context); + SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static const ggml_backend_device_i ggml_backend_sycl_device_interface = { + /* .get_name = */ ggml_backend_sycl_device_get_name, + /* .get_description = */ ggml_backend_sycl_device_get_description, + /* .get_memory = */ ggml_backend_sycl_device_get_memory, + /* .get_type = */ ggml_backend_sycl_device_get_type, + /* .get_props = */ ggml_backend_sycl_device_get_props, + /* .init_backend = */ ggml_backend_sycl_device_init, + /* .get_buffer_type = */ ggml_backend_sycl_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_sycl_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ ggml_backend_sycl_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_sycl_device_supports_op, + /* .supports_buft = */ ggml_backend_sycl_device_supports_buft, + /* .offload_op = */ ggml_backend_sycl_device_offload_op, + /* .event_new = */ ggml_backend_sycl_device_event_new, + /* .event_free = */ ggml_backend_sycl_device_event_free, + /* .event_synchronize = */ ggml_backend_sycl_device_event_synchronize, }; -static ggml_guid_t ggml_backend_sycl_guid() { - static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; - return &guid; +// backend reg + +struct ggml_backend_sycl_reg_context { + std::vector devices; +}; + +static const char * ggml_backend_sycl_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return GGML_SYCL_NAME; +} + +static size_t ggml_backend_sycl_reg_get_device_count(ggml_backend_reg_t reg) { + ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; + return ctx->devices.size(); +} + +static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t reg, size_t index) { + ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; +} + +static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) { + GGML_UNUSED(reg); + + // TODO: update to the current function signature + //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { + // return (void *)ggml_backend_sycl_split_buffer_type; + //} + + // SYCL doesn't support registering host memory, left here for reference + // "ggml_backend_register_host_buffer" + // "ggml_backend_unregister_host_buffer" + return nullptr; +} + +static const ggml_backend_reg_i ggml_backend_sycl_reg_interface = { + /* .get_name = */ ggml_backend_sycl_reg_get_name, + /* .get_device_count = */ ggml_backend_sycl_reg_get_device_count, + /* .get_device_get = */ ggml_backend_sycl_reg_get_device, + /* .get_proc_address = */ ggml_backend_sycl_reg_get_proc_address, +}; + + +// backend registry + +ggml_backend_reg_t ggml_backend_sycl_reg() { + static ggml_backend_reg reg; + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + ggml_backend_sycl_reg_context * ctx = new ggml_backend_sycl_reg_context; + + for (int i = 0; i < ggml_sycl_info().device_count; i++) { + ggml_backend_sycl_device_context * dev_ctx = new ggml_backend_sycl_device_context; + dev_ctx->device = i; + dev_ctx->name = GGML_SYCL_NAME + std::to_string(i); + + ggml_sycl_set_device(i); + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(i)))); + + dev_ctx->description = prop.get_name(); + + ggml_backend_dev_t dev = new ggml_backend_device { + /* .interface = */ ggml_backend_sycl_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx + }; + ctx->devices.push_back(dev); + } + + reg = ggml_backend_reg { + /* .interface = */ ggml_backend_sycl_reg_interface, + /* .context = */ ctx + }; + } + + initialized = true; + } + + return ® } ggml_backend_t ggml_backend_sycl_init(int device) { @@ -5225,18 +4671,10 @@ ggml_backend_t ggml_backend_sycl_init(int device) { ggml_backend_t sycl_backend = new ggml_backend { /* .guid = */ ggml_backend_sycl_guid(), /* .interface = */ ggml_backend_sycl_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), device), /* .context = */ ctx }; return sycl_backend; } -bool ggml_backend_is_sycl(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); -} - -int ggml_backend_sycl_get_device_count() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); - return ggml_sycl_info().device_count; -} diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 1b96925e14..7b10cf6881 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -1,6 +1,6 @@ #include "mmvq.hpp" #include "vecdotq.hpp" - +#include template static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, @@ -13,7 +13,8 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -37,7 +38,7 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -61,7 +62,8 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -85,7 +87,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -109,8 +111,8 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -133,7 +135,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -157,8 +159,8 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -181,7 +183,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -205,8 +207,8 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -229,7 +231,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -253,8 +255,8 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -277,7 +279,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -301,8 +303,8 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -325,7 +327,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -349,8 +351,8 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -373,7 +375,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -397,8 +399,8 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -421,7 +423,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -446,8 +448,8 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -470,7 +472,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -487,7 +489,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -495,7 +497,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -511,7 +513,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -519,7 +521,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -535,7 +537,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK5_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -543,7 +545,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -559,7 +561,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK5_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -567,7 +569,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -583,7 +585,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK8_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -591,7 +593,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -607,7 +609,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -615,7 +617,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -631,7 +633,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -639,7 +641,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -655,7 +657,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -663,7 +665,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -679,7 +681,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -687,7 +689,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -703,7 +705,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -711,7 +713,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -728,13 +730,13 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -749,7 +751,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -759,7 +761,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -774,7 +776,7 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -784,7 +786,7 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -799,7 +801,7 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -809,7 +811,7 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq3_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -824,7 +826,7 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -833,7 +835,7 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq3_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -848,7 +850,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -858,7 +860,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq1_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -873,13 +875,13 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq1_m_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -894,14 +896,14 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_NL == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq4_nl_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -916,14 +918,14 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq4_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index b3159b9d1b..72d8fdb878 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -8,7 +8,6 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); sycl::float2 mean_var = sycl::float2(0.f, 0.f); for (int col = tid; col < ncols; col += block_size) { @@ -55,7 +54,6 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con int end = start + group_size; const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); start += item_ct1.get_local_id(2); int nreduce = nwarps / WARP_SIZE; @@ -144,7 +142,6 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa const int tid = item_ct1.get_local_id(2); const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); float tmp = 0.0f; // partial sum for thread in warp for (int col = tid; col < ncols; col += block_size) { @@ -202,6 +199,7 @@ static void norm_f32_sycl(const float* x, float* dst, const int ncols, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:17: The work-group size passed to the SYCL kernel may exceed @@ -244,6 +242,7 @@ static void group_norm_f32_sycl(const float* x, float* dst, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:18: The work-group size passed to the SYCL kernel may exceed @@ -290,6 +289,7 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:19: The work-group size passed to the SYCL kernel may exceed diff --git a/ggml/src/ggml-sycl/outprod.cpp b/ggml/src/ggml-sycl/outprod.cpp new file mode 100644 index 0000000000..e61cdc2ca5 --- /dev/null +++ b/ggml/src/ggml-sycl/outprod.cpp @@ -0,0 +1,56 @@ +#include +#include +#include "outprod.hpp" + + +void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst) { + + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + GGML_TENSOR_BINARY_OP_LOCALS + + // Get SYCL queue + dpct::queue_ptr stream = ctx.stream(); + + // Dimension checks + GGML_ASSERT(ne01 == ne11); // Inner dimensions must match + GGML_ASSERT(ne0 == ne00); // Output rows match src0 rows + GGML_ASSERT(ne1 == ne10); // Output cols match src1 cols + + // Get data pointers + const float* src0_d = (const float*)src0->data; + const float* src1_d = (const float*)src1->data; + float* dst_d = (float*)dst->data; + + // GEMM parameters + const float alpha = 1.0f; + const float beta = 0.0f; + + // Handle transposition of src1 + const bool src1_T = ggml_is_transposed(src1); + const oneapi::mkl::transpose src1_op = + src1_T ? oneapi::mkl::transpose::nontrans : oneapi::mkl::transpose::trans; + const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float); + + try { + // Perform matrix multiplication using oneMKL GEMM + oneapi::mkl::blas::column_major::gemm(*stream, + oneapi::mkl::transpose::nontrans, src1_op, + ne0, ne1, ne01, + alpha, + src0_d, ne00, + src1_d, ldb, + beta, + dst_d, ne0); + } + catch (sycl::exception const& exc) { + std::cerr << exc.what() << std::endl; + GGML_ASSERT(false); + } +} diff --git a/ggml/src/ggml-sycl/outprod.hpp b/ggml/src/ggml-sycl/outprod.hpp new file mode 100644 index 0000000000..9c042738a4 --- /dev/null +++ b/ggml/src/ggml-sycl/outprod.hpp @@ -0,0 +1,11 @@ +#ifndef GGML_SYCL_OUTPROD_HPP +#define GGML_SYCL_OUTPROD_HPP + +#include "common.hpp" + +void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst); + + +#endif // GGML_SYCL_OUTPROD_HPP + diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index 340ab8e932..af1890727d 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -25,6 +25,11 @@ #define SYCL_RELU_BLOCK_SIZE 256 #define SYCL_HARDSIGMOID_BLOCK_SIZE 256 #define SYCL_HARDSWISH_BLOCK_SIZE 256 +#define SYCL_EXP_BLOCK_SIZE 256 +#define SYCL_NEG_BLOCK_SIZE 256 +#define SYCL_SIGMOID_BLOCK_SIZE 256 +#define SYCL_SQRT_BLOCK_SIZE 256 +#define SYCL_SIN_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 @@ -41,6 +46,7 @@ #define SYCL_ACC_BLOCK_SIZE 256 #define SYCL_IM2COL_BLOCK_SIZE 256 #define SYCL_POOL2D_BLOCK_SIZE 256 +#define SYCL_ARGMAX_BLOCK_SIZE 256 #define SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE 256 #define SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index d2dccade20..c5942008ad 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -968,8 +968,8 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, grid1[0] ^ signs[0], signs[0], std::minus<>()); const int grid_h = dpct::vectorized_binary( grid2[0] ^ signs[1], signs[1], std::minus<>()); - sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); - sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + sumi = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi); q8 += 8; aux32 >>= 7; } @@ -1009,8 +1009,8 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, grid1[0] ^ signs0, signs0, std::minus<>()); const int grid_h = dpct::vectorized_binary( grid2[0] ^ signs1, signs1, std::minus<>()); - sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); - sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + sumi = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi); q8 += 8; } const float d = diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp new file mode 100644 index 0000000000..4c737f4bfc --- /dev/null +++ b/ggml/src/ggml-sycl/wkv6.cpp @@ -0,0 +1,138 @@ +#include +#include "wkv6.hpp" + +constexpr int WKV_BLOCK_SIZE = 64; // Matching CUDA_WKV_BLOCK_SIZE + +// Helper function for the main kernel +static void rwkv_wkv_f32_kernel( + const int B, const int T, const int C, const int H, + const float* k, const float* v, const float* r, + const float* tf, const float* td, const float* s, + float* dst, const sycl::nd_item<3>& item_ct1, float* shared_mem) { + + const int tid = item_ct1.get_local_id(2); + const int bid = item_ct1.get_group(2); + + const int head_size = WKV_BLOCK_SIZE; + const int batch_i = bid / H; + const int head_i = bid % H; + const int state_size = C * head_size; + const int n_seq_tokens = T / B; + + // Set up shared memory pointers + float* _k = shared_mem; + float* _r = _k + head_size; + float* _tf = _r + head_size; + float* _td = _tf + head_size; + + // Local state array + float state[WKV_BLOCK_SIZE]; + + // Load initial state + #pragma unroll + for (int i = 0; i < head_size; i++) { + state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid]; + } + + // Sync threads before shared memory operations + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Load time-mixing parameters + _tf[tid] = tf[head_i * head_size + tid]; + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Main sequence processing loop + for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; + t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; + t += C) { + + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Load current timestep data to shared memory + _k[tid] = k[t]; + _r[tid] = r[t]; + _td[tid] = td[t]; + + item_ct1.barrier(sycl::access::fence_space::local_space); + + const float _v = v[t]; + float y = 0; + + // Process in chunks of 4 for better vectorization + sycl::float4 k4, r4, tf4, td4, s4, kv4; + #pragma unroll + for (int j = 0; j < head_size; j += 4) { + // Load data in vec4 chunks + k4 = sycl::float4(_k[j], _k[j+1], _k[j+2], _k[j+3]); + r4 = sycl::float4(_r[j], _r[j+1], _r[j+2], _r[j+3]); + tf4 = sycl::float4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]); + td4 = sycl::float4(_td[j], _td[j+1], _td[j+2], _td[j+3]); + s4 = sycl::float4(state[j], state[j+1], state[j+2], state[j+3]); + + // Compute key-value product + sycl::float4 kv4 = k4 * _v; + + // Accumulate weighted sum + y += sycl::dot(r4, tf4 * kv4 + s4); + + // Update state + s4 = s4 * td4 + kv4; + + // Store updated state + state[j] = s4.x(); + state[j+1] = s4.y(); + state[j+2] = s4.z(); + state[j+3] = s4.w(); + } + + dst[t] = y; + } + + // Save final state + #pragma unroll + for (int i = 0; i < head_size; i++) { + dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i]; + } +} + +void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst) { + + const float* k_d = (const float*)dst->src[0]->data; + const float* v_d = (const float*)dst->src[1]->data; + const float* r_d = (const float*)dst->src[2]->data; + const float* tf_d = (const float*)dst->src[3]->data; + const float* td_d = (const float*)dst->src[4]->data; + const float* s_d = (const float*)dst->src[5]->data; + float* dst_d = (float*)dst->data; + + const int64_t B = dst->src[5]->ne[1]; + const int64_t T = dst->src[0]->ne[3]; + const int64_t C = dst->ne[0]; + const int64_t H = dst->src[0]->ne[2]; + + GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32); + GGML_ASSERT(C % H == 0); + GGML_ASSERT(C / H == WKV_BLOCK_SIZE); // The current sycl kernel is designed for RWKV6, HEAD_SIZE == 64 + + dpct::queue_ptr stream = ctx.stream(); + + // Calculate execution configuration + const size_t shared_mem_size = WKV_BLOCK_SIZE * 4 * sizeof(float); // For k, r, tf, td + sycl::range<3> block_dims(1, 1, C / H); + sycl::range<3> grid_dims(1, 1, B * H); + + // Submit kernel + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rwkv_wkv_f32_kernel( + B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d, + item_ct1, shared_mem_acc.get_pointer() + ); + }); + }); +} diff --git a/ggml/src/ggml-sycl/wkv6.hpp b/ggml/src/ggml-sycl/wkv6.hpp new file mode 100644 index 0000000000..ddfa3377b4 --- /dev/null +++ b/ggml/src/ggml-sycl/wkv6.hpp @@ -0,0 +1,10 @@ +#ifndef GGML_SYCL_WKV6_HPP +#define GGML_SYCL_WKV6_HPP + +#include "common.hpp" + +void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor * dst); + + +#endif // GGML_SYCL_WKV6_HPP diff --git a/ggml/src/ggml-threading.cpp b/ggml/src/ggml-threading.cpp new file mode 100644 index 0000000000..25a19eedb9 --- /dev/null +++ b/ggml/src/ggml-threading.cpp @@ -0,0 +1,12 @@ +#include "ggml-threading.h" +#include + +std::mutex ggml_critical_section_mutex; + +void ggml_critical_section_start() { + ggml_critical_section_mutex.lock(); +} + +void ggml_critical_section_end(void) { + ggml_critical_section_mutex.unlock(); +} diff --git a/ggml/src/ggml-threading.h b/ggml/src/ggml-threading.h new file mode 100644 index 0000000000..ce975d880a --- /dev/null +++ b/ggml/src/ggml-threading.h @@ -0,0 +1,12 @@ +#pragma once + +#ifdef __cplusplus +extern "C" { +#endif + +void ggml_critical_section_start(void); +void ggml_critical_section_end(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt new file mode 100644 index 0000000000..1e85dd15b7 --- /dev/null +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -0,0 +1,78 @@ +find_package(Vulkan COMPONENTS glslc REQUIRED) + +if (Vulkan_FOUND) + message(STATUS "Vulkan found") + + add_library(ggml-vulkan + ggml-vulkan.cpp + ../../include/ggml-vulkan.h + ) + + target_link_libraries(ggml-vulkan PRIVATE ggml-base Vulkan::Vulkan) + target_include_directories(ggml-vulkan PRIVATE . .. ${CMAKE_CURRENT_BINARY_DIR}) + + # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build + # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) + endif() + + if (GGML_VULKAN_CHECK_RESULTS) + add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) + endif() + + if (GGML_VULKAN_DEBUG) + add_compile_definitions(GGML_VULKAN_DEBUG) + endif() + + if (GGML_VULKAN_MEMORY_DEBUG) + add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) + endif() + + if (GGML_VULKAN_SHADER_DEBUG_INFO) + add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) + endif() + + if (GGML_VULKAN_PERF) + add_compile_definitions(GGML_VULKAN_PERF) + endif() + + if (GGML_VULKAN_VALIDATE) + add_compile_definitions(GGML_VULKAN_VALIDATE) + endif() + + if (GGML_VULKAN_RUN_TESTS) + add_compile_definitions(GGML_VULKAN_RUN_TESTS) + endif() + + add_subdirectory(vulkan-shaders) + + set (_ggml_vk_genshaders_cmd vulkan-shaders-gen) + set (_ggml_vk_header ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp) + set (_ggml_vk_source ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp) + set (_ggml_vk_input_dir ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders) + set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv) + + file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp") + + add_custom_command( + OUTPUT ${_ggml_vk_header} + ${_ggml_vk_source} + + COMMAND ${_ggml_vk_genshaders_cmd} + --glslc ${Vulkan_GLSLC_EXECUTABLE} + --input-dir ${_ggml_vk_input_dir} + --output-dir ${_ggml_vk_output_dir} + --target-hpp ${_ggml_vk_header} + --target-cpp ${_ggml_vk_source} + --no-clean + + DEPENDS ${_ggml_vk_shader_deps} + COMMENT "Generate vulkan shaders" + ) + + target_sources(ggml-vulkan PRIVATE ${_ggml_vk_source} ${_ggml_vk_header}) + +else() + message(WARNING "Vulkan not found") +endif() diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp similarity index 75% rename from ggml/src/ggml-vulkan.cpp rename to ggml/src/ggml-vulkan/ggml-vulkan.cpp index 374c6ecd7a..ca71da2f7b 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -106,6 +106,15 @@ struct vk_matmul_pipeline_struct { typedef std::shared_ptr vk_matmul_pipeline; +struct vk_matmul_pipeline2 { + vk_matmul_pipeline2() { + f16acc = std::make_shared(); + f32acc = std::make_shared(); + } + vk_matmul_pipeline f32acc; + vk_matmul_pipeline f16acc; +}; + struct vk_device_struct; typedef std::shared_ptr vk_device; typedef std::weak_ptr vk_device_ref; @@ -149,6 +158,7 @@ struct vk_device_struct { std::string name; uint64_t max_memory_allocation_size; bool fp16; + bool pipeline_robustness; vk::Device device; uint32_t vendor_id; vk_queue compute_queue; @@ -161,11 +171,11 @@ struct vk_device_struct { vk_matmul_pipeline pipeline_matmul_f32; vk_matmul_pipeline pipeline_matmul_f32_f16; - vk_matmul_pipeline pipeline_matmul_f16; - vk_matmul_pipeline pipeline_matmul_f16_f32; + vk_matmul_pipeline2 pipeline_matmul_f16; + vk_matmul_pipeline2 pipeline_matmul_f16_f32; vk_pipeline pipeline_matmul_split_k_reduce; - vk_matmul_pipeline pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; vk_matmul_pipeline pipeline_matmul_id_f32; vk_matmul_pipeline pipeline_matmul_id_f16; @@ -183,9 +193,10 @@ struct vk_device_struct { vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT]; vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_acc_f32; - vk_pipeline pipeline_add_f32, pipeline_add_f16_f32_f16; - vk_pipeline pipeline_mul_f32; - vk_pipeline pipeline_div_f32; + vk_pipeline pipeline_add_f32, pipeline_add_f32_norepeat; + vk_pipeline pipeline_add_f16_f32_f16, pipeline_add_f16_f32_f16_norepeat; + vk_pipeline pipeline_mul_f32, pipeline_mul_f32_norepeat; + vk_pipeline pipeline_div_f32, pipeline_div_f32_norepeat; vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32; vk_pipeline pipeline_upscale_f32; vk_pipeline pipeline_scale_f32; @@ -196,6 +207,7 @@ struct vk_device_struct { vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_repeat_f32; vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16; vk_pipeline pipeline_norm_f32; vk_pipeline pipeline_group_norm_f32; vk_pipeline pipeline_rms_norm_f32; @@ -207,12 +219,14 @@ struct vk_device_struct { vk_pipeline pipeline_tanh_f32; vk_pipeline pipeline_diag_mask_inf_f32; vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; + vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; vk_pipeline pipeline_argsort_f32; vk_pipeline pipeline_sum_rows_f32; vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16; vk_pipeline pipeline_timestep_embedding_f32; + vk_pipeline pipeline_pool2d_f32; std::unordered_map pipelines; std::unordered_map pipeline_descriptor_set_requirements; @@ -376,6 +390,7 @@ struct vk_op_soft_max_push_constants { float m0; float m1; uint32_t n_head_log2; + uint32_t nrows_x; }; struct vk_op_argsort_push_constants { @@ -403,6 +418,17 @@ struct vk_op_timestep_embedding_push_constants { uint32_t max_period; }; +struct vk_op_pool2d_push_constants { + uint32_t IW; uint32_t IH; + uint32_t OW; uint32_t OH; + uint32_t OC; + uint32_t pelements; + uint32_t op; + int32_t k0; int32_t k1; + int32_t s0; int32_t s1; + int32_t p0; int32_t p1; +}; + // Allow pre-recording command buffers struct vk_staging_memcpy { vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} @@ -629,7 +655,7 @@ static uint32_t compile_count = 0; static std::mutex compile_count_mutex; static std::condition_variable compile_count_cond; -static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align) { +static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align, bool disable_robustness) { VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")"); GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT @@ -699,6 +725,15 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin vk::PipelineCreateFlags(), pipeline_shader_create_info, pipeline->layout); + + vk::PipelineRobustnessCreateInfoEXT rci; + + if (device->pipeline_robustness && disable_robustness) { + rci.storageBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + rci.uniformBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + compute_pipeline_create_info.setPNext(&rci); + } + pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; { @@ -710,6 +745,12 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin std::lock_guard guard(compile_count_mutex); assert(compile_count > 0); compile_count--; + + // "Progress bar" for shader compiles + static uint32_t total_compile_count = 0; + if ((total_compile_count++ % 10) == 0) { + std::cerr << "."; + } } compile_count_cond.notify_all(); } @@ -1035,7 +1076,6 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor return buf; } - buf->size = size; vk::BufferCreateInfo buffer_create_info{ vk::BufferCreateFlags(), size, @@ -1063,7 +1103,6 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor if (memory_type_index == UINT32_MAX) { device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } @@ -1080,13 +1119,11 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor } catch (const vk::SystemError& e) { device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw e; } } else { // Out of Host/Device memory, clean up buffer device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw e; } } @@ -1099,6 +1136,7 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); buf->device = device; + buf->size = size; #ifdef GGML_VULKAN_MEMORY_DEBUG device->memory_logger->log_allocation(buf, size); @@ -1191,38 +1229,31 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events static void ggml_vk_load_shaders(vk_device& device) { VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")"); + std::cerr << "ggml_vulkan: Compiling shaders"; + // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_s = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + std::vector l_warptile, m_warptile, s_warptile, l_warptile_mmq, m_warptile_mmq, s_warptile_mmq; + std::array l_wg_denoms, m_wg_denoms, s_wg_denoms; + uint32_t l_align, m_align, s_align; - std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_mmq_s = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; + l_warptile = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + m_warptile = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + s_warptile = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; - std::array l_wg_denoms = {128, 128, 1 }; - std::array m_wg_denoms = { 64, 64, 1 }; - std::array s_wg_denoms = { 32, 32, 1 }; + l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + s_warptile_mmq = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; - uint32_t l_align = 128; - uint32_t m_align = 64; - uint32_t s_align = 32; + l_wg_denoms = {128, 128, 1 }; + m_wg_denoms = { 64, 64, 1 }; + s_wg_denoms = { 32, 32, 1 }; + + l_align = 128; + m_align = 64; + s_align = 32; device->pipeline_matmul_f32 = std::make_shared(); device->pipeline_matmul_f32_f16 = std::make_shared(); - device->pipeline_matmul_f16_f32 = std::make_shared(); - device->pipeline_matmul_f16 = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL] = std::make_shared(); device->pipeline_matmul_id_f32 = std::make_shared(); device->pipeline_matmul_id_f16_f32 = std::make_shared(); @@ -1240,7 +1271,7 @@ static void ggml_vk_load_shaders(vk_device& device) { device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared(); std::vector> compiles; - auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { + auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, const std::vector& specialization_constants, uint32_t align, bool disable_robustness = false) { { // wait until fewer than N compiles are in progress uint32_t N = std::max(1u, std::thread::hardware_concurrency()); @@ -1250,459 +1281,144 @@ static void ggml_vk_load_shaders(vk_device& device) { } compile_count++; } - compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align)); + compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness)); }; if (device->fp16) { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \ - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); +#undef CREATE_MM } else { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \ - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); +#undef CREATE_MM } // mul mat vec - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + // computing two rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0. + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); // dequant shaders ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -1750,13 +1466,21 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f32_norepeat, "add_f32_norepeat", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16_norepeat, "add_f16_f32_f16_norepeat", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_mul_f32_norepeat, "mul_f32_norepeat", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_div_f32_norepeat, "div_f32_norepeat", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); @@ -1785,8 +1509,10 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -1803,9 +1529,12 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1); + for (auto &c : compiles) { c.wait(); } + std::cerr << "Done!" << std::endl; } static vk_device ggml_vk_get_device(size_t idx) { @@ -1872,12 +1601,15 @@ static vk_device ggml_vk_get_device(size_t idx) { bool fp16_storage = false; bool fp16_compute = false; + bool pipeline_robustness = false; for (const auto& properties : ext_props) { if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { fp16_storage = true; } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { fp16_compute = true; + } else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) { + pipeline_robustness = true; } } @@ -1923,10 +1655,22 @@ static vk_device ggml_vk_get_device(size_t idx) { vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; vk11_features.pNext = &vk12_features; + VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features; + pl_robustness_features.pNext = nullptr; + pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT; + pl_robustness_features.pipelineRobustness = VK_FALSE; + + if (pipeline_robustness) { + vk12_features.pNext = &pl_robustness_features; + device_extensions.push_back("VK_EXT_pipeline_robustness"); + } + vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); device->fp16 = device->fp16 && vk12_features.shaderFloat16; + device->pipeline_robustness = pl_robustness_features.pipelineRobustness; + if (!vk11_features.storageBuffer16BitAccess) { std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; throw std::runtime_error("Unsupported device"); @@ -1941,7 +1685,7 @@ static vk_device ggml_vk_get_device(size_t idx) { if (device->fp16) { device_extensions.push_back("VK_KHR_shader_float16_int8"); } - device->name = device->properties.deviceName.data(); + device->name = GGML_VK_NAME + std::to_string(idx); device_create_info = { vk::DeviceCreateFlags(), @@ -1968,7 +1712,7 @@ static vk_device ggml_vk_get_device(size_t idx) { device->buffer_type = { /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx), /* .context = */ new ggml_backend_vk_buffer_type_context{ device->name, device }, }; @@ -2049,10 +1793,11 @@ static void ggml_vk_print_gpu_info(size_t idx) { fp16 = fp16 && vk12_features.shaderFloat16; std::string device_name = props2.properties.deviceName.data(); - std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %d | warp size: %zu\n", + idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16, subgroup_size); if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { - std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want.\n"); } } @@ -2107,8 +1852,7 @@ void ggml_vk_instance_init() { }; validation_features.setPNext(nullptr); instance_create_info.setPNext(&validation_features); - - std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n"); } vk_instance.instance = vk::createInstance(instance_create_info); @@ -2222,8 +1966,7 @@ void ggml_vk_instance_init() { vk_instance.device_indices.push_back(0); } } - - std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Found %zu Vulkan devices:\n", vk_instance.device_indices.size()); for (size_t i = 0; i < vk_instance.device_indices.size(); i++) { ggml_vk_print_gpu_info(i); @@ -2288,10 +2031,10 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return ctx->device->pipeline_matmul_f32_f16; } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_f16_f32; + return ctx->device->pipeline_matmul_f16_f32.f32acc; } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return ctx->device->pipeline_matmul_f16; + return ctx->device->pipeline_matmul_f16.f32acc; } if (src1_type != GGML_TYPE_F32) { @@ -2315,7 +2058,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return nullptr; } - return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; + return ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc; } static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { @@ -3050,18 +2793,34 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { - if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return ctx->device->pipeline_cpy_f32_f32; +static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to) { + + // Choose "contiguous copy" shader if src/dst are contiguous + bool contig = ggml_is_contiguous(src) && (!dst || ggml_is_contiguous(dst)); + + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f32; + } else { + return ctx->device->pipeline_cpy_f32_f32; + } } - if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return ctx->device->pipeline_cpy_f32_f16; + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f16; + } else { + return ctx->device->pipeline_cpy_f32_f16; + } } - if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return ctx->device->pipeline_cpy_f16_f16; + if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f16; + } else { + return ctx->device->pipeline_cpy_f16_f16; + } } - std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; + std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl; GGML_ABORT("fatal error"); } @@ -3071,6 +2830,15 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& const int tensor_type_size = ggml_type_size(tensor->type); const uint32_t ne = ggml_nelements(tensor); + std::array elements; + + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } const vk_op_unary_push_constants pc = { (uint32_t)ne, @@ -3080,7 +2848,7 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& 0.0f, 0.0f, }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, elements); } static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { @@ -3136,7 +2904,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub const bool qx_needs_dequant = mmp == nullptr || x_non_contig; const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - if (mmp == nullptr) { + if (qx_needs_dequant) { // Fall back to dequant + f16 mulmat mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16); } @@ -3165,12 +2933,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3350,10 +3118,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& vk_pipeline to_fp16_vk_0 = nullptr; vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3447,7 +3215,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& if (ne01 > max_groups_x) { groups_z = 64; - groups_x /= groups_z; + groups_x = CEIL_DIV(groups_x, groups_z); } // compute @@ -3619,9 +3387,19 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); - if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) { + if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1 && + // detect 0213 permutation, and batch size of 1 + src0->nb[0] <= src0->nb[2] && + src0->nb[2] <= src0->nb[1] && + src0->nb[1] <= src0->nb[3] && + src1->nb[0] <= src1->nb[2] && + src1->nb[2] <= src1->nb[1] && + src1->nb[1] <= src1->nb[3] && + src0->ne[3] == 1 && + src1->ne[3] == 1) { ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun); - } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) { + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 && + !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) { ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun); } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); @@ -3697,7 +3475,7 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& const bool qx_needs_dequant = mmp == nullptr || x_non_contig; const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - if (mmp == nullptr) { + if (qx_needs_dequant) { GGML_ABORT("fatal error"); } @@ -3724,12 +3502,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3917,10 +3695,10 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte vk_pipeline to_fp16_vk_0 = nullptr; vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -4014,7 +3792,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte if (ne01 > max_groups_x) { groups_z = 64; - groups_x /= groups_z; + groups_x = CEIL_DIV(groups_x, groups_z); } // compute @@ -4057,20 +3835,20 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_add_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f32_norepeat : ctx->device->pipeline_add_f32; } if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_add_f16_f32_f16; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f16_f32_f16_norepeat : ctx->device->pipeline_add_f16_f32_f16; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_mul_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_f32_norepeat : ctx->device->pipeline_mul_f32; } return nullptr; case GGML_OP_DIV: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_div_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_f32_norepeat : ctx->device->pipeline_div_f32; } return nullptr; case GGML_OP_CONCAT: @@ -4127,7 +3905,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); + return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_norm_f32; @@ -4183,10 +3961,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32; + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32; } if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32_f16; + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16; } return nullptr; case GGML_OP_ROPE: @@ -4234,6 +4012,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_timestep_embedding_f32; } return nullptr; + case GGML_OP_POOL_2D: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_pool2d_f32; + } + return nullptr; case GGML_OP_LEAKY_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_leaky_relu_f32; @@ -4255,7 +4038,6 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_DIV: case GGML_OP_CONCAT: case GGML_OP_UPSCALE: - case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SIN: case GGML_OP_COS: @@ -4454,7 +4236,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint32_t OH = is_2D ? dst->ne[2] : 1; const uint32_t OW = dst->ne[1]; - const uint32_t batch = src1->ne[3]; + const uint32_t batch = src1->ne[is_2D ? 3 : 2]; elements = { OW * KW * KH, OH, batch * IC }; } break; @@ -4464,6 +4246,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co uint32_t half_ceil = (dim + 1) / 2; elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; } break; + case GGML_OP_POOL_2D: + { + const uint32_t N = dst->ne[3]; + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + elements = { N * OC * OH * OW, 1, 1}; + } break; case GGML_OP_ADD: case GGML_OP_DIV: case GGML_OP_MUL: @@ -4820,6 +4610,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, scale, max_bias, m0, m1, n_head_log2, + nrows_x, }, dryrun); } @@ -4891,7 +4682,7 @@ static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint32_t OW = dst->ne[1]; const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const uint32_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 + const uint32_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 const uint32_t pelements = OW * KW * KH; @@ -4914,6 +4705,34 @@ static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context }, dryrun); } +static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + uint32_t op = static_cast(dst->op_params[0]); + const int32_t k1 = dst->op_params[1]; + const int32_t k0 = dst->op_params[2]; + const int32_t s1 = dst->op_params[3]; + const int32_t s0 = dst->op_params[4]; + const int32_t p1 = dst->op_params[5]; + const int32_t p0 = dst->op_params[6]; + + const uint32_t IH = src0->ne[1]; + const uint32_t IW = src0->ne[0]; + + const uint32_t N = dst->ne[3]; + + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + + const uint32_t parallel_elements = N * OC * OH * OW; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_POOL_2D, { + IW, IH, OW, OH, OC, + parallel_elements, + op, + k0, k1, s0, s1, p0, p1, + }, dryrun); +} + static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const float * op_params = (const float *)dst->op_params; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); @@ -4970,10 +4789,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_s; shname = "F32_F16_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_s; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_s; + p = ctx->device->pipeline_matmul_f16.f32acc->a_s; shname = "F16_ALIGNED_S"; } else { GGML_ABORT("fatal error"); @@ -4986,10 +4805,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_m; shname = "F32_F16_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_m; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_m; + p = ctx->device->pipeline_matmul_f16.f32acc->a_m; shname = "F16_ALIGNED_M"; } else { GGML_ABORT("fatal error"); @@ -5002,10 +4821,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_l; shname = "F32_F16_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_l; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_l; + p = ctx->device->pipeline_matmul_f16.f32acc->a_l; shname = "F16_ALIGNED_L"; } else { GGML_ABORT("fatal error"); @@ -5025,10 +4844,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->s; shname = "F32_F16_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->s; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->s; + p = ctx->device->pipeline_matmul_f16.f32acc->s; shname = "F16_S"; } } else if (shader_size == 1) { @@ -5039,10 +4858,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->m; shname = "F32_F16_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->m; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->m; + p = ctx->device->pipeline_matmul_f16.f32acc->m; shname = "F16_M"; } } else if (shader_size == 2) { @@ -5053,10 +4872,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->l; shname = "F32_F16_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->l; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->l; + p = ctx->device->pipeline_matmul_f16.f32acc->l; shname = "F16_L"; } } @@ -5385,13 +5204,13 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, vk_pipeline p; std::string shname; if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_s; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_m; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_l; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; } else { GGML_ASSERT(0); @@ -5401,13 +5220,13 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, if (k != kpad) { if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->s; shname = std::string(ggml_type_name(quant)) + "_S"; } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->m; shname = std::string(ggml_type_name(quant)) + "_M"; } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->l; shname = std::string(ggml_type_name(quant)) + "_L"; } else { GGML_ASSERT(0); @@ -5792,6 +5611,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: break; default: @@ -5927,6 +5747,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_TIMESTEP_EMBEDDING: ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun); + break; + case GGML_OP_POOL_2D: + ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun); + break; case GGML_OP_LEAKY_RELU: ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); @@ -6018,6 +5842,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: case GGML_OP_REPEAT: buf = tensor->buffer; @@ -6186,13 +6011,8 @@ static void ggml_vk_get_device_description(int device, char * description, size_ // device backend -static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_vk_buffer_get_name; + return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name; } static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { @@ -6256,7 +6076,6 @@ static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t v } static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { - /* .get_name = */ ggml_backend_vk_buffer_get_name, /* .free_buffer = */ ggml_backend_vk_buffer_free_buffer, /* .get_base = */ ggml_backend_vk_buffer_get_base, /* .init_tensor = */ ggml_backend_vk_buffer_init_tensor, @@ -6352,7 +6171,6 @@ static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_ ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_vk_host_buffer_name; buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer; return buffer; @@ -6378,7 +6196,7 @@ ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0), /* .context = */ nullptr, }; @@ -6581,9 +6399,132 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg UNUSED(backend); } -static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) { - // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context; +// TODO: enable async and synchronize +static ggml_backend_i ggml_backend_vk_interface = { + /* .get_name = */ ggml_backend_vk_name, + /* .free = */ ggml_backend_vk_free, + /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, + /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, + /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_vk_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; +static ggml_guid_t ggml_backend_vk_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + return &guid; +} + +ggml_backend_t ggml_backend_vk_init(size_t dev_num) { + VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); + + ggml_backend_vk_context * ctx = new ggml_backend_vk_context; + ggml_vk_init(ctx, dev_num); + + ggml_backend_t vk_backend = new ggml_backend { + /* .guid = */ ggml_backend_vk_guid(), + /* .interface = */ ggml_backend_vk_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num), + /* .context = */ ctx, + }; + + return vk_backend; +} + +bool ggml_backend_is_vk(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); +} + +int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + int dev_idx = vk_instance.device_indices[device]; + ggml_vk_get_device_description(dev_idx, description, description_size); +} + +void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + + vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); + + for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { + if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { + *total = heap.size; + *free = heap.size; + break; + } + } +} + +////////////////////////// + +struct ggml_backend_vk_device_context { + size_t device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context; + ggml_backend_vk_get_device_memory(ctx->device, free, total); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) { + UNUSED(dev); + return ggml_backend_vk_host_buffer_type(); +} + +static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) { + UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_vk_device_get_name(dev); + props->description = ggml_backend_vk_device_get_description(dev); + props->type = ggml_backend_vk_device_get_type(dev); + ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ false, + /* .host_buffer = */ true, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) { + UNUSED(params); + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_init(ctx->device); +} + +static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -6630,6 +6571,11 @@ static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tenso if (a->ne[3] != b->ne[3]) { return false; } + if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) || + !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) { + return false; + } + return true; } break; case GGML_OP_GET_ROWS: @@ -6695,103 +6641,108 @@ static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tenso case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: return true; default: return false; } - UNUSED(backend); + UNUSED(dev); } -static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) { +static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { + return false; + } + + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + + return buft_ctx->device->idx == ctx->device; +} + +static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); - UNUSED(backend); + UNUSED(dev); } -static bool ggml_backend_vk_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { - return false; - } - - ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - return buft_ctx->device == ctx->device; -} - -// TODO: enable async and synchronize -static ggml_backend_i ggml_backend_vk_interface = { - /* .get_name = */ ggml_backend_vk_name, - /* .free = */ ggml_backend_vk_free, - /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type, - /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, - /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, - /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, - /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_vk_graph_compute, - /* .supports_op = */ ggml_backend_vk_supports_op, - /* .supports_buft = */ ggml_backend_vk_supports_buft, - /* .offload_op = */ ggml_backend_vk_offload_op, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, +static const struct ggml_backend_device_i ggml_backend_vk_device_i = { + /* .get_name = */ ggml_backend_vk_device_get_name, + /* .get_description = */ ggml_backend_vk_device_get_description, + /* .get_memory = */ ggml_backend_vk_device_get_memory, + /* .get_type = */ ggml_backend_vk_device_get_type, + /* .get_props = */ ggml_backend_vk_device_get_props, + /* .init_backend = */ ggml_backend_vk_device_init, + /* .get_buffer_type = */ ggml_backend_vk_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_vk_device_supports_op, + /* .supports_buft = */ ggml_backend_vk_device_supports_buft, + /* .offload_op = */ ggml_backend_vk_device_offload_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, }; -static ggml_guid_t ggml_backend_vk_guid() { - static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; - return &guid; +static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) { + UNUSED(reg); + return GGML_VK_NAME; } -ggml_backend_t ggml_backend_vk_init(size_t dev_num) { - VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); - - ggml_backend_vk_context * ctx = new ggml_backend_vk_context; - ggml_vk_init(ctx, dev_num); - - ggml_backend_t vk_backend = new ggml_backend { - /* .guid = */ ggml_backend_vk_guid(), - /* .interface = */ ggml_backend_vk_interface, - /* .device = */ nullptr, - /* .context = */ ctx, - }; - - return vk_backend; +static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) { + UNUSED(reg); + return ggml_backend_vk_get_device_count(); } -bool ggml_backend_is_vk(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); -} +static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) { + static std::vector devices; -int ggml_backend_vk_get_device_count() { - return ggml_vk_get_device_count(); -} + static bool initialized = false; -void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { - ggml_vk_get_device_description(device, description, description_size); -} - -void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { - GGML_ASSERT(device < (int) vk_instance.device_indices.size()); - - vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; - - vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); - - for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { - if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { - *total = heap.size; - *free = heap.size; - break; + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) { + ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context; + char desc[256]; + ggml_backend_vk_get_device_description(i, desc, sizeof(desc)); + ctx->device = i; + ctx->name = GGML_VK_NAME + std::to_string(i); + ctx->description = desc; + devices.push_back(new ggml_backend_device { + /* .iface = */ ggml_backend_vk_device_i, + /* .reg = */ reg, + /* .context = */ ctx, + }); + } + initialized = true; } } + + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = { + /* .get_name = */ ggml_backend_vk_reg_get_name, + /* .get_device_count = */ ggml_backend_vk_reg_get_device_count, + /* .get_device = */ ggml_backend_vk_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_vk_reg() { + static ggml_backend_reg reg = { + /* .iface = */ ggml_backend_vk_reg_i, + /* .context = */ nullptr, + }; + + return ® } // Extension availability @@ -7204,6 +7155,16 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { const int32_t dim = tensor->op_params[0]; const int32_t max_period = tensor->op_params[1]; tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period); + } else if (tensor->op == GGML_OP_POOL_2D) { + enum ggml_op_pool op = static_cast(dst->op_params[0]); + const int32_t k0 = tensor->op_params[1]; + const int32_t k1 = tensor->op_params[2]; + const int32_t s0 = tensor->op_params[3]; + const int32_t s1 = tensor->op_params[4]; + const int32_t p0 = tensor->op_params[5]; + const int32_t p1 = tensor->op_params[6]; + + tensor_clone = ggml_pool_2d(ggml_ctx, src0_clone, op, k0, k1, s0, s1, p0, p1); } else if (tensor->op == GGML_OP_LEAKY_RELU) { const float * op_params = (const float *)tensor->op_params; tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false); diff --git a/ggml/src/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt similarity index 100% rename from ggml/src/vulkan-shaders/CMakeLists.txt rename to ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt diff --git a/ggml/src/vulkan-shaders/acc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp similarity index 51% rename from ggml/src/vulkan-shaders/acc.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/acc.comp index 4c8739efee..4f5a04e71c 100644 --- a/ggml/src/vulkan-shaders/acc.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.x; if (idx >= p.ne) { @@ -15,10 +17,13 @@ void main() { const uint oy = (src1_i - (oz * p.nb02)) / p.nb01; const uint ox = src1_i % p.nb01; + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); } else { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)])); + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)])); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp new file mode 100644 index 0000000000..da61b76dfc --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp @@ -0,0 +1,29 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/argsort.comp b/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp similarity index 100% rename from ggml/src/vulkan-shaders/argsort.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp diff --git a/ggml/src/vulkan-shaders/clamp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp similarity index 83% rename from ggml/src/vulkan-shaders/clamp.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp index 7071302a4b..ae8fa8753d 100644 --- a/ggml/src/vulkan-shaders/clamp.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/concat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp similarity index 95% rename from ggml/src/vulkan-shaders/concat.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/concat.comp index c23b6eb1b0..683f9ac3c1 100644 --- a/ggml/src/vulkan-shaders/concat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; const int dim = p.param3; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp new file mode 100644 index 0000000000..9acbdd3d2e --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "types.comp" +#include "generic_unary_head.comp" + +#extension GL_EXT_control_flow_attributes : require + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + // fast path for when all four iterations are in-bounds + if (idx + (num_iter-1)*num_threads < p.ne) { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); +#else + data_d[p.d_offset + idx] = data_a[idx]; +#endif + idx += num_threads; + } + } else { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); +#else + data_d[p.d_offset + idx] = data_a[idx]; +#endif + idx += num_threads; + } + } +} diff --git a/ggml/src/vulkan-shaders/copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp similarity index 83% rename from ggml/src/vulkan-shaders/copy.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/copy.comp index c26917c0f9..2775068f9a 100644 --- a/ggml/src/vulkan-shaders/copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/cos.comp b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp similarity index 80% rename from ggml/src/vulkan-shaders/cos.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/cos.comp index f9a858cbf1..fbd9d272c3 100644 --- a/ggml/src/vulkan-shaders/cos.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/dequant_f32.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_f32.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp new file mode 100644 index 0000000000..5fc1ba4ad3 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp @@ -0,0 +1,116 @@ +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#endif + +#include "types.comp" + +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +#if defined(DATA_A_F32) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +#endif + +#if defined(DATA_A_F16) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +#endif + +#if defined(DATA_A_Q4_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) - 8.0f) * d; +} +#endif + +#if defined(DATA_A_Q4_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(vui & 0xF, vui >> 4) * d + m; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const float m = float(data_a_packed16[a_offset + ib].m); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) * d + m; +} +#endif + +#if defined(DATA_A_Q5_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint uint_qh = uint(data_a_packed16[a_offset + ib].qh[1]) << 16 | data_a_packed16[a_offset + ib].qh[0]; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) - 16.0f) * d; +} +#endif + +#if defined(DATA_A_Q5_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint uint_qh = data_a[a_offset + ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const float m = float(data_a_packed16[a_offset + ib].m); + const uint uint_qh = data_a_packed16[a_offset + ib].qh; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * d + m; +} +#endif + +#if defined(DATA_A_Q8_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + uint32_t v0 = data_a_packed16[a_offset + ib].qs[iqs/2]; + uint32_t v1 = data_a_packed16[a_offset + ib].qs[iqs/2 + 1]; + return vec4(int8_t(v0 & 0xFF), int8_t((v0 >> 8) & 0xFF), int8_t(v1 & 0xFF), int8_t((v1 >> 8) & 0xFF)) * d; +} +#endif + +#if defined(DATA_A_IQ4_NL) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[(vui >> 12) & 0xF]) * d; +} +#endif diff --git a/ggml/src/vulkan-shaders/dequant_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.comp diff --git a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp similarity index 97% rename from ggml/src/vulkan-shaders/dequant_iq4_nl.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp index 34ef3da30b..8de14fc03f 100644 --- a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp @@ -10,6 +10,8 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; void main() { const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + init_iq4nl_shmem(); + const uint tid = gl_LocalInvocationID.x % 64; const uint il = tid/32; const uint ir = tid%32; diff --git a/ggml/src/vulkan-shaders/dequant_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q2_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q3_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_1.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_1.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q6_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q8_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q8_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp diff --git a/ggml/src/vulkan-shaders/diag_mask_inf.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp similarity index 100% rename from ggml/src/vulkan-shaders/diag_mask_inf.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp new file mode 100644 index 0000000000..e581905b3f --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/gelu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gelu.comp similarity index 100% rename from ggml/src/vulkan-shaders/gelu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/gelu.comp diff --git a/ggml/src/vulkan-shaders/gelu_quick.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gelu_quick.comp similarity index 100% rename from ggml/src/vulkan-shaders/gelu_quick.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/gelu_quick.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp new file mode 100644 index 0000000000..a6555fa270 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp @@ -0,0 +1,60 @@ +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint d_offset; + float param1; float param2; int param3; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +// true if src0/src1 are the same shape and the indices can be reused without additional modulus +layout(constant_id = 0) const bool norepeat = false; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +// mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1 +uint fastmod(uint a, uint b) { + if ((b & (b-1)) == 0) { + return a & (b-1); + } + return a % b; +} + +uint fastdiv(uint a, uint b) { + return (a < b) ? 0 : (a / b); +} + +void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03) { + i03 = fastdiv(idx, (p.ne02*p.ne01*p.ne00)); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + i02 = fastdiv((idx - i03_offset), (p.ne01*p.ne00)); + const uint i02_offset = i02*p.ne01*p.ne00; + i01 = (idx - i03_offset - i02_offset) / p.ne00; + i00 = idx - i03_offset - i02_offset - i01*p.ne00; +} + +uint src0_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint src1_idx(uint i00, uint i01, uint i02, uint i03) { + if (norepeat) { + return i03*p.nb13 + i02*p.nb12 + i01*p.nb11 + i00*p.nb10; + } else { + return fastmod(i03, p.ne13)*p.nb13 + fastmod(i02, p.ne12)*p.nb12 + fastmod(i01, p.ne11)*p.nb11 + fastmod(i00, p.ne10)*p.nb10; + } +} + +uint dst_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb23 + i02*p.nb22 + i01*p.nb21 + i00*p.nb20; +} diff --git a/ggml/src/vulkan-shaders/generic_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/generic_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/generic_head.comp diff --git a/ggml/src/vulkan-shaders/generic_unary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp similarity index 95% rename from ggml/src/vulkan-shaders/generic_unary_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp index eacdefc7d8..4e1fa3af3a 100644 --- a/ggml/src/vulkan-shaders/generic_unary_head.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp @@ -1,4 +1,5 @@ #extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require layout (push_constant) uniform parameter { @@ -9,8 +10,6 @@ layout (push_constant) uniform parameter float param1; float param2; } p; -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; diff --git a/ggml/src/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp similarity index 91% rename from ggml/src/vulkan-shaders/get_rows.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index e9ff22efa9..a7b81e52ce 100644 --- a/ggml/src/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint i00 = gl_GlobalInvocationID.x; const uint i10 = gl_GlobalInvocationID.y; diff --git a/ggml/src/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp similarity index 88% rename from ggml/src/vulkan-shaders/get_rows_quant.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp index 53a9a96f23..7f608315b6 100644 --- a/ggml/src/vulkan-shaders/get_rows_quant.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp @@ -4,12 +4,18 @@ #include "generic_binary_head.comp" #include "dequant_funcs.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint i00 = (gl_GlobalInvocationID.x)*2; const uint i10 = gl_GlobalInvocationID.y; const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + if (i00 >= p.ne00) { return; } diff --git a/ggml/src/vulkan-shaders/group_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/group_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp diff --git a/ggml/src/vulkan-shaders/im2col.comp b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp similarity index 100% rename from ggml/src/vulkan-shaders/im2col.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp diff --git a/ggml/src/vulkan-shaders/leaky_relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp similarity index 100% rename from ggml/src/vulkan-shaders/leaky_relu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp new file mode 100644 index 0000000000..5ce57cbcfc --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp similarity index 100% rename from ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp new file mode 100644 index 0000000000..2d5b8e4661 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp @@ -0,0 +1,177 @@ +#version 450 + +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#endif +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; + +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#define K_PER_ITER 8 +#else +#define K_PER_ITER 2 +#endif + + +uint a_offset, b_offset, d_offset, y_offset; + +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; + +void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) +{ + const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index + +#if K_PER_ITER == 8 +#if QUANT_R == 2 + B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4]; + B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4]; + FLOAT_TYPE b0 = FLOAT_TYPE(bv02.x); + FLOAT_TYPE b1 = FLOAT_TYPE(bv13.x); + FLOAT_TYPE b2 = FLOAT_TYPE(bv02.y); + FLOAT_TYPE b3 = FLOAT_TYPE(bv13.y); + FLOAT_TYPE b4 = FLOAT_TYPE(bv02.z); + FLOAT_TYPE b5 = FLOAT_TYPE(bv13.z); + FLOAT_TYPE b6 = FLOAT_TYPE(bv02.w); + FLOAT_TYPE b7 = FLOAT_TYPE(bv13.w); +#else + B_TYPE_VEC4 bv0 = data_b_v4[(b_offset + iybs + iqs) / 4]; + B_TYPE_VEC4 bv1 = data_b_v4[(b_offset + iybs + iqs) / 4 + 1]; + FLOAT_TYPE b0 = FLOAT_TYPE(bv0.x); + FLOAT_TYPE b1 = FLOAT_TYPE(bv0.y); + FLOAT_TYPE b2 = FLOAT_TYPE(bv0.z); + FLOAT_TYPE b3 = FLOAT_TYPE(bv0.w); + FLOAT_TYPE b4 = FLOAT_TYPE(bv1.x); + FLOAT_TYPE b5 = FLOAT_TYPE(bv1.y); + FLOAT_TYPE b6 = FLOAT_TYPE(bv1.z); + FLOAT_TYPE b7 = FLOAT_TYPE(bv1.w); +#endif +#else + // Check if the second of the pair of elements is OOB, and don't fetch B or + // accumulate it. We still fetch a pair of elements for A, which is fine for + // quantized formats since they'll be within the same block. We should + // probably skip fetching the second element for F16/F32, but as of now we + // still do. + const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols); + + FLOAT_TYPE b0 = 0, b1 = 0; + b0 = FLOAT_TYPE(data_b[b_offset + iybs + iqs]); + if (!OOB) { + b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); + } +#endif + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib = ((first_row + n)*p.ncols + col)/QUANT_K; // block index + +#if K_PER_ITER == 8 + const vec4 v = dequantize4(ib, iqs, a_offset); + const vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); + + // matrix multiplication + temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.z), b2, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.w), b3, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.x), b4, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.y), b5, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.z), b6, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.w), b7, temp[n]); +#else + const vec2 v = dequantize(ib, iqs, a_offset); + + // matrix multiplication + temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); + if (!OOB) { + temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); + } +#endif + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + const uint tid = gl_LocalInvocationID.x; + + get_offsets(a_offset, b_offset, d_offset); + a_offset /= QUANT_K; + + y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + FLOAT_TYPE temp[NUM_ROWS]; + + for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } + + uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE); + if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) { + num_iters++; + } + int unroll_count = 4; + uint unrolled_iters = num_iters & ~(unroll_count - 1); + + uint i = 0; + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } + unroll_count = 2; + unrolled_iters = num_iters & ~(unroll_count - 1); + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } + while (i < num_iters) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, true); + i++; + } + + // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } + } + barrier(); + } + if (tid == 0) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp similarity index 92% rename from ggml/src/vulkan-shaders/mul_mat_vec_base.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp index 5920bc9364..8d0a579137 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp @@ -12,6 +12,9 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 1) readonly buffer BV2 {B_TYPE_VEC2 data_b_v2[];}; +layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];}; + layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; #ifdef MUL_MAT_ID layout (binding = 3) readonly buffer IDS {int data_ids[];}; diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp similarity index 100% rename from ggml/src/vulkan-shaders/mul_mat_vec_nc.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp similarity index 100% rename from ggml/src/vulkan-shaders/mul_mat_vec_p021.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp similarity index 98% rename from ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp index ec8eadcd58..e2625d32b9 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -9,6 +9,10 @@ shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + if (row >= p.stride_d) { + return; + } + uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp similarity index 98% rename from ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp index 3ca4ad85a5..a28804533c 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -9,6 +9,10 @@ shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + if (row >= p.stride_d) { + return; + } + uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp new file mode 100644 index 0000000000..5846f2e86f --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -0,0 +1,127 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +// This shader assumes K_QUANTS_PER_ITERATION == 2 for alignment of loads +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + + const uint il = tid/step; // 0...3 + const uint ir = tid - step*il; // 0...7 or 0...3 + const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = n * (2 * ir + v_in); // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4]; + uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16]; + + uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F; + uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F; + uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F; + + uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4)); + uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4)); + uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4)); + uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4)); + + const uint32_t q4_0 = qs0_lo4.x; + const uint32_t q4_1 = qs0_lo4.y; + const uint32_t q4_2 = qs0_lo4.z; + const uint32_t q4_3 = qs0_lo4.w; + const uint32_t q4_4 = qs0_hi4.x; + const uint32_t q4_5 = qs0_hi4.y; + const uint32_t q4_6 = qs0_hi4.z; + const uint32_t q4_7 = qs0_hi4.w; + const uint32_t q4_8 = qs64_lo4.x; + const uint32_t q4_9 = qs64_lo4.y; + const uint32_t q4_10 = qs64_lo4.z; + const uint32_t q4_11 = qs64_lo4.w; + const uint32_t q4_12 = qs64_hi4.x; + const uint32_t q4_13 = qs64_hi4.y; + const uint32_t q4_14 = qs64_hi4.z; + const uint32_t q4_15 = qs64_hi4.w; + + B_TYPE_VEC4 by10 = data_b_v4[(b_offset + y1_idx) / 4]; + B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8]; + B_TYPE_VEC4 by20 = data_b_v4[(b_offset + y2_idx) / 4]; + B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8]; + + const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, + fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, + fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, + fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); + temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp new file mode 100644 index 0000000000..22a6bfae4f --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -0,0 +1,151 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 + + const uint il = tid/4; // 0...3 + const uint ir = tid - 4*il; // 0...7 or 0...3 + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = 4*ir + 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + const uint8_t hm1 = uint8_t(1 << (2*v_im)); + const uint8_t hm2 = uint8_t(hm1 << 4); + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); + uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16); + + uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F; + uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F; + uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F; + + uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4)); + uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4)); + uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4)); + uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4)); + + const uint32_t q4_0 = qs0_16_lo4.x; + const uint32_t q4_1 = qs0_16_lo4.y; + const uint32_t q4_2 = qs0_16_lo4.z; + const uint32_t q4_3 = qs0_16_lo4.w; + const uint32_t q4_4 = qs0_16_hi4.x; + const uint32_t q4_5 = qs0_16_hi4.y; + const uint32_t q4_6 = qs0_16_hi4.z; + const uint32_t q4_7 = qs0_16_hi4.w; + const uint32_t q4_8 = qs64_80_lo4.x; + const uint32_t q4_9 = qs64_80_lo4.y; + const uint32_t q4_10 = qs64_80_lo4.z; + const uint32_t q4_11 = qs64_80_lo4.w; + const uint32_t q4_12 = qs64_80_hi4.x; + const uint32_t q4_13 = qs64_80_hi4.y; + const uint32_t q4_14 = qs64_80_hi4.z; + const uint32_t q4_15 = qs64_80_hi4.w; + + B_TYPE_VEC2 by10 = data_b_v2[(b_offset + y1_idx) / 2]; + B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8]; + B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16]; + B_TYPE_VEC2 by148 = data_b_v2[(b_offset + y1_idx) / 2 + 24]; + B_TYPE_VEC2 by20 = data_b_v2[(b_offset + y2_idx) / 2]; + B_TYPE_VEC2 by216 = data_b_v2[(b_offset + y2_idx) / 2 + 8]; + B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16]; + B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24]; + + uint32_t qh0 = data_a_packed16[ib0 + i].qh[l0 / 2]; + uint32_t qh1 = qh0 >> 8; + uint32_t qh16 = data_a_packed16[ib0 + i].qh[l0 / 2 + 8]; + uint32_t qh17 = qh16 >> 8; + + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(by10.x), (q4_0 + (((qh0 & hm1) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by10.y), (q4_1 + (((qh1 & hm1) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by116.x), (q4_2 + (((qh16 & hm1) != 0) ? 16 : 0)), + FLOAT_TYPE(by116.y) * (q4_3 + (((qh17 & hm1) != 0) ? 16 : 0))))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(by132.x), (q4_4 + (((qh0 & (hm1 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by132.y), (q4_5 + (((qh1 & (hm1 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by148.x), (q4_6 + (((qh16 & (hm1 << 1)) != 0) ? 16 : 0)), + FLOAT_TYPE(by148.y) * (q4_7 + (((qh17 & (hm1 << 1)) != 0) ? 16 : 0))))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(by20.x), (q4_8 + (((qh0 & hm2) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by20.y), (q4_9 + (((qh1 & hm2) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by216.x), (q4_10 + (((qh16 & hm2) != 0) ? 16 : 0)), + FLOAT_TYPE(by216.y) * (q4_11 + (((qh17 & hm2) != 0) ? 16 : 0))))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(by232.x), (q4_12 + (((qh0 & (hm2 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by232.y), (q4_13 + (((qh1 & (hm2 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(by248.x), (q4_14 + (((qh16 & (hm2 << 1)) != 0) ? 16 : 0)), + FLOAT_TYPE(by248.y) * (q4_15 + (((qh17 & (hm2 << 1)) != 0) ? 16 : 0))))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, + fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, + fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, + (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); + temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp new file mode 100644 index 0000000000..0b392d68d0 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -0,0 +1,110 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 + +#if K_QUANTS_PER_ITERATION == 1 + const uint l0 = v_in; // 0...15 + const uint is = 0; +#else + const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const uint is = v_in / 4; +#endif + + const uint ql_offset = 64*v_im + l0; + const uint qh_offset = 32*v_im + l0; + const uint s_offset = 8*v_im + is; + const uint y_offset = 128*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + FLOAT_TYPE scales[4]; + scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]); + scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]); + scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]); + scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]); + + uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16); + uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16); + + uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F; + uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F; + uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F; + uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F; + + uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16); + uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4; + uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2; + uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0; + uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2; + + uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32; + uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32; + uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32; + uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32; + + uvec4 q0 = uvec4(unpack8(q0_u32)); + uvec4 q1 = uvec4(unpack8(q1_u32)); + uvec4 q2 = uvec4(unpack8(q2_u32)); + uvec4 q3 = uvec4(unpack8(q3_u32)); + + B_TYPE_VEC4 by0 = data_b_v4[(b_offset + y_idx) / 4]; + B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8]; + B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16]; + B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24]; + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 4; ++l) { + sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), + fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), + fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), + fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + } + temp += sum * d; + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp similarity index 99% rename from ggml/src/vulkan-shaders/mul_mm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp index fffdd18189..2ff5c43051 100644 --- a/ggml/src/vulkan-shaders/mul_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -75,6 +75,10 @@ shared u16vec2 row_ids[3072]; #endif void main() { +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + #ifdef MUL_MAT_ID const uint expert_idx = gl_GlobalInvocationID.z; #else diff --git a/ggml/src/vulkan-shaders/norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/norm.comp diff --git a/ggml/src/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp similarity index 92% rename from ggml/src/vulkan-shaders/pad.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/pad.comp index a465cd52bc..e87d8b18b1 100644 --- a/ggml/src/vulkan-shaders/pad.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp new file mode 100644 index 0000000000..b6124411a0 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp @@ -0,0 +1,74 @@ +#version 450 + +#include "types.comp" + +#extension GL_EXT_shader_16bit_storage : require + +layout(push_constant) uniform parameter { + uint IW; uint IH; + uint OW; uint OH; + uint OC; + uint pelements; + uint op; + int k0; int k1; + int s0; int s1; + int p0; int p1; +} p; + +#define BLOCK_SIZE 512 +#define FLT_MAX 3.402823466e+38F +#define OP_POOL_MAX 0u +#define OP_POOL_AVG 1u + +layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout(binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + if (idx >= p.pelements) { + return; + } + + const uint O_HW = p.OW * p.OH; + + const uint nc = idx / O_HW; + const uint cur_oh = (idx % O_HW) / p.OW; + const uint cur_ow = (idx % O_HW) % p.OW; + + const int start_h = int(cur_oh) * p.s0 - p.p0; + const uint bh = max(start_h, 0); + const uint eh = min(start_h + p.k0, p.IH); + + const int start_w = int(cur_ow) * p.s1 - p.p1; + const uint bw = max(start_w, 0); + const uint ew = min(start_w + p.k1, p.IW); + + const float scale = 1.0 / float(p.k0 * p.k1); + float res; + + if (p.op == OP_POOL_AVG) { + res = 0.0; + } else if (p.op == OP_POOL_MAX) { + res = -FLT_MAX; + } else { + return; + } + + #pragma unroll + for (uint i = bh; i < eh; i++) { + #pragma unroll + for (uint j = bw; j < ew; j++) { + const float cur = D_TYPE(data_a[nc * p.IH * p.IW + i * p.IW + j]); + + if (p.op == OP_POOL_AVG) { + res += cur * scale; + } else if (p.op == OP_POOL_MAX) { + res = max(res, cur); + } + } + } + + data_d[nc * O_HW + cur_oh * p.OW + cur_ow] = res; +} diff --git a/ggml/src/vulkan-shaders/relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp similarity index 100% rename from ggml/src/vulkan-shaders/relu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/relu.comp diff --git a/ggml/src/vulkan-shaders/repeat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp similarity index 91% rename from ggml/src/vulkan-shaders/repeat.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp index a86af87e7b..c03f737cc1 100644 --- a/ggml/src/vulkan-shaders/repeat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + uint src0_idx_mod(uint idx) { const uint i13 = idx / (p.ne12*p.ne11*p.ne10); const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; diff --git a/ggml/src/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/rms_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp diff --git a/ggml/src/vulkan-shaders/rope_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_head.comp diff --git a/ggml/src/vulkan-shaders/rope_neox.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_neox.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp diff --git a/ggml/src/vulkan-shaders/rope_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp new file mode 100644 index 0000000000..5cfee8c3bd --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp @@ -0,0 +1,24 @@ +#version 450 + +#include "types.comp" +#include "generic_unary_head.comp" + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + + data_d[p.d_offset + idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/silu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/silu.comp similarity index 100% rename from ggml/src/vulkan-shaders/silu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/silu.comp diff --git a/ggml/src/vulkan-shaders/sin.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp similarity index 80% rename from ggml/src/vulkan-shaders/sin.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/sin.comp index 7faf9be936..67c48fb9aa 100644 --- a/ggml/src/vulkan-shaders/sin.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp new file mode 100644 index 0000000000..6e20b6411c --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp @@ -0,0 +1,174 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; +} p; + +#include "types.comp" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +// num_iters is the number of BLOCK_SIZE loop iterations we need to iterate +// over all the columns. The main function tries to pass a constant here, +// as if it were a template function, to allow unrolling. +void soft_max(uint num_iters) { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint rowy = rowx % p.KY; + + if (rowx >= p.nrows_x) { + return; + } + + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = rowx/p.KY; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + // Find max + FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); + + // Cache values while we compute the max, so we don't need to read them + // again when we're ready to compute exp(x-max). + const uint DATA_CACHE_SIZE = 16; + FLOAT_TYPE data_cache[DATA_CACHE_SIZE]; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy * p.KX + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = v; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum, and cache the new value + // in data_cache if possible. + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + if (idx < DATA_CACHE_SIZE) { + val = exp(data_cache[idx] - max_val); + } else { + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + } + sum += val; + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = val; + } else { + data_d[i] = D_TYPE(val); + } + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + sum = vals[0]; + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + if (idx < DATA_CACHE_SIZE) { + data_d[rowx*p.KX + col] = D_TYPE(data_cache[idx] * rcpdivisor); + } else { + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } + } +} + +void main() { + // instantiate the soft_max function for several different + // dimensions, to allow loop unrolling + uint num_blocks = (p.KX + BLOCK_SIZE - 1) / BLOCK_SIZE; + if (num_blocks > 32) { + soft_max(num_blocks); + } else if (num_blocks > 16) { + soft_max(32); + } else if (num_blocks > 8) { + soft_max(16); + } else if (num_blocks > 4) { + soft_max(8); + } else if (num_blocks == 4) { + soft_max(4); + } else if (num_blocks == 3) { + soft_max(3); + } else if (num_blocks == 2) { + soft_max(2); + } else if (num_blocks == 1) { + soft_max(1); + } +} diff --git a/ggml/src/vulkan-shaders/square.comp b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp similarity index 80% rename from ggml/src/vulkan-shaders/square.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/square.comp index 1fa118c996..2ff48ddc53 100644 --- a/ggml/src/vulkan-shaders/square.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/sum_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp similarity index 100% rename from ggml/src/vulkan-shaders/sum_rows.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp diff --git a/ggml/src/vulkan-shaders/tanh.comp b/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp similarity index 100% rename from ggml/src/vulkan-shaders/tanh.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp diff --git a/ggml/src/vulkan-shaders/timestep_embedding.comp b/ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp similarity index 100% rename from ggml/src/vulkan-shaders/timestep_embedding.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp diff --git a/ggml/src/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp similarity index 57% rename from ggml/src/vulkan-shaders/types.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/types.comp index 21dce72fc7..bc28e0ab85 100644 --- a/ggml/src/vulkan-shaders/types.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp @@ -1,6 +1,8 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif + +#if !defined(GGML_TYPES_COMP) +#define GGML_TYPES_COMP + +#extension GL_EXT_shader_explicit_arithmetic_types : require #if defined(DATA_A_F32) #define QUANT_K 1 @@ -38,8 +40,14 @@ struct block_q4_0 float16_t d; uint8_t qs[16]; }; +struct block_q4_0_packed16 +{ + float16_t d; + uint16_t qs[16/2]; +}; #define A_TYPE block_q4_0 +#define A_TYPE_PACKED16 block_q4_0_packed16 #endif #if defined(DATA_A_Q4_1) @@ -54,7 +62,15 @@ struct block_q4_1 uint8_t qs[16]; }; +struct block_q4_1_packed16 +{ + float16_t d; + float16_t m; + uint16_t qs[16/2]; +}; + #define A_TYPE block_q4_1 +#define A_TYPE_PACKED16 block_q4_1_packed16 #endif #if defined(DATA_A_Q5_0) @@ -70,7 +86,15 @@ struct block_q5_0 uint8_t qs[16]; }; +struct block_q5_0_packed16 +{ + float16_t d; + uint16_t qh[2]; + uint16_t qs[16/2]; +}; + #define A_TYPE block_q5_0 +#define A_TYPE_PACKED16 block_q5_0_packed16 #endif #if defined(DATA_A_Q5_1) @@ -87,7 +111,16 @@ struct block_q5_1 uint8_t qs[16]; }; +struct block_q5_1_packed16 +{ + float16_t d; + float16_t m; + uint qh; + uint16_t qs[16/2]; +}; + #define A_TYPE block_q5_1 +#define A_TYPE_PACKED16 block_q5_1_packed16 #endif #if defined(DATA_A_Q8_0) @@ -100,8 +133,14 @@ struct block_q8_0 float16_t d; int8_t qs[32]; }; +struct block_q8_0_packed16 +{ + float16_t d; + uint16_t qs[32/2]; +}; #define A_TYPE block_q8_0 +#define A_TYPE_PACKED16 block_q8_0_packed16 #endif // K-quants @@ -116,7 +155,23 @@ struct block_q2_K f16vec2 d; }; +struct block_q2_K_packed16 +{ + uint16_t scales[QUANT_K/16/2]; + uint16_t qs[QUANT_K/4/2]; + f16vec2 d; +}; + +struct block_q2_K_packed32 +{ + uint32_t scales[QUANT_K/16/4]; + uint32_t qs[QUANT_K/4/4]; + f16vec2 d; +}; + #define A_TYPE block_q2_K +#define A_TYPE_PACKED16 block_q2_K_packed16 +#define A_TYPE_PACKED32 block_q2_K_packed32 #endif #if defined(DATA_A_Q3_K) @@ -131,7 +186,16 @@ struct block_q3_K float16_t d; }; +struct block_q3_K_packed16 +{ + uint16_t hmask[QUANT_K/8/2]; + uint16_t qs[QUANT_K/4/2]; + uint16_t scales[12/2]; + float16_t d; +}; + #define A_TYPE block_q3_K +#define A_TYPE_PACKED16 block_q3_K_packed16 #endif #if defined(DATA_A_Q4_K) @@ -145,7 +209,23 @@ struct block_q4_K uint8_t qs[QUANT_K/2]; }; +struct block_q4_K_packed16 +{ + f16vec2 d; + uint16_t scales[3*QUANT_K/64/2]; + uint16_t qs[QUANT_K/2/2]; +}; + +struct block_q4_K_packed32 +{ + f16vec2 d; + uint32_t scales[3*QUANT_K/64/4]; + uint32_t qs[QUANT_K/2/4]; +}; + #define A_TYPE block_q4_K +#define A_TYPE_PACKED16 block_q4_K_packed16 +#define A_TYPE_PACKED32 block_q4_K_packed32 #endif #if defined(DATA_A_Q5_K) @@ -160,7 +240,16 @@ struct block_q5_K uint8_t qs[QUANT_K/2]; }; +struct block_q5_K_packed16 +{ + f16vec2 d; + uint16_t scales[12/2]; + uint16_t qh[QUANT_K/8/2]; + uint16_t qs[QUANT_K/2/2]; +}; + #define A_TYPE block_q5_K +#define A_TYPE_PACKED16 block_q5_K_packed16 #endif #if defined(DATA_A_Q6_K) @@ -175,7 +264,16 @@ struct block_q6_K float16_t d; }; +struct block_q6_K_packed16 +{ + uint16_t ql[QUANT_K/2/2]; + uint16_t qh[QUANT_K/4/2]; + int8_t scales[QUANT_K/16]; + float16_t d; +}; + #define A_TYPE block_q6_K +#define A_TYPE_PACKED16 block_q6_K_packed16 #endif // IQuants @@ -191,10 +289,30 @@ struct block_iq4_nl uint8_t qs[QUANT_K/2]; }; -#define A_TYPE block_iq4_nl +struct block_iq4_nl_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K/2/2]; +}; -const int8_t kvalues_iq4nl[16] = { +#define A_TYPE block_iq4_nl +#define A_TYPE_PACKED16 block_iq4_nl_packed16 + +const int8_t kvalues_iq4nl_const[16] = { int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10), int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113) }; + +shared FLOAT_TYPE kvalues_iq4nl[16]; + +void init_iq4nl_shmem() +{ + // copy the table into shared memory and sync + if (gl_LocalInvocationIndex.x < 16) { + kvalues_iq4nl[gl_LocalInvocationIndex.x] = FLOAT_TYPE(kvalues_iq4nl_const[gl_LocalInvocationIndex.x]); + } + barrier(); +} #endif + +#endif // !defined(GGML_TYPES_COMP) diff --git a/ggml/src/vulkan-shaders/upscale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp similarity index 100% rename from ggml/src/vulkan-shaders/upscale.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp similarity index 52% rename from ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp rename to ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 1bd1b6f67d..6bbe8e96ed 100644 --- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #include #include @@ -92,11 +93,11 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s std::array buffer; DWORD bytes_read; - while (ReadFile(stdout_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + while (ReadFile(stdout_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { stdout_str.append(buffer.data(), bytes_read); } - while (ReadFile(stderr_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + while (ReadFile(stderr_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { stderr_str.append(buffer.data(), bytes_read); } @@ -190,7 +191,12 @@ std::string basename(const std::string &path) { return path.substr(path.find_last_of("/\\") + 1); } -void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { +// variables to track number of compiles in progress +static uint32_t compile_count = 0; +static std::mutex compile_count_mutex; +static std::condition_variable compile_count_cond; + +void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { std::string name = _name + (fp16 ? "" : "_fp32"); std::string out_fname = join_paths(output_dir, name + ".spv"); std::string in_path = join_paths(input_dir, in_fname); @@ -233,6 +239,12 @@ void string_to_spv(const std::string& _name, const std::string& in_fname, const } catch (const std::exception& e) { std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl; } + { + std::lock_guard guard(compile_count_mutex); + assert(compile_count > 0); + compile_count--; + } + compile_count_cond.notify_all(); } std::map merge_maps(const std::map& a, const std::map& b) { @@ -241,7 +253,22 @@ std::map merge_maps(const std::map>& tasks, bool fp16, bool matmul_id) { +static std::vector> compiles; +void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { + { + // wait until fewer than N compiles are in progress. + // 16 is an arbitrary limit, the goal is to avoid "failed to create pipe" errors. + uint32_t N = 16; + std::unique_lock guard(compile_count_mutex); + while (compile_count >= N) { + compile_count_cond.wait(guard); + } + compile_count++; + } + compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16)); +} + +void matmul_shaders(bool fp16, bool matmul_id) { std::string load_vec = fp16 ? "8" : "4"; std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4"; std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4"; @@ -259,19 +286,11 @@ void matmul_shaders(std::vector>& tasks, bool fp16, bool matmu } // Shaders with f16 B_TYPE - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - })); + string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); + string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - })); + string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); + string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); for (const auto& tname : type_names) { std::string data_a_key = "DATA_A_" + to_uppercase(tname); @@ -279,22 +298,18 @@ void matmul_shaders(std::vector>& tasks, bool fp16, bool matmu std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2"; // For aligned matmul loads std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2"; - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16); - })); + string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16); + string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16); } } -void process_shaders(std::vector>& tasks) { +void process_shaders() { std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl; std::map base_dict = {{"FLOAT_TYPE", "float"}}; for (const auto& fp16 : {false, true}) { - matmul_shaders(tasks, fp16, false); - matmul_shaders(tasks, fp16, true); + matmul_shaders(fp16, false); + matmul_shaders(fp16, true); } for (const auto& tname : type_names) { @@ -302,197 +317,106 @@ void process_shaders(std::vector>& tasks) { std::string data_a_key = "DATA_A_" + to_uppercase(tname); std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp"; - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - })); + string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}})); + string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}})); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); + string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}})); // Dequant shaders if (tname != "f16") { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); - })); + string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); } if (!string_ends_with(tname, "_k")) { shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp"; if (tname == "f16") { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); } else { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}); - })); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); } - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}); - })); + string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); } } - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); // Norms - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); + string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); + string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); - })); + string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); + string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}); - })); + string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); + string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - })); + string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - })); + string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - })); + string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); - })); + string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); + string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}})); - })); + string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}})); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); + string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + for (auto &c : compiles) { + c.wait(); + } } void write_output_files() { @@ -587,12 +511,7 @@ int main(int argc, char** argv) { } } - std::vector> tasks; - process_shaders(tasks); - - for (auto& task : tasks) { - task.get(); - } + process_shaders(); write_output_files(); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 3f01092d9f..78e7874dee 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1,11 +1,13 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows +#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows #define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-backend.h" #include "ggml-impl.h" -#include "ggml-cpu-impl.h" -#include "ggml-quants.h" +#include "ggml-threading.h" #include "ggml.h" + +// FIXME: required here for quantization functions +#include "ggml-quants.h" #include "ggml-aarch64.h" #if defined(_MSC_VER) || defined(__MINGW32__) @@ -31,170 +33,38 @@ #include #endif -#ifdef GGML_USE_OPENMP -#include -#endif - -#ifdef GGML_USE_METAL +#if defined(__APPLE__) #include +#include +#include #endif -#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) -#undef GGML_USE_LLAMAFILE -#endif - -#ifdef GGML_USE_LLAMAFILE -#include -#endif - -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) - -// disable POSIX deprecation warnings -// these functions are never going away, anyway -#pragma warning(disable: 4996) - -// unreachable code because of multiple instances of code after GGML_ABORT -#pragma warning(disable: 4702) -#endif - -// Note: once we move threading into a separate C++ file -// will use std::hardware_destructive_interference_size instead of hardcoding it here -// and we'll use C++ attribute syntax. -#define GGML_CACHE_LINE 64 - -#if defined(__clang__) || defined(__GNUC__) -#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) -#endif - -#if defined(__has_feature) -#if __has_feature(thread_sanitizer) -#define GGML_TSAN_ENABLED 1 -#endif -#else // __has_feature -#if defined(__SANITIZE_THREAD__) -#define GGML_TSAN_ENABLED 1 -#endif -#endif // __has_feature - #if defined(_WIN32) - #define WIN32_LEAN_AND_MEAN #ifndef NOMINMAX #define NOMINMAX #endif #include - -#if !defined(__clang__) -#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) - -typedef volatile LONG atomic_int; -typedef atomic_int atomic_bool; -typedef atomic_int atomic_flag; - -#define ATOMIC_FLAG_INIT 0 - -typedef enum { - memory_order_relaxed, - memory_order_consume, - memory_order_acquire, - memory_order_release, - memory_order_acq_rel, - memory_order_seq_cst -} memory_order; - -static void atomic_store(atomic_int * ptr, LONG val) { - InterlockedExchange(ptr, val); -} -static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { - // TODO: add support for explicit memory order - InterlockedExchange(ptr, val); -} -static LONG atomic_load(atomic_int * ptr) { - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { - return InterlockedExchangeAdd(ptr, inc); -} -static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedExchangeAdd(ptr, inc); -} -static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { - return InterlockedExchange(ptr, 1); -} -static void atomic_flag_clear(atomic_flag * ptr) { - InterlockedExchange(ptr, 0); -} -static void atomic_thread_fence(memory_order mo) { - MemoryBarrier(); -} -#else // clang -#include #endif -typedef HANDLE pthread_t; +#define UNUSED GGML_UNUSED -typedef DWORD thread_ret_t; -static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { - (void) unused; - HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); - if (handle == NULL) - { - return EAGAIN; - } - - *out = handle; - return 0; -} - -static int pthread_join(pthread_t thread, void * unused) { - (void) unused; - int ret = (int) WaitForSingleObject(thread, INFINITE); - CloseHandle(thread); - return ret; -} - -static int sched_yield (void) { - Sleep (0); - return 0; -} +#if defined(_MSC_VER) +#define m512bh(p) p +#define m512i(p) p #else - -#include -#include -#include -#if defined(__FreeBSD__) -#include +#define m512bh(p) (__m512bh)(p) +#define m512i(p) (__m512i)(p) #endif -typedef void * thread_ret_t; - -#include -#include -#include - -#endif - -typedef pthread_t ggml_thread_t; - -#ifdef GGML_USE_CPU_HBM -#include -#endif - -#if defined(__APPLE__) -#include -#endif +// precomputed f32 table for f16 (256 KB) (ggml-impl.h) +float ggml_table_f32_f16[1 << 16]; #if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \ (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH)) - +#include +#include +#include #include #if defined(__ANDROID__) @@ -307,14 +177,6 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) { abort(); } -#define GGML_DEBUG 0 -#define GGML_GELU_FP16 -#define GGML_GELU_QUICK_FP16 - -#define GGML_SOFT_MAX_UNROLL 4 -#define GGML_VEC_DOT_UNROLL 2 -#define GGML_VEC_MAD_UNROLL 32 - // // logging // @@ -326,8 +188,9 @@ struct ggml_logger_state { static struct ggml_logger_state g_logger_state = {ggml_log_callback_default, NULL}; static void ggml_log_internal_v(enum ggml_log_level level, const char * format, va_list args) { - if (format == NULL) + if (format == NULL) { return; + } va_list args_copy; va_copy(args_copy, args); char buffer[128]; @@ -358,24 +221,6 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi fflush(stderr); } -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - // // end of logging block // @@ -386,23 +231,41 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi //#define GGML_SOFT_MAX_ACCELERATE #endif + +void * ggml_aligned_malloc(size_t size) { + const int alignment = 64; + #if defined(_MSC_VER) || defined(__MINGW32__) -#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) -#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) + return _aligned_malloc(size, alignment); #else -inline static void * ggml_aligned_malloc(size_t size) { if (size == 0) { GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n"); return NULL; } void * aligned_memory = NULL; -#ifdef GGML_USE_CPU_HBM - int result = hbw_posix_memalign(&aligned_memory, 16, size); -#elif GGML_USE_METAL - int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size); -#else - int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size); -#endif + #ifdef GGML_USE_CPU_HBM + int result = hbw_posix_memalign(&aligned_memory, alignment, size); + #elif TARGET_OS_OSX + GGML_UNUSED(alignment); + kern_return_t alloc_status = vm_allocate((vm_map_t) mach_task_self(), (vm_address_t *) &aligned_memory, size, VM_FLAGS_ANYWHERE); + int result = EFAULT; + switch (alloc_status) { + case KERN_SUCCESS: + result = 0; + break; + case KERN_INVALID_ADDRESS: + result = EINVAL; + break; + case KERN_NO_SPACE: + result = ENOMEM; + break; + default: + result = EFAULT; + break; + } + #else + int result = posix_memalign(&aligned_memory, alignment, size); + #endif if (result != 0) { // Handle allocation failure const char *error_desc = "unknown allocation error"; @@ -415,18 +278,29 @@ inline static void * ggml_aligned_malloc(size_t size) { break; } GGML_LOG_ERROR("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); - GGML_ABORT("fatal error"); return NULL; } return aligned_memory; +#endif } -#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) -#ifdef GGML_USE_CPU_HBM -#define GGML_ALIGNED_FREE(ptr) if(NULL != ptr) hbw_free(ptr) + +void ggml_aligned_free(void * ptr, size_t size) { + GGML_UNUSED(size); +#if defined(_MSC_VER) || defined(__MINGW32__) + _aligned_free(ptr); +#elif GGML_USE_CPU_HBM + if (ptr != NULL) { + hbw_free(ptr); + } +#elif TARGET_OS_OSX + if (ptr != NULL) { + vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ptr, size); + } #else -#define GGML_ALIGNED_FREE(ptr) free(ptr) -#endif + free(ptr); #endif +} + inline static void * ggml_malloc(size_t size) { if (size == 0) { @@ -460,44 +334,6 @@ inline static void * ggml_calloc(size_t num, size_t size) { #define GGML_FREE(ptr) free(ptr) -#define UNUSED GGML_UNUSED -#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) - -#if defined(GGML_USE_ACCELERATE) -#include -#endif - -// floating point type used to accumulate sums -typedef double ggml_float; - -#undef MIN -#undef MAX - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - -// -// global data -// - -// precomputed gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; - -// precomputed quick gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; - -// precomputed f32 table for f16 (256 KB) (ggml-impl.h) -float ggml_table_f32_f16[1 << 16]; - -#if defined(__ARM_ARCH) -struct ggml_arm_arch_features_type { - int has_neon; - int has_i8mm; - int has_sve; - int sve_cnt; -} ggml_arm_arch_features = {-1, -1, -1, 0}; -#endif - const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; @@ -535,19 +371,23 @@ void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { } } +// FIXME: these functions must detect the instruction set at runtime, since they are part of the core ggml library +// currently, the ggml_cpu_has_* functions are entirely compile-time void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { int64_t i = 0; #if defined(__F16C__) - for (; i + 7 < n; i += 8) { - __m256 x_vec = _mm256_loadu_ps(x + i); - __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storeu_si128((__m128i *)(y + i), y_vec); - } - for(; i + 3 < n; i += 4) { - __m128 x_vec = _mm_loadu_ps(x + i); - __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storel_epi64((__m128i *)(y + i), y_vec); - } + //if (ggml_cpu_has_f16c()) { + for (; i + 7 < n; i += 8) { + __m256 x_vec = _mm256_loadu_ps(x + i); + __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storeu_si128((__m128i *)(y + i), y_vec); + } + for(; i + 3 < n; i += 4) { + __m128 x_vec = _mm_loadu_ps(x + i); + __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storel_epi64((__m128i *)(y + i), y_vec); + } + //} #endif for (; i < n; i++) { y[i] = GGML_FP32_TO_FP16(x[i]); @@ -557,25 +397,30 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { int64_t i = 0; #if defined(__AVX512F__) - for (; i + 16 <= n; i += 16) { - _mm512_storeu_ps(y + i, - _mm512_castsi512_ps( - _mm512_slli_epi32( - _mm512_cvtepu16_epi32( - _mm256_loadu_si256( - (const __m256i *)(x + i))), - 16))); - } -#elif defined(__AVX2__) - for (; i + 8 <= n; i += 8) { - _mm256_storeu_ps(y + i, - _mm256_castsi256_ps( - _mm256_slli_epi32( - _mm256_cvtepu16_epi32( - _mm_loadu_si128( - (const __m128i *)(x + i))), - 16))); - } + //if (ggml_cpu_has_avx512()) { + for (; i + 16 <= n; i += 16) { + _mm512_storeu_ps(y + i, + _mm512_castsi512_ps( + _mm512_slli_epi32( + _mm512_cvtepu16_epi32( + _mm256_loadu_si256( + (const __m256i *)(x + i))), + 16))); + } + //} +#endif +#if defined(__AVX2__) + //if (ggml_cpu_has_avx2()) { + for (; i + 8 <= n; i += 8) { + _mm256_storeu_ps(y + i, + _mm256_castsi256_ps( + _mm256_slli_epi32( + _mm256_cvtepu16_epi32( + _mm_loadu_si128( + (const __m128i *)(x + i))), + 16))); + } + //} #endif for (; i < n; i++) { y[i] = GGML_BF16_TO_FP32(x[i]); @@ -707,24 +552,8 @@ FILE * ggml_fopen(const char * fname, const char * mode) { #else return fopen(fname, mode); #endif + } - -// -// cache line -// - -#if defined(__cpp_lib_hardware_interference_size) -#define CACHE_LINE_SIZE hardware_destructive_interference_size -#else -#if defined(__POWER9_VECTOR__) -#define CACHE_LINE_SIZE 128 -#else -#define CACHE_LINE_SIZE 64 -#endif -#endif - -static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); - static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); @@ -759,16 +588,12 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .blck_size = 1, .type_size = sizeof(double), .is_quantized = false, - .nrows = 1, }, [GGML_TYPE_F32] = { .type_name = "f32", .blck_size = 1, .type_size = sizeof(float), .is_quantized = false, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, - .vec_dot_type = GGML_TYPE_F32, - .nrows = 1, }, [GGML_TYPE_F16] = { .type_name = "f16", @@ -776,11 +601,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(ggml_fp16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, - .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, .from_float_ref = (ggml_from_float_t) ggml_fp32_to_fp16_row, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, - .vec_dot_type = GGML_TYPE_F16, - .nrows = 1, }, [GGML_TYPE_Q4_0] = { .type_name = "q4_0", @@ -788,15 +609,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_0, - .from_float = quantize_row_q4_0, .from_float_ref = (ggml_from_float_t) quantize_row_q4_0_ref, - .vec_dot = ggml_vec_dot_q4_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [GGML_TYPE_Q4_1] = { .type_name = "q4_1", @@ -804,39 +617,19 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_1, - .from_float = quantize_row_q4_1, .from_float_ref = (ggml_from_float_t) quantize_row_q4_1_ref, - .vec_dot = ggml_vec_dot_q4_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [4] = { // GGML_TYPE_Q4_2 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, - .to_float = NULL, - .from_float = NULL, - .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, }, [5] = { // GGML_TYPE_Q4_3 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, - .to_float = NULL, - .from_float = NULL, - .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, }, [GGML_TYPE_Q5_0] = { .type_name = "q5_0", @@ -844,11 +637,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_0, - .from_float = quantize_row_q5_0, .from_float_ref = (ggml_from_float_t) quantize_row_q5_0_ref, - .vec_dot = ggml_vec_dot_q5_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, }, [GGML_TYPE_Q5_1] = { .type_name = "q5_1", @@ -856,11 +645,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_1, - .from_float = quantize_row_q5_1, .from_float_ref = (ggml_from_float_t) quantize_row_q5_1_ref, - .vec_dot = ggml_vec_dot_q5_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, }, [GGML_TYPE_Q8_0] = { .type_name = "q8_0", @@ -868,26 +653,14 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q8_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q8_0, - .from_float = quantize_row_q8_0, .from_float_ref = (ggml_from_float_t) quantize_row_q8_0_ref, - .from_float_to_mat = quantize_mat_q8_0, - .vec_dot = ggml_vec_dot_q8_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [GGML_TYPE_Q8_1] = { .type_name = "q8_1", .blck_size = QK8_1, .type_size = sizeof(block_q8_1), .is_quantized = true, - .from_float = quantize_row_q8_1, .from_float_ref = (ggml_from_float_t) quantize_row_q8_1_ref, - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, }, [GGML_TYPE_Q2_K] = { .type_name = "q2_K", @@ -895,11 +668,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q2_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q2_K, - .from_float = quantize_row_q2_K, .from_float_ref = (ggml_from_float_t) quantize_row_q2_K_ref, - .vec_dot = ggml_vec_dot_q2_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q3_K] = { .type_name = "q3_K", @@ -907,11 +676,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q3_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q3_K, - .from_float = quantize_row_q3_K, .from_float_ref = (ggml_from_float_t) quantize_row_q3_K_ref, - .vec_dot = ggml_vec_dot_q3_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q4_K] = { .type_name = "q4_K", @@ -919,11 +684,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_K, - .from_float = quantize_row_q4_K, .from_float_ref = (ggml_from_float_t) quantize_row_q4_K_ref, - .vec_dot = ggml_vec_dot_q4_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q5_K] = { .type_name = "q5_K", @@ -931,11 +692,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_K, - .from_float = quantize_row_q5_K, .from_float_ref = (ggml_from_float_t) quantize_row_q5_K_ref, - .vec_dot = ggml_vec_dot_q5_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q6_K] = { .type_name = "q6_K", @@ -943,11 +700,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q6_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q6_K, - .from_float = quantize_row_q6_K, .from_float_ref = (ggml_from_float_t) quantize_row_q6_K_ref, - .vec_dot = ggml_vec_dot_q6_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_XXS] = { .type_name = "iq2_xxs", @@ -955,11 +708,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_XS] = { .type_name = "iq2_xs", @@ -967,11 +716,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq2_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ3_XXS] = { .type_name = "iq3_xxs", @@ -979,11 +724,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq3_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs, - .from_float = quantize_row_iq3_xxs, .from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_ref, - .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ3_S] = { .type_name = "iq3_s", @@ -991,11 +732,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq3_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_s, - .from_float = quantize_row_iq3_s, .from_float_ref = (ggml_from_float_t)quantize_row_iq3_s_ref, - .vec_dot = ggml_vec_dot_iq3_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_S] = { .type_name = "iq2_s", @@ -1003,11 +740,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_s, - .from_float = quantize_row_iq2_s, .from_float_ref = (ggml_from_float_t)quantize_row_iq2_s_ref, - .vec_dot = ggml_vec_dot_iq2_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", @@ -1015,11 +748,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq1_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_s, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq1_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ1_M] = { .type_name = "iq1_m", @@ -1027,11 +756,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq1_m), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_m, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq1_m_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ4_NL] = { .type_name = "iq4_nl", @@ -1039,11 +764,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq4_nl), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_nl, - .from_float = quantize_row_iq4_nl, .from_float_ref = (ggml_from_float_t)quantize_row_iq4_nl_ref, - .vec_dot = ggml_vec_dot_iq4_nl_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", @@ -1051,18 +772,13 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, - .from_float = quantize_row_iq4_xs, .from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_ref, - .vec_dot = ggml_vec_dot_iq4_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, .type_size = sizeof(block_q8_K), .is_quantized = true, - .from_float = quantize_row_q8_K, }, [GGML_TYPE_BF16] = { .type_name = "bf16", @@ -1070,11 +786,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(ggml_bf16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row, - .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, .from_float_ref = (ggml_from_float_t) ggml_fp32_to_bf16_row_ref, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, - .vec_dot_type = GGML_TYPE_BF16, - .nrows = 1, }, [GGML_TYPE_Q4_0_4_4] = { .type_name = "q4_0_4x4", @@ -1083,14 +795,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x4_q8_0, - .gemm = ggml_gemm_q4_0_4x4_q8_0, }, [GGML_TYPE_Q4_0_4_8] = { .type_name = "q4_0_4x8", @@ -1099,14 +804,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x8_q8_0, - .gemm = ggml_gemm_q4_0_4x8_q8_0, }, [GGML_TYPE_Q4_0_8_8] = { .type_name = "q4_0_8x8", @@ -1115,14 +813,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 8, - .gemv = ggml_gemv_q4_0_8x8_q8_0, - .gemm = ggml_gemm_q4_0_8x8_q8_0, }, [GGML_TYPE_TQ1_0] = { .type_name = "tq1_0", @@ -1130,11 +821,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_tq1_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_tq1_0, - .from_float = quantize_row_tq1_0, .from_float_ref = (ggml_from_float_t) quantize_row_tq1_0_ref, - .vec_dot = ggml_vec_dot_tq1_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_TQ2_0] = { .type_name = "tq2_0", @@ -1142,826 +829,15 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_tq2_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_tq2_0, - .from_float = quantize_row_tq2_0, .from_float_ref = (ggml_from_float_t) quantize_row_tq2_0_ref, - .vec_dot = ggml_vec_dot_tq2_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, }; -// For internal test use const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) { GGML_ASSERT(type < GGML_TYPE_COUNT); return &type_traits[type]; } -// -// simd mappings -// - -// we define a common set of C macros which map to specific intrinsics based on the current architecture -// we then implement the fundamental computation operations below using only these macros -// adding support for new architectures requires to define the corresponding SIMD macros -// -// GGML_F32_STEP / GGML_F16_STEP -// number of elements to process in a single step -// -// GGML_F32_EPR / GGML_F16_EPR -// number of elements to fit in a single register -// - -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) - -#define GGML_SIMD - -// F32 NEON - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 float32x4_t -#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) -#define GGML_F32x4_SET1(x) vdupq_n_f32(x) -#define GGML_F32x4_LOAD vld1q_f32 -#define GGML_F32x4_STORE vst1q_f32 -#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) -#define GGML_F32x4_ADD vaddq_f32 -#define GGML_F32x4_MUL vmulq_f32 -#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - #define GGML_F16_STEP 32 - #define GGML_F16_EPR 8 - - #define GGML_F16x8 float16x8_t - #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) - #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) - #define GGML_F16x8_STORE vst1q_f16 - #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) - #define GGML_F16x8_ADD vaddq_f16 - #define GGML_F16x8_MUL vmulq_f16 - #define GGML_F16x8_REDUCE(res, x) \ - do { \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ - const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ - (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ - } while (0) - - #define GGML_F16_VEC GGML_F16x8 - #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO - #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) - #define GGML_F16_VEC_FMA GGML_F16x8_FMA - #define GGML_F16_VEC_ADD GGML_F16x8_ADD - #define GGML_F16_VEC_MUL GGML_F16x8_MUL - #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE -#else - // if FP16 vector arithmetic is not supported, we use FP32 instead - // and take advantage of the vcvt_ functions to convert to/from FP16 - - #define GGML_F16_STEP 16 - #define GGML_F16_EPR 4 - - #define GGML_F32Cx4 float32x4_t - #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) - #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) - #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) - #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) - #define GGML_F32Cx4_ADD vaddq_f32 - #define GGML_F32Cx4_MUL vmulq_f32 - #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - - #define GGML_F16_VEC GGML_F32Cx4 - #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO - #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) - #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA - #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD - #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL - #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE -#endif - -#elif defined(__AVX512F__) - -#define GGML_SIMD - -// F32 AVX512 - -#define GGML_F32_STEP 64 -#define GGML_F32_EPR 16 - -#define GGML_F32x16 __m512 -#define GGML_F32x16_ZERO _mm512_setzero_ps() -#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) -#define GGML_F32x16_LOAD _mm512_loadu_ps -#define GGML_F32x16_STORE _mm512_storeu_ps -// _mm512_fmadd_ps is defined in AVX512F so no guard is required -#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32x16_ADD _mm512_add_ps -#define GGML_F32x16_MUL _mm512_mul_ps -#define GGML_F32x16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x16 -#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD -#define GGML_F32_VEC_STORE GGML_F32x16_STORE -#define GGML_F32_VEC_FMA GGML_F32x16_FMA -#define GGML_F32_VEC_ADD GGML_F32x16_ADD -#define GGML_F32_VEC_MUL GGML_F32x16_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE - -// F16 AVX512 - -// F16 AVX - -#define GGML_F16_STEP 64 -#define GGML_F16_EPR 16 - -// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead - -#define GGML_F32Cx16 __m512 -#define GGML_F32Cx16_ZERO _mm512_setzero_ps() -#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) - -// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F -// so F16C guard isn't required -#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) -#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) - -#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32Cx16_ADD _mm512_add_ps -#define GGML_F32Cx16_MUL _mm512_mul_ps -#define GGML_F32Cx16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -#define GGML_F16_VEC GGML_F32Cx16 -#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE - -#elif defined(__AVX__) - -#define GGML_SIMD - -// F32 AVX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO _mm256_setzero_ps() -#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) -#define GGML_F32x8_LOAD _mm256_loadu_ps -#define GGML_F32x8_STORE _mm256_storeu_ps -#if defined(__FMA__) - #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) -#else - #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) -#endif -#define GGML_F32x8_ADD _mm256_add_ps -#define GGML_F32x8_MUL _mm256_mul_ps -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ - _mm256_extractf128_ps(x[0], 1)); \ - const __m128 t1 = _mm_hadd_ps(t0, t0); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 AVX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO _mm256_setzero_ps() -#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) - -#if defined(__F16C__) -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) -#else -static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { - float arr[8]; - - _mm256_storeu_ps(arr, y); - - for (int i = 0; i < 8; i++) - x[i] = GGML_FP32_TO_FP16(arr[i]); -} -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) -#endif - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD _mm256_add_ps -#define GGML_F32Cx8_MUL _mm256_mul_ps -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__POWER9_VECTOR__) - -#define GGML_SIMD - -// F32 POWER9 - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 vector float -#define GGML_F32x4_ZERO 0.0f -#define GGML_F32x4_SET1 vec_splats -#define GGML_F32x4_LOAD(p) vec_xl(0, p) -#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) -#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) -#define GGML_F32x4_ADD vec_add -#define GGML_F32x4_MUL vec_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - res = vec_extract(x[0], 0) + \ - vec_extract(x[0], 1) + \ - vec_extract(x[0], 2) + \ - vec_extract(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 POWER9 -#define GGML_F16_STEP GGML_F32_STEP -#define GGML_F16_EPR GGML_F32_EPR -#define GGML_F16_VEC GGML_F32x4 -#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F16_VEC_FMA GGML_F32x4_FMA -#define GGML_F16_VEC_ADD GGML_F32x4_ADD -#define GGML_F16_VEC_MUL GGML_F32x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE -// Use vec_xl, not vec_ld, in case the load address is not aligned. -#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ - vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ - vec_extract_fp32_from_shortl(vec_xl(0, p)) -#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] -#define GGML_F16_VEC_STORE(p, r, i) \ - if (i & 0x1) \ - vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ - r[i - GGML_ENDIAN_BYTE(0)]), \ - 0, p - GGML_F16_EPR) - -#elif defined(__wasm_simd128__) - -#define GGML_SIMD - -// F32 WASM - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 v128_t -#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F32x4_LOAD wasm_v128_load -#define GGML_F32x4_STORE wasm_v128_store -#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) -#define GGML_F32x4_ADD wasm_f32x4_add -#define GGML_F32x4_MUL wasm_f32x4_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 WASM - -#define GGML_F16_STEP 16 -#define GGML_F16_EPR 4 - -inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(p[0]); - tmp[1] = GGML_FP16_TO_FP32(p[1]); - tmp[2] = GGML_FP16_TO_FP32(p[2]); - tmp[3] = GGML_FP16_TO_FP32(p[3]); - - return wasm_v128_load(tmp); -} - -inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { - float tmp[4]; - - wasm_v128_store(tmp, x); - - p[0] = GGML_FP32_TO_FP16(tmp[0]); - p[1] = GGML_FP32_TO_FP16(tmp[1]); - p[2] = GGML_FP32_TO_FP16(tmp[2]); - p[3] = GGML_FP32_TO_FP16(tmp[3]); -} - -#define GGML_F16x4 v128_t -#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) -#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) -#define GGML_F16x4_FMA GGML_F32x4_FMA -#define GGML_F16x4_ADD wasm_f32x4_add -#define GGML_F16x4_MUL wasm_f32x4_mul -#define GGML_F16x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F16_VEC GGML_F16x4 -#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F16x4_FMA -#define GGML_F16_VEC_ADD GGML_F16x4_ADD -#define GGML_F16_VEC_MUL GGML_F16x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE - -#elif defined(__SSE3__) - -#define GGML_SIMD - -// F32 SSE - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO _mm_setzero_ps() -#define GGML_F32x4_SET1(x) _mm_set1_ps(x) -#define GGML_F32x4_LOAD _mm_loadu_ps -#define GGML_F32x4_STORE _mm_storeu_ps -#if defined(__FMA__) - // TODO: Does this work? - #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) -#else - #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) -#endif -#define GGML_F32x4_ADD _mm_add_ps -#define GGML_F32x4_MUL _mm_mul_ps -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ -} -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 SSE - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return _mm_loadu_ps(tmp); -} - -static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { - float arr[4]; - - _mm_storeu_ps(arr, y); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO _mm_setzero_ps() -#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) -#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD _mm_add_ps -#define GGML_F32Cx4_MUL _mm_mul_ps -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#elif defined(__loongarch_asx) - -#define GGML_SIMD - -// F32 LASX -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) -#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) -#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) -#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) -#define GGML_F32x8_ADD __lasx_xvfadd_s -#define GGML_F32x8_MUL __lasx_xvfmul_s -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - float *tmp_p = (float *)&x[0]; \ - res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 LASX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) - -static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return (__m256)__lasx_xvld(tmp, 0); -} -static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { - float arr[8]; - - __lasx_xvst(y, arr, 0); - - for (int i = 0; i < 8; i++) { - x[i] = GGML_FP32_TO_FP16(arr[i]); - } -} -#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD __lasx_xvfadd_s -#define GGML_F32Cx8_MUL __lasx_xvfmul_s -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__loongarch_sx) - -#define GGML_SIMD - -// F32 LSX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO __lsx_vldi(0) -#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) -#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) -#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) -#define GGML_F32x4_ADD __lsx_vfadd_s -#define GGML_F32x4_MUL __lsx_vfmul_s -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ - tmp = __lsx_vsrli_d((__m128i)t0, 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 LSX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return __lsx_vld(tmp, 0); -} - -static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { - float arr[4]; - - __lsx_vst(y, arr, 0); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO __lsx_vldi(0) -#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD __lsx_vfadd_s -#define GGML_F32Cx4_MUL __lsx_vfmul_s -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#endif - -// GGML_F32_ARR / GGML_F16_ARR -// number of registers to use per step -#ifdef GGML_SIMD -#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) -#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) -#endif - // // ggml object // @@ -1985,18 +861,14 @@ static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); struct ggml_context { size_t mem_size; - void* mem_buffer; + void * mem_buffer; bool mem_buffer_owned; bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; }; struct ggml_context_container { @@ -2005,972 +877,6 @@ struct ggml_context_container { struct ggml_context context; }; -// -// Threading defs -// - -typedef pthread_t ggml_thread_t; - -#if defined(_WIN32) - -typedef CONDITION_VARIABLE ggml_cond_t; -typedef SRWLOCK ggml_mutex_t; - -#define ggml_mutex_init(m) InitializeSRWLock(m) -#define ggml_mutex_destroy(m) -#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) -#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) -#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) -#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) - -#define ggml_cond_init(c) InitializeConditionVariable(c) -#define ggml_cond_destroy(c) -#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) -#define ggml_cond_broadcast(c) WakeAllConditionVariable(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#else - -typedef pthread_cond_t ggml_cond_t; -typedef pthread_mutex_t ggml_mutex_t; - -#define ggml_mutex_init(m) pthread_mutex_init(m, NULL) -#define ggml_mutex_destroy(m) pthread_mutex_destroy(m) -#define ggml_mutex_lock(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock(m) pthread_mutex_unlock(m) -#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) - -#define ggml_lock_init(x) UNUSED(x) -#define ggml_lock_destroy(x) UNUSED(x) -#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) -#define ggml_lock_lock(x) _mm_pause() -#else -#define ggml_lock_lock(x) UNUSED(x) -#endif -#define ggml_lock_unlock(x) UNUSED(x) - -#define GGML_LOCK_INITIALIZER 0 -#define ggml_cond_init(c) pthread_cond_init(c, NULL) -#define ggml_cond_destroy(c) pthread_cond_destroy(c) -#define ggml_cond_wait(c, m) pthread_cond_wait(c, m) -#define ggml_cond_broadcast(c) pthread_cond_broadcast(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#endif - -// Threadpool def -struct ggml_threadpool { - ggml_mutex_t mutex; // mutex for cond.var - ggml_cond_t cond; // cond.var for waiting for new work - - struct ggml_cgraph * cgraph; - struct ggml_cplan * cplan; - - // synchronization primitives - atomic_int n_graph; // incremented when there is work to be done (i.e each graph) - atomic_int GGML_CACHE_ALIGN n_barrier; - atomic_int GGML_CACHE_ALIGN n_barrier_passed; - atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. - - // these are atomic as an annotation for thread-sanitizer - atomic_bool stop; // Used for stopping the threadpool altogether - atomic_bool pause; // Used for pausing the threadpool or individual threads - atomic_bool abort; // Used for aborting processing of a graph - - struct ggml_compute_state * workers; // per thread state - int n_threads_max; // number of threads in the pool - atomic_int n_threads_cur; // number of threads used in the current graph - - int32_t prio; // Scheduling priority - uint32_t poll; // Polling level (0 - no polling) - - enum ggml_status ec; -}; - -// Per-thread state -struct ggml_compute_state { -#ifndef GGML_USE_OPENMP - ggml_thread_t thrd; - bool cpumask[GGML_MAX_N_THREADS]; - int last_graph; - bool pending; -#endif - struct ggml_threadpool * threadpool; - int ith; -}; - -struct ggml_compute_params { - // ith = thread index, nth = number of threads - int ith, nth; - - // work buffer for all threads - size_t wsize; - void * wdata; - - struct ggml_threadpool * threadpool; -}; - -// -// fundamental operations -// - -inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } -inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } -inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } -inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } -inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } -inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } -inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } -inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } -inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } - -static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - -#if defined(GGML_SIMD) - float sumf = 0.0f; - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F32_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += x[i]*y[i]; - } -#else - // scalar - ggml_float sumf = 0.0; - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(x[i]*y[i]); - } -#endif - - *s = sumf; -} - -static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - int i = 0; - ggml_float sumf = 0; - -#if defined(__AVX512BF16__) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), - m512bh(_mm512_loadu_si512((y + i)))); - c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), - m512bh(_mm512_loadu_si512((y + i + 32)))); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#elif defined(__AVX512F__) -#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#undef LOAD -#elif defined(__AVX2__) -#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) - __m256 c1 = _mm256_setzero_ps(); - __m256 c2 = _mm256_setzero_ps(); - __m256 c3 = _mm256_setzero_ps(); - __m256 c4 = _mm256_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); - c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); - c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); - } - __m128 g; - c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), - _mm256_add_ps(c2, c4)); - g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), - _mm256_castps256_ps128(c1)); - g = _mm_add_ps(g, _mm_movehl_ps(g, g)); - g = _mm_add_ss(g, _mm_movehdup_ps(g)); - sumf += (ggml_float)_mm_cvtss_f32(g); - -#undef LOAD -#endif - - for (; i < n; ++i) { - sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * - GGML_BF16_TO_FP32(y[i])); - } - *s = sumf; -} - -static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - ggml_float sumf = 0.0; - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F16_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#else - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#endif - - *s = sumf; -} - -// compute GGML_VEC_DOT_UNROLL dot products at once -// xs - x row stride in bytes -inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { - ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; - - ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); - - sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); - } - } - } - - // reduce sum0..sum3 to sum0 - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - GGML_F16_VEC_REDUCE(sumf[k], sum[k]); - } - - // leftovers - for (int i = np; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#else - for (int i = 0; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#endif - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - s[i] = sumf[i]; - } -} - -inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] += x[i]*v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] += x[i]*v; - } -#endif -} - -inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#endif -} - -// xs and vs are byte strides of x and v -inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { - - const float * restrict x[GGML_VEC_MAD_UNROLL]; - const float * restrict v[GGML_VEC_MAD_UNROLL]; - - for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { - x[i] = (const float *) ((const char *) xv + i*xs); - v[i] = (const float *) ((const char *) vv + i*vs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - vx[k] = GGML_F32_VEC_SET1(v[k][0]); - } - - GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); - } - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = np; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#else - // scalar - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = 0; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#endif -} - -//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } -inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { -#if defined(GGML_USE_ACCELERATE) - vDSP_vsmul(y, 1, &v, y, 1, n); -#elif defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_MUL(ay[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] *= v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] *= v; - } -#endif -} - -inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_MUL(ay[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#endif -} - -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } -inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } -inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } -inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } -inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } -inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } -inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } -inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } -inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } -inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } -inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } -inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } -inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } -inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } -// TODO: optimize performance -inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } - -static const float GELU_COEF_A = 0.044715f; -static const float GELU_QUICK_COEF = -1.702f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - -inline static float ggml_gelu_f32(float x) { - return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - const uint16_t * i16 = (const uint16_t *) x; - for (int i = 0; i < n; ++i) { - y[i] = ggml_table_gelu_f16[i16[i]]; - } -} - -#ifdef GGML_GELU_FP16 -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - if (x[i] <= -10.0f) { - y[i] = 0.0f; - } else if (x[i] >= 10.0f) { - y[i] = x[i]; - } else { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); - } - } -} -#else -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_f32(x[i]); - } -} -#endif - -inline static float ggml_gelu_quick_f32(float x) { - return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); -} - -//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_gelu_quick_f16[i16[i]]; -// } -//} - -#ifdef GGML_GELU_QUICK_FP16 -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); - } -} -#else -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]); - } -} -#endif - -// Sigmoid Linear Unit (SiLU) function -inline static float ggml_silu_f32(float x) { - return x/(1.0f + expf(-x)); -} - -#if __FINITE_MATH_ONLY__ -#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" -#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" -#endif - -#if defined(__ARM_NEON) && defined(__aarch64__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static float32x4_t ggml_v_expf(float32x4_t x) { - const float32x4_t r = vdupq_n_f32(0x1.8p23f); - const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); - const float32x4_t n = vsubq_f32(z, r); - const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, - vdupq_n_f32(0x1.7f7d1cp-20f)); - const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); - const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); - const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); - const float32x4_t u = vmulq_f32(b, b); - const float32x4_t j = vfmaq_f32( - vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), - vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), - vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); - if (!vpaddd_u64(vreinterpretq_u64_u32(c))) - return vfmaq_f32(k, j, k); - const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); - const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); - const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); - return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), - vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static float32x4_t ggml_v_silu(float32x4_t x) { - const float32x4_t one = vdupq_n_f32(1.0f); - const float32x4_t zero = vdupq_n_f32(0.0f); - const float32x4_t neg_x = vsubq_f32(zero, x); - const float32x4_t exp_neg_x = ggml_v_expf(neg_x); - const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); - return vdivq_f32(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX512F__) && defined(__AVX512DQ__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m512 ggml_v_expf(__m512 x) { - const __m512 r = _mm512_set1_ps(0x1.8p23f); - const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); - const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __mmask16 d = - _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps( - _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); - const __m512 res = _mm512_scalef_ps(j, n); - if (_mm512_kortestz(d, d)) - return res; - const __m512 zero = _mm512_setzero_ps(); - const __m512 alt = _mm512_mask_blend_ps( - _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); - return _mm512_mask_blend_ps(d, res, alt); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m512 ggml_v_silu(__m512 x) { - const __m512 one = _mm512_set1_ps(1); - const __m512 zero = _mm512_setzero_ps(); - const __m512 neg_x = _mm512_sub_ps(zero, x); - const __m512 exp_neg_x = ggml_v_expf(neg_x); - const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); - return _mm512_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX2__) && defined(__FMA__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m256 ggml_v_expf(__m256 x) { - const __m256 r = _mm256_set1_ps(0x1.8p23f); - const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); - const __m256 n = _mm256_sub_ps(z, r); - const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), - _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); - const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); - const __m256 k = _mm256_castsi256_ps( - _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); - const __m256i c = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(126), _CMP_GT_OQ)); - const __m256 u = _mm256_mul_ps(b, b); - const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, - _mm256_set1_ps(0x1.573e2ep-5f)), u, - _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, - _mm256_set1_ps(0x1.fffdb6p-2f))), - u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) - return _mm256_fmadd_ps(j, k, k); - const __m256i g = _mm256_and_si256( - _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), - _mm256_set1_epi32(0x82000000u)); - const __m256 s1 = - _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); - const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); - const __m256i d = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(192), _CMP_GT_OQ)); - return _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), - _mm256_andnot_ps( - _mm256_castsi256_ps(d), - _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(c), - _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), - _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m256 ggml_v_silu(__m256 x) { - const __m256 one = _mm256_set1_ps(1); - const __m256 zero = _mm256_setzero_ps(); - const __m256 neg_x = _mm256_sub_ps(zero, x); - const __m256 exp_neg_x = ggml_v_expf(neg_x); - const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); - return _mm256_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON - -#if defined(__FMA__) -#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) -#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) -#else -#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) -#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) -#endif - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m128 ggml_v_expf(__m128 x) { - const __m128 r = _mm_set1_ps(0x1.8p23f); - const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); - const __m128 n = _mm_sub_ps(z, r); - const __m128 b = - NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); - const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); - const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); - const __m128i c = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); - const __m128 u = _mm_mul_ps(b, b); - const __m128 j = - MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, - MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), - u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm_movemask_epi8(c)) - return MADD128(j, k, k); - const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), - _mm_set1_epi32(0x82000000u)); - const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); - const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); - const __m128i d = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); - return _mm_or_ps( - _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), - _mm_andnot_ps(_mm_castsi128_ps(d), - _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), - _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m128 ggml_v_silu(__m128 x) { - const __m128 one = _mm_set1_ps(1); - const __m128 zero = _mm_setzero_ps(); - const __m128 neg_x = _mm_sub_ps(zero, x); - const __m128 exp_neg_x = ggml_v_expf(neg_x); - const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); - return _mm_div_ps(x, one_plus_exp_neg_x); -} - -#endif // __ARM_NEON / __AVX2__ / __SSE2__ - -static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - int i = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); - } -#endif - for (; i < n; ++i) { - y[i] = ggml_silu_f32(x[i]); - } -} - -static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { - int i = 0; - ggml_float sum = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), - _mm512_set1_ps(max))); - _mm512_storeu_ps(y + i, val); - sum += (ggml_float)_mm512_reduce_add_ps(val); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), - _mm256_set1_ps(max))); - _mm256_storeu_ps(y + i, val); - __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), - _mm256_castps256_ps128(val)); - val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); - val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); - sum += (ggml_float)_mm_cvtss_f32(val2); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), - _mm_set1_ps(max))); - _mm_storeu_ps(y + i, val); -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) - val = _mm_add_ps(val, _mm_movehl_ps(val, val)); - val = _mm_add_ss(val, _mm_movehdup_ps(val)); -#else - __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); - val = _mm_add_ps(val, tmp); - tmp = _mm_movehl_ps(tmp, val); - val = _mm_add_ss(val, tmp); -#endif - sum += (ggml_float)_mm_cvtss_f32(val); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), - vdupq_n_f32(max))); - vst1q_f32(y + i, val); - sum += (ggml_float)vaddvq_f32(val); - } -#endif - for (; i < n; ++i) { - float val = expf(x[i] - max); - sum += (ggml_float)val; - y[i] = val; - } - return sum; -} - -static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { - // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) - - int i = 0; - ggml_float sum = 0; - for (; i < n; ++i) { - float val = x[i] - max; - y[i] = val; - sum += (ggml_float)expf(val); - } - return sum = (ggml_float)logf(sum); -} - -inline static float ggml_silu_backward_f32(float x, float dy) { - const float s = 1.0f/(1.0f + expf(-x)); - return dy*s*(1.0f + x*(1.0f - s)); -} - -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - dx[i] = ggml_silu_backward_f32(x[i], dy[i]); - } -} - -inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -#else - vDSP_sve(x, 1, s, n); -#endif -} - -inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -} - -inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_FP16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_BF16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - float max = -INFINITY; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - } - *s = max; -#else - vDSP_maxv(x, 1, s, n); -#endif -} - -inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { - ggml_vec_norm_f32(n, s, x); - *s = 1.f/(*s); -} - -inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { - float max = -INFINITY; - int idx = 0; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - if (max == x[i]) { idx = i; } - } - *s = idx; -} - // // data types // @@ -3048,7 +954,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "WIN_UNPART", "GET_REL_POS", "ADD_REL_POS", - "RWKV_WKV", + "RWKV_WKV6", "UNARY", @@ -3143,7 +1049,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "win_unpart(x)", "get_rel_pos(x)", "add_rel_pos(x)", - "rwkv_wkv(k, v, r, tf, td, s)", + "rwkv_wkv6(k, v, r, tf, td, s)", "unary(x)", @@ -3191,215 +1097,6 @@ static_assert(GGML_UNARY_OP_COUNT == 14, "GGML_UNARY_OP_COUNT != 14"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); -// Helpers for polling loops -#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) -static inline void ggml_thread_cpu_relax(void) { - __asm__ volatile("yield" ::: "memory"); -} -#elif defined(__x86_64__) -static inline void ggml_thread_cpu_relax(void) { - _mm_pause(); -} -#else -static inline void ggml_thread_cpu_relax(void) {;} -#endif - -// -// NUMA support -// - -#define GGML_NUMA_MAX_NODES 8 -#define GGML_NUMA_MAX_CPUS 512 - -struct ggml_numa_node { - uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node - uint32_t n_cpus; -}; - -struct ggml_numa_nodes { - enum ggml_numa_strategy numa_strategy; - struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; - uint32_t n_nodes; - uint32_t total_cpus; // hardware threads on system - uint32_t current_node; // node on which main process is execting -#if defined(__gnu_linux__) - cpu_set_t cpuset; // cpuset from numactl -#else - uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype -#endif -}; - -// -// ggml state -// - -struct ggml_state { - struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; - struct ggml_numa_nodes numa; -}; - -// global state -static struct ggml_state g_state; -static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; - -// critical section via spin lock -inline static void ggml_critical_section_start(void) { - while (atomic_flag_test_and_set(&g_state_critical)) { - // spin - sched_yield(); - } -} - -static void ggml_barrier(struct ggml_threadpool * tp) { - int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); - if (n_threads == 1) { - return; - } - -#ifdef GGML_USE_OPENMP - #pragma omp barrier -#else - int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); - - // enter barrier (full seq-cst fence) - int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); - - if (n_barrier == (n_threads - 1)) { - // last thread - atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); - - // exit barrier (fill seq-cst fence) - atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); - return; - } - - // wait for other threads - while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { - ggml_thread_cpu_relax(); - } - - // exit barrier (full seq-cst fence) - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif -#endif -} - -// TODO: make this somehow automatically executed -// some sort of "sentry" mechanism -inline static void ggml_critical_section_end(void) { - atomic_flag_clear(&g_state_critical); -} - -#if defined(__gnu_linux__) -static cpu_set_t ggml_get_numa_affinity(void) { - cpu_set_t cpuset; - pthread_t thread; - thread = pthread_self(); - CPU_ZERO(&cpuset); - pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); - return cpuset; -} -#else -static uint32_t ggml_get_numa_affinity(void) { - return 0; // no NUMA support -} -#endif - -void ggml_numa_init(enum ggml_numa_strategy numa_flag) { - if (g_state.numa.n_nodes > 0) { - fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); - - return; - } - -#if defined(__gnu_linux__) - struct stat st; - char path[256]; - int rv; - - // set numa scheme - g_state.numa.numa_strategy = numa_flag; - - GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); - - g_state.numa.cpuset = ggml_get_numa_affinity(); - - // enumerate nodes - while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.n_nodes; - } - - // enumerate CPUs - while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.total_cpus; - } - - GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); - - // figure out which node we're on - uint current_cpu; - int getcpu_ret = 0; -#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__) - getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); -#else - // old glibc doesn't have a wrapper for this call. Fall back on direct syscall -# if !defined(SYS_getcpu) && defined(SYS_get_cpu) -# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name -# endif - getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); -#endif - - if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { - g_state.numa.n_nodes = 0; - return; - } - - GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); - - for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { - struct ggml_numa_node * node = &g_state.numa.nodes[n]; - GGML_PRINT_DEBUG("CPUs on node %u:", n); - node->n_cpus = 0; - for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) == 0) { - node->cpus[node->n_cpus++] = c; - GGML_PRINT_DEBUG(" %u", c); - } - } - GGML_PRINT_DEBUG("\n"); - } - - if (ggml_is_numa()) { - FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); - if (fptr != NULL) { - char buf[42]; - if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { - GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); - } - fclose(fptr); - } - } -#else - UNUSED(numa_flag); - // TODO -#endif -} - -bool ggml_is_numa(void) { - return g_state.numa.n_nodes > 1; -} //////////////////////////////////////////////////////////////////////////////// @@ -3435,7 +1132,7 @@ int64_t ggml_nrows(const struct ggml_tensor * tensor) { size_t ggml_nbytes(const struct ggml_tensor * tensor) { size_t nbytes; - size_t blck_size = ggml_blck_size(tensor->type); + const size_t blck_size = ggml_blck_size(tensor->type); if (blck_size == 1) { nbytes = ggml_type_size(tensor->type); for (int i = 0; i < GGML_MAX_DIMS; ++i) { @@ -3536,22 +1233,6 @@ int ggml_n_dims(const struct ggml_tensor * tensor) { return 1; } -static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return (t0->ne[0] == t1->ne[0]) && - (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable - (t1->ne[3]%t0->ne[3] == 0); -} - -static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return (t0->ne[1] == t1->ne[1]) && - (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable - (t1->ne[3]%t0->ne[3] == 0); -} - enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { enum ggml_type wtype = GGML_TYPE_COUNT; @@ -3698,167 +1379,35 @@ static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const str return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); } -static inline int ggml_up32(int n) { - return (n + 31) & ~31; -} - -//static inline int ggml_up64(int n) { -// return (n + 63) & ~63; -//} - -static inline int ggml_up(int n, int m) { - // assert m is a power of 2 - GGML_ASSERT((m & (m - 1)) == 0); - return (n + m - 1) & ~(m - 1); -} - // assert that pointer is aligned to GGML_MEM_ALIGN #define GGML_ASSERT_ALIGNED(ptr) \ GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// -#if defined(__ARM_ARCH) - -#if defined(__linux__) && defined(__aarch64__) -#include -#elif defined(__APPLE__) -#include -#endif - -#if !defined(HWCAP2_I8MM) -#define HWCAP2_I8MM 0 -#endif - -static void ggml_init_arm_arch_features(void) { -#if defined(__linux__) && defined(__aarch64__) - uint32_t hwcap = getauxval(AT_HWCAP); - uint32_t hwcap2 = getauxval(AT_HWCAP2); - - ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); - ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); - ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); -#endif -#elif defined(__APPLE__) - int oldp = 0; - size_t size = sizeof(oldp); - if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_neon = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_i8mm = oldp; - - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#else -// Run-time CPU feature detection not implemented for this platform, fallback to compile time -#if defined(__ARM_NEON) - ggml_arm_arch_features.has_neon = 1; -#else - ggml_arm_arch_features.has_neon = 0; -#endif - -#if defined(__ARM_FEATURE_MATMUL_INT8) - ggml_arm_arch_features.has_i8mm = 1; -#else - ggml_arm_arch_features.has_i8mm = 0; -#endif - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.has_sve = 1; - ggml_arm_arch_features.sve_cnt = 16; -#else - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#endif -#endif -} -#endif - struct ggml_context * ggml_init(struct ggml_init_params params) { - // make this function thread safe - ggml_critical_section_start(); - static bool is_first_call = true; + ggml_critical_section_start(); + if (is_first_call) { // initialize time system (required on Windows) ggml_time_init(); - // initialize GELU, Quick GELU, SILU and EXP F32 tables - { - const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - - for (int i = 0; i < (1 << 16); ++i) { - union { - uint16_t u16; - ggml_fp16_t fp16; - } u = {i}; - float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); - ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); - ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - } - - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - - GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); + for (int i = 0; i < (1 << 16); ++i) { + union { + uint16_t u16; + ggml_fp16_t fp16; + } u = {i}; + ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); } - // initialize g_state - { - const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - - g_state = (struct ggml_state) { - /*.contexts =*/ { { 0 } }, - /*.numa =*/ { - .n_nodes = 0, - .total_cpus = 0, - }, - }; - - for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { - g_state.contexts[i].used = false; - } - - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - - GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); - } - -#if defined(__ARM_ARCH) - ggml_init_arm_arch_features(); -#endif - is_first_call = false; } - // find non-used context in g_state - struct ggml_context * ctx = NULL; + ggml_critical_section_end(); - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (!g_state.contexts[i].used) { - g_state.contexts[i].used = true; - ctx = &g_state.contexts[i].context; - - GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); - break; - } - } - - if (ctx == NULL) { - GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); - - ggml_critical_section_end(); - - return NULL; - } + struct ggml_context * ctx = GGML_MALLOC(sizeof(struct ggml_context)); // allow to call ggml_init with 0 size if (params.mem_size == 0) { @@ -3869,15 +1418,12 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { *ctx = (struct ggml_context) { /*.mem_size =*/ mem_size, - /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), + /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, - /*.no_alloc_save =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, - /*.scratch =*/ { 0, 0, NULL, }, - /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); @@ -3886,56 +1432,35 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: context initialized\n", __func__); - ggml_critical_section_end(); - return ctx; } +void ggml_reset(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + + ctx->n_objects = 0; + ctx->objects_begin = NULL; + ctx->objects_end = NULL; +} + void ggml_free(struct ggml_context * ctx) { if (ctx == NULL) { return; } - // make this function thread safe - ggml_critical_section_start(); - - bool found = false; - - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (&g_state.contexts[i].context == ctx) { - g_state.contexts[i].used = false; - - GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", - __func__, i, ggml_used_mem(ctx)); - - if (ctx->mem_buffer_owned) { - GGML_ALIGNED_FREE(ctx->mem_buffer); - } - - found = true; - break; - } + if (ctx->mem_buffer_owned) { + ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); } - if (!found) { - GGML_PRINT_DEBUG("%s: context not found\n", __func__); - } - - ggml_critical_section_end(); + GGML_FREE(ctx); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; } -size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { - const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; - - ctx->scratch = scratch; - - return result; -} - bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; } @@ -3963,27 +1488,6 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { return max_size; } -// IMPORTANT: -// when creating "opt" tensors, always save and load the scratch buffer -// this is an error prone process, but it is necessary to support inplace -// operators when using scratch buffers -// TODO: implement a better way -static void ggml_scratch_save(struct ggml_context * ctx) { - // this is needed to allow opt tensors to store their data - // TODO: again, need to find a better way - ctx->no_alloc_save = ctx->no_alloc; - ctx->no_alloc = false; - - ctx->scratch_save = ctx->scratch; - ctx->scratch.data = NULL; -} - -static void ggml_scratch_load(struct ggml_context * ctx) { - ctx->no_alloc = ctx->no_alloc_save; - - ctx->scratch = ctx->scratch_save; -} - //////////////////////////////////////////////////////////////////////////////// static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { @@ -4003,7 +1507,9 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); - assert(false); +#ifndef NDEBUG + GGML_ABORT("not enough space in the context's memory pool"); +#endif return NULL; } @@ -4062,29 +1568,13 @@ static struct ggml_tensor * ggml_new_tensor_impl( size_t obj_alloc_size = 0; if (view_src == NULL && !ctx->no_alloc) { - if (ctx->scratch.data != NULL) { - // allocate tensor data in the scratch buffer - if (ctx->scratch.offs + data_size > ctx->scratch.size) { - GGML_LOG_WARN("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", - __func__, ctx->scratch.offs + data_size, ctx->scratch.size); - assert(false); - return NULL; - } - - data = (char * const) ctx->scratch.data + ctx->scratch.offs; - - ctx->scratch.offs += data_size; - } else { - // allocate tensor data in the context's memory pool - obj_alloc_size = data_size; - } + // allocate tensor data in the context's memory pool + obj_alloc_size = data_size; } struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); GGML_ASSERT(obj_new); - // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here - struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); #ifdef __clang__ @@ -4102,14 +1592,13 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.op =*/ GGML_OP_NONE, /*.op_params =*/ { 0 }, /*.flags =*/ 0, - /*.grad =*/ NULL, /*.src =*/ { NULL }, /*.view_src =*/ view_src, /*.view_offs =*/ view_offs, /*.data =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data, /*.name =*/ { 0 }, /*.extra =*/ NULL, - ///*.padding =*/ { 0 }, + /*.padding =*/ { 0 }, }; #ifdef __clang__ @@ -4179,191 +1668,16 @@ struct ggml_tensor * ggml_new_tensor_4d( return ggml_new_tensor(ctx, type, 4, ne); } -struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { - ggml_scratch_save(ctx); +void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes) { + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, nbytes); - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - - ggml_scratch_load(ctx); - - ggml_set_i32(result, value); - - return result; -} - -struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { - ggml_scratch_save(ctx); - - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); - - ggml_scratch_load(ctx); - - ggml_set_f32(result, value); - - return result; + return (uint8_t *)ctx->mem_buffer + obj->offs; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor(ctx, src->type, GGML_MAX_DIMS, src->ne); } -static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { - GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings - assert(params_size <= GGML_MAX_OP_PARAMS); - memcpy(tensor->op_params, params, params_size); -} - -static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - return ((const int32_t *)(tensor->op_params))[i]; -} - -static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); - return ((const float *)(tensor->op_params))[i]; -} - -static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - ((int32_t *)(tensor->op_params))[i] = value; -} - -static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); - ((float *)(tensor->op_params))[i] = value; -} - -struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { - if (ggml_is_empty(tensor)) { - return tensor; - } - if (tensor->buffer) { - ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor)); - } else { - GGML_ASSERT(tensor->data); - memset(tensor->data, 0, ggml_nbytes(tensor)); - } - return tensor; -} - -struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - -struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_bf16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) { const int64_t ne2 = tensor->ne[2]; const int64_t ne1 = tensor->ne[1]; @@ -4388,280 +1702,6 @@ void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * } } -int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } @@ -5215,6 +2255,7 @@ struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(ggml_is_matrix(a)); + GGML_ASSERT(a->ne[0] <= INT32_MAX); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]); @@ -5636,6 +2677,14 @@ struct ggml_tensor * ggml_group_norm_inplace( // ggml_mul_mat +static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[0] == t1->ne[0]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, @@ -5705,6 +2754,14 @@ struct ggml_tensor * ggml_mul_mat_id( // ggml_out_prod +static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[1] == t1->ne[1]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + struct ggml_tensor * ggml_out_prod( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6568,6 +3625,22 @@ struct ggml_tensor * ggml_rope_custom_inplace( ); } +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims( + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); +} + // ggml_rope_back struct ggml_tensor * ggml_rope_back( @@ -7066,6 +4139,7 @@ struct ggml_tensor * ggml_argsort( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_sort_order order) { + GGML_ASSERT(a->ne[0] <= INT32_MAX); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne); ggml_set_op_params_i32(result, 0, (int32_t) order); @@ -7121,8 +4195,6 @@ struct ggml_tensor * ggml_flash_attn_ext( GGML_ASSERT(mask); } - bool is_node = false; - // permute(0, 2, 1, 3) int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); @@ -7130,8 +4202,7 @@ struct ggml_tensor * ggml_flash_attn_ext( float params[] = { scale, max_bias, logit_softcap }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_FLASH_ATTN_EXT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_FLASH_ATTN_EXT; result->src[0] = q; result->src[1] = k; result->src[2] = v; @@ -7150,6 +4221,15 @@ void ggml_flash_attn_ext_set_prec( ggml_set_op_params_i32(a, 3, prec_i32); // scale is on first pos, max_bias on second } +enum ggml_prec ggml_flash_attn_ext_get_prec( + const struct ggml_tensor * a) { + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + + const int32_t prec_i32 = ggml_get_op_params_i32(a, 3); + + return (enum ggml_prec) prec_i32; +} + // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( @@ -7190,14 +4270,6 @@ struct ggml_tensor * ggml_flash_attn_back( GGML_ASSERT(ne2 % kvne2 == 0); - bool is_node = false; - - if (q->grad || k->grad || v->grad) { - // when using this operation (in backwards pass) these grads are set. - // we don't want to create (big) grad of our result, so is_node is false. - is_node = false; - } - // store gradients of q, k and v as continuous tensors concatenated in result. // note: v and gradv are actually transposed, i.e. v->ne[0] != D. const int64_t elem_q = ggml_nelements(q); @@ -7220,8 +4292,7 @@ struct ggml_tensor * ggml_flash_attn_back( int32_t masked_i = masked ? 1 : 0; ggml_set_op_params(result, &masked_i, sizeof(masked_i)); - result->op = GGML_OP_FLASH_ATTN_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_FLASH_ATTN_BACK; result->src[0] = q; result->src[1] = k; result->src[2] = v; @@ -7245,6 +4316,7 @@ struct ggml_tensor * ggml_ssm_conv( const int64_t n_s = sx->ne[2]; // TODO: maybe support other strides than 1? + // FIXME: this is always true? GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t); GGML_ASSERT(sx->ne[1] == d_inner); GGML_ASSERT(n_t >= 0); @@ -7424,9 +4496,9 @@ struct ggml_tensor * ggml_add_rel_pos_inplace( return ggml_add_rel_pos_impl(ctx, a, pw, ph, true); } -// ggml_rwkv_wkv +// ggml_rwkv_wkv6 -struct ggml_tensor * ggml_rwkv_wkv( +struct ggml_tensor * ggml_rwkv_wkv6( struct ggml_context * ctx, struct ggml_tensor * k, struct ggml_tensor * v, @@ -7458,7 +4530,7 @@ struct ggml_tensor * ggml_rwkv_wkv( const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_RWKV_WKV; + result->op = GGML_OP_RWKV_WKV6; result->src[0] = k; result->src[1] = v; result->src[2] = r; @@ -7676,11 +4748,6 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32( } // ggml_map_custom1 -struct ggml_map_custom1_op_params { - ggml_custom1_op_t fun; - int n_tasks; - void * userdata; -}; static struct ggml_tensor * ggml_map_custom1_impl( struct ggml_context * ctx, @@ -7726,12 +4793,6 @@ struct ggml_tensor * ggml_map_custom1_inplace( // ggml_map_custom2 -struct ggml_map_custom2_op_params { - ggml_custom2_op_t fun; - int n_tasks; - void * userdata; -}; - static struct ggml_tensor * ggml_map_custom2_impl( struct ggml_context * ctx, struct ggml_tensor * a, @@ -7780,12 +4841,6 @@ struct ggml_tensor * ggml_map_custom2_inplace( // ggml_map_custom3 -struct ggml_map_custom3_op_params { - ggml_custom3_op_t fun; - int n_tasks; - void * userdata; -}; - static struct ggml_tensor * ggml_map_custom3_impl( struct ggml_context * ctx, struct ggml_tensor * a, @@ -7879,9706 +4934,30 @@ struct ggml_tensor * ggml_opt_step_adamw( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * grad, - float alpha, - float beta1, - float beta2, - float eps, - float wd) { + struct ggml_tensor * m, + struct ggml_tensor * v, + struct ggml_tensor * adamw_params) { GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM); GGML_ASSERT(ggml_are_same_shape(a, grad)); - GGML_ASSERT(alpha > 0.0f); - GGML_ASSERT(beta1 >= 0.0f && beta1 <= 1.0f); - GGML_ASSERT(beta2 >= 0.0f && beta2 <= 1.0f); - GGML_ASSERT(eps >= 0.0f); - GGML_ASSERT(wd >= 0.0f && wd <= 1.0f); + GGML_ASSERT(ggml_are_same_shape(a, m)); + GGML_ASSERT(ggml_are_same_shape(a, v)); + GGML_ASSERT(adamw_params->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); struct ggml_tensor * result = ggml_view_tensor(ctx, a); - const int64_t iter = 1; - memcpy(&result->op_params[0], &iter, sizeof(int64_t)); - ggml_set_op_params_f32(result, 2, alpha); - ggml_set_op_params_f32(result, 3, beta1); - ggml_set_op_params_f32(result, 4, beta2); - ggml_set_op_params_f32(result, 5, eps); - ggml_set_op_params_f32(result, 6, wd); - result->op = GGML_OP_OPT_STEP_ADAMW; result->src[0] = a; result->src[1] = grad; - result->src[2] = ggml_dup_tensor(ctx, grad); - result->src[3] = ggml_dup_tensor(ctx, grad); + result->src[2] = m; + result->src[3] = v; + result->src[4] = adamw_params; return result; } //////////////////////////////////////////////////////////////////////////////// -// ggml_compute_forward_dup - -static void ggml_compute_forward_dup_same_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(src0->type == dst->type); - - const size_t nb0 = ggml_type_size(src0->type); - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by elements - const int ne = ggml_nelements(dst); - const int dr = (ne + nth - 1) / nth; - const int ie0 = dr * ith; - const int ie1 = MIN(ie0 + dr, ne); - - if (ie0 < ie1) { - memcpy( - ((char *) dst->data + ie0*nb0), - ((char *) src0->data + ie0*nb0), - (ie1 - ie0) * nb0); - } -} - -static void ggml_compute_forward_dup_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_fp16_t)) { - if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_bf16_t)) { - if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - // TODO: simplify - if (nb00 == sizeof(float)) { - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - quantize_row_q(src0_ptr, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(float)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. -static void ggml_compute_forward_dup_bytes( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(src0->type == dst->type); - - GGML_TENSOR_UNARY_OP_LOCALS; - - if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { - ggml_compute_forward_dup_same_cont(params, dst); - return; - } - - const size_t type_size = ggml_type_size(src0->type); - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == type_size && nb0 == type_size) { - // copy by rows - const size_t rs = ne00 * type_size; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - size_t id = 0; - char * dst_ptr = (char *) dst->data; - const size_t rs = ne00 * type_size; - - if (nb00 == type_size) { - // src0 is contigous on first dimension, copy by rows - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, type_size); - - id += type_size; - } - } - id += rs * (ne01 - ir1); - } - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, type_size); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } -} - -static void ggml_compute_forward_dup( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (src0->type == dst->type) { - ggml_compute_forward_dup_bytes(params, dst); - return; - } - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_dup_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_dup_bf16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_dup_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add - -static void ggml_compute_forward_add_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_add_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_F16); - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_F16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_BF16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_fp16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_bf16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - const enum ggml_type dtype = dst->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - ggml_from_float_t const quantize_row_q = type_traits[dtype].from_float; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - // src1 and dst are same shape as src0 => same indices - const int i13 = i03; - const int i12 = i02; - const int i11 = i01; - - const int i3 = i03; - const int i2 = i02; - const int i1 = i01; - - void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); - void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - assert(ne00 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne00); - // add src1 - ggml_vec_acc_f32(ne00, wdata, src1_row); - // quantize row to dst - if (quantize_row_q != NULL) { - quantize_row_q(wdata, dst_row, ne00); - } else { - memcpy(dst_row, wdata, ne0*nb0); - } - } -} - -static void ggml_compute_forward_add( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add1 - -static void ggml_compute_forward_add1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_add1_f32); - - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, - (float *) ((char *) src1->data), 0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, - ne0); -#else - ggml_vec_add1_f32(ne0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), - *(float *) src1->data); -#endif - } -} - -static void ggml_compute_forward_add1_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - ggml_from_float_t const quantize_row_q = type_traits[type].from_float; - - // we don't support permuted src0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(dst->type == src0->type); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); - void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); - - assert(ne0 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne0); - // add src1 - ggml_vec_acc1_f32(ne0, wdata, v); - // quantize row to dst - quantize_row_q(wdata, dst_row, ne0); - } -} - -static void ggml_compute_forward_add1_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add1_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add1_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add1_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add1_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_acc - -static void ggml_compute_forward_acc_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during acc - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during acc - const size_t nb0 = ggml_element_size(src0); - - const size_t nb00 = nb0; - const size_t nb01 = nb1; - const size_t nb02 = nb2; - const size_t nb03 = nb3; - - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - -#ifdef GGML_USE_ACCELERATE - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); -#else - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); -#endif - } -} - -static void ggml_compute_forward_acc( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_acc_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sub - -static void ggml_compute_forward_sub_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_sub( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sub_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul - -static void ggml_compute_forward_mul_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0 ; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_mul_f32); - - vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_mul( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mul_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_div - -static void ggml_compute_forward_div_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_div_f32); - - vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_div( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_div_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqr - -static void ggml_compute_forward_sqr_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqr_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqr( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqr_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqrt - -static void ggml_compute_forward_sqrt_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqrt_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqrt( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqrt_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_log - -static void ggml_compute_forward_log_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_log_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_log( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_log_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sin - -static void ggml_compute_forward_sin_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sin_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sin( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sin_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cos - -static void ggml_compute_forward_cos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_cos_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_cos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum - -static void ggml_compute_forward_sum_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - ggml_float sum = 0; - ggml_float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32_ggf(ne00, - &row_sum, - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - sum += row_sum; - } - } - } - ((float *) dst->data)[0] = sum; -} - -static void ggml_compute_forward_sum_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_fp16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f16_ggf(ne00, - &row_sum, - (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); -} - -static void ggml_compute_forward_sum_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_bf16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_bf16_ggf(ne00, - &row_sum, - (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); -} - -static void ggml_compute_forward_sum( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_sum_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_sum_bf16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum_rows - -static void ggml_compute_forward_sum_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne0 == 1); - GGML_ASSERT(ne1 == ne01); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - for (int64_t i3 = 0; i3 < ne03; i3++) { - for (int64_t i2 = 0; i2 < ne02; i2++) { - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); - float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); - float row_sum = 0; - ggml_vec_sum_f32(ne00, &row_sum, src_row); - dst_row[0] = row_sum; - } - } - } -} - -static void ggml_compute_forward_sum_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mean - -static void ggml_compute_forward_mean_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - assert(ne0 == 1); - assert(ne1 == ne01); - assert(ne2 == ne02); - assert(ne3 == ne03); - - UNUSED(ne0); - UNUSED(ne1); - UNUSED(ne2); - UNUSED(ne3); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32(ne00, - (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - - *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; - } - } - } -} - -static void ggml_compute_forward_mean( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mean_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argmax - -static void ggml_compute_forward_argmax_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - assert(dst->nb[0] == sizeof(float)); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - - const size_t nb01 = src0->nb[1]; - const size_t nb0 = dst->nb[0]; - - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src = (float *) ((char *) src0->data + i1*nb01); - int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); - int v = 0; - ggml_vec_argmax_f32(ne00, &v, src); - dst_[0] = v; - } -} - -static void ggml_compute_forward_argmax( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argmax_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_count_equal - -static void ggml_compute_forward_count_equal_i32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(src0->type == GGML_TYPE_I32); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_I64); - - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - int64_t * sums = (int64_t *) params->wdata; - int64_t sum_thread = 0; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir / (ne02*ne01); - const int64_t i02 = (ir - i03*ne03) / ne01; - const int64_t i01 = ir - i03*ne03 - i02*ne02; - - const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; - const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; - - for (int64_t i00 = 0; i00 < ne00; ++i00) { - const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); - const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); - - sum_thread += val0 == val1; - } - } - if (ith != 0) { - sums[ith] = sum_thread; - } - ggml_barrier(params->threadpool); - - if (ith != 0) { - return; - } - - for (int ith_other = 1; ith_other < nth; ++ith_other) { - sum_thread += sums[ith_other]; - } - *((int64_t *) dst->data) = sum_thread; -} - -static void ggml_compute_forward_count_equal( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_I32: - { - ggml_compute_forward_count_equal_i32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat - -static void ggml_compute_forward_repeat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_cpy_f32(ne00, - (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), - (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); - ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); - // ggml_vec_cpy_f16(ne00, y, x) - for (int i = 0; i < ne00; ++i) { - y[i] = x[i]; - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_I16: - { - ggml_compute_forward_repeat_f16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_repeat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat_back - -static void ggml_compute_forward_repeat_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(dst, src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne00/ne0); - const int nr1 = (int)(ne01/ne1); - const int nr2 = (int)(ne02/ne2); - const int nr3 = (int)(ne03/ne3); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (ggml_is_contiguous(dst)) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } else { - for (int k3 = 0; k3 < ne3; k3++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int k1 = 0; k1 < ne1; k1++) { - ggml_vec_set_f32(ne0, - (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), - 0); - } - } - } - } - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne3; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne1; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_acc_f32(ne0, - (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), - (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_repeat_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_concat - -static void ggml_compute_forward_concat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const float * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_concat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_abs - -static void ggml_compute_forward_abs_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_abs_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_abs( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_abs_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sgn - -static void ggml_compute_forward_sgn_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sgn_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sgn( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sgn_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_neg - -static void ggml_compute_forward_neg_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_neg_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_neg( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_neg_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_step - -static void ggml_compute_forward_step_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_step_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_step( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_step_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_tanh - -static void ggml_compute_forward_tanh_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_tanh_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_tanh( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_tanh_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_elu - -static void ggml_compute_forward_elu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_elu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_elu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_elu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_relu - -static void ggml_compute_forward_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sigmoid - -static void ggml_compute_forward_sigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu - -static void ggml_compute_forward_gelu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu_quick - -static void ggml_compute_forward_gelu_quick_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_quick_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_quick( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_quick_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu - -static void ggml_compute_forward_silu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -// ggml_compute_forward_leaky_relu - -static void ggml_compute_forward_leaky_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_leaky_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); - } -} - -static void ggml_compute_forward_leaky_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_leaky_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu_back - -static void ggml_compute_forward_silu_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * grad = dst->src[1]; - - assert(ggml_is_contiguous_1(grad)); - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - assert(ggml_are_same_shape(src0, grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_backward_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1])), - (float *) ((char *) grad->data + i1*(grad->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -static void ggml_compute_forward_hardswish_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardswish_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} -static void ggml_compute_forward_hardswish( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardswish_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_hardsigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardsigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_hardsigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardsigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_exp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_exp_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_exp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_exp_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_norm - -static void ggml_compute_forward_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; - } - - float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_float sum2 = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sum2 += (ggml_float)(v*v); - } - - float variance = sum2/ne00; - const float scale = 1.0f/sqrtf(variance + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_rms_norm - -static void ggml_compute_forward_rms_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)(x[i00] * x[i00]); - } - - const float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - memcpy(y, x, ne00 * sizeof(float)); - // for (int i00 = 0; i00 < ne00; i00++) { - // y[i00] = x[i00]; - // } - - const float scale = 1.0f/sqrtf(mean + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_rms_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_rms_norm_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - // src1 is same shape as src0 => same indices - const int64_t i11 = i01; - const int64_t i12 = i02; - const int64_t i13 = i03; - - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); - - ggml_float sum_xx = 0.0; - ggml_float sum_xdz = 0.0; - - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum_xx += (ggml_float)(x[i00] * x[i00]); - sum_xdz += (ggml_float)(x[i00] * dz[i00]); - } - - //const float mean = (float)(sum_xx)/ne00; - const float mean_eps = (float)(sum_xx)/ne00 + eps; - const float sum_eps = (float)(sum_xx) + eps*ne00; - //const float mean_xdz = (float)(sum_xdz)/ne00; - // we could cache rms from forward pass to improve performance. - // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. - //const float rms = sqrtf(mean_eps); - const float rrms = 1.0f / sqrtf(mean_eps); - //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) - - { - // z = rms_norm(x) - // - // rms_norm(src0) = - // scale( - // src0, - // div( - // 1, - // sqrt( - // add( - // scale( - // sum( - // sqr( - // src0)), - // (1.0/N)), - // eps)))); - - // postorder: - // ## op args grad - // 00 param src0 grad[#00] - // 01 const 1 - // 02 sqr (#00) grad[#02] - // 03 sum (#02) grad[#03] - // 04 const 1/N - // 05 scale (#03, #04) grad[#05] - // 06 const eps - // 07 add (#05, #06) grad[#07] - // 08 sqrt (#07) grad[#08] - // 09 div (#01,#08) grad[#09] - // 10 scale (#00,#09) grad[#10] - // - // backward pass, given grad[#10] - // #10: scale - // grad[#00] += scale(grad[#10],#09) - // grad[#09] += sum(mul(grad[#10],#00)) - // #09: div - // grad[#08] += neg(mul(grad[#09], div(#09,#08))) - // #08: sqrt - // grad[#07] += mul(grad[#08], div(0.5, #08)) - // #07: add - // grad[#05] += grad[#07] - // #05: scale - // grad[#03] += scale(grad[#05],#04) - // #03: sum - // grad[#02] += repeat(grad[#03], #02) - // #02: - // grad[#00] += scale(mul(#00, grad[#02]), 2.0) - // - // substitute and simplify: - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#02] = repeat(grad[#03], #02) - // grad[#02] = repeat(scale(grad[#05],#04), #02) - // grad[#02] = repeat(scale(grad[#07],#04), #02) - // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) - // a = b*c + d*e - // a = b*c*f/f + d*e*f/f - // a = (b*c*f + d*e*f)*(1/f) - // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) - // a = (b + d*e/c)*c - // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms - // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms - // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms - // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms - // a = (dz + x*div(-mean_xdz,mean_eps))*rrms - // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) - // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - } - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // post-order: - // dx := x - // dx := scale(dx,-mean_xdz/mean_eps) - // dx := add(dx, dz) - // dx := scale(dx, rrms) - float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_vec_cpy_f32 (ne00, dx, x); - // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); - ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); - ggml_vec_acc_f32 (ne00, dx, dz); - ggml_vec_scale_f32(ne00, dx, rrms); - } - } - } -} - -static void ggml_compute_forward_rms_norm_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_norm - -static void ggml_compute_forward_group_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - // TODO: optimize - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - int n_channels = src0->ne[2]; - int n_groups = dst->op_params[0]; - int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i += nth) { - int start = i * n_channels_per_group; - int end = start + n_channels_per_group; - if (end > n_channels) { - end = n_channels; - } - int step = end - start; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - ggml_float sum = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sumr += (ggml_float)x[i00]; - } - sum += sumr; - } - } - const float mean = sum / (ne00 * ne01 * step); - - ggml_float sum2 = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sumr += (ggml_float)(v * v); - } - sum2 += sumr; - } - } - const float variance = sum2 / (ne00 * ne01 * step); - const float scale = 1.0f / sqrtf(variance + eps); - - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - ggml_vec_scale_f32(ne00, y, scale); - } - } - } - } -} - -static void ggml_compute_forward_group_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_group_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul_mat - -static void ggml_compute_forward_mul_mat_one_chunk( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const int64_t num_rows_per_vec_dot, - const int64_t ir0_start, - const int64_t ir0_end, - const int64_t ir1_start, - const int64_t ir1_end) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); - - // threads with no work simply yield (not sure if it helps) - if (ir0_start >= ir0_end || ir1_start >= ir1_end) { - return; - } - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; - - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; - - for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { - for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { - const int64_t i13 = (ir1 / (ne12 * ne1)); - const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; - const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); - - // broadcast src0 into src1 - const int64_t i03 = i13 / r3; - const int64_t i02 = i12 / r2; - - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; - - const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char*)wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12 + i13 * nb13)); - float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { - vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); - } - - for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { - memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); - } - } - } - } -} - -static void ggml_compute_forward_mul_mat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float; - ggml_from_float_to_mat_t const from_float_to_mat = type_traits[vec_dot_type].from_float_to_mat; - int64_t const vec_dot_num_rows = type_traits[type].nrows; - int64_t const matmul_num_cols = type_traits[type].ncols; - int64_t const blck_size_interleave = type_traits[type].blck_size_interleave; - ggml_gemv_t const gemv = type_traits[type].gemv; - ggml_gemm_t const gemm = type_traits[type].gemm; - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if GGML_USE_LLAMAFILE - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const bool src1_cont = ggml_is_contiguous(src1); - - if (src1_cont) { - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)src1->data + i12*nb12 + i13*nb13, - nb11/ggml_type_size(src1->type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - src1->type, - dst->type)) - goto UseGgmlGemm1; - return; - } -UseGgmlGemm1:; -#endif - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - int64_t i11_processed = 0; - if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { - for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { - from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - 4, ne10, blck_size_interleave); - } - i11_processed = ne11 - ne11 % 4; - } - for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - - if (ith == 0) { - // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); - } - - ggml_barrier(params->threadpool); - -#if GGML_USE_LLAMAFILE - if (src1->type != vec_dot_type) { - const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, - row_size/ggml_type_size(vec_dot_type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - vec_dot_type, - dst->type)) - goto UseGgmlGemm2; - return; - } -UseGgmlGemm2:; -#endif - - // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) - const int64_t nr0 = ne0; - - // This is the size of the rest of the dimensions of the result - const int64_t nr1 = ne1 * ne2 * ne3; - - // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t num_rows_per_vec_dot = vec_dot_num_rows; - // TODO: currently the mmla kernels support only even numbered rows/cols. - // this check can be removed once they are extended to support odd numbered rows/cols too - if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - num_rows_per_vec_dot = 1; - } - - // Now select a reasonable chunk size. - int chunk_size = 16; - - // We need to step up the size if it's small - if (nr0 == 1 || nr1 == 1) { - chunk_size = 64; - } - - // distribute the work across the inner or outer loop based on which one is larger - // The number of chunks in the 0/1 dim. - // CEIL(nr0/chunk_size) - int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; - int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - - // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. - // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 - // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. - if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { - // distribute the thread work across the inner or outer loop based on which one is larger - nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - } - - // The number of elements in each chunk - const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; - const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - - if ((ggml_n_dims(src0) == 2) && gemv) { - const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; - int64_t src0_start = (ith * ne01) / nth; - int64_t src0_end = ((ith + 1) * ne01) / nth; - src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; - src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; - if (src0_start >= src0_end) return; - - // If there are more than three rows in src1, use gemm; otherwise, use gemv. - if (gemm && (ne11 > 3)) { - gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, - (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); - } - for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { - gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, - (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, - src0_end - src0_start); - } - return; - } - - // The first chunk comes from our thread_id, the rest will get auto-assigned. - int current_chunk = ith; - - while (current_chunk < nchunk0 * nchunk1) { - const int64_t ith0 = current_chunk % nchunk0; - const int64_t ith1 = current_chunk / nchunk0; - - const int64_t ir0_start = dr0 * ith0; - const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - - const int64_t ir1_start = dr1 * ith1; - const int64_t ir1_end = MIN(ir1_start + dr1, nr1); - - ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); - - if (nth >= nchunk0 * nchunk1) { - break; - } - - current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); - } -} - -// ggml_compute_forward_mul_mat_id - -static void ggml_compute_forward_mul_mat_id( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * ids = dst->src[2]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float; - int64_t const matmul_num_cols = type_traits[type].ncols; - ggml_gemv_t const gemv = type_traits[type].gemv; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // row groups - const int n_ids = ids->ne[0]; // n_expert_used - const int n_as = ne02; // n_expert - - char * wdata_src1_end = (src1->type == vec_dot_type) ? - (char *) params->wdata : - (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); - - struct mmid_row_mapping { - int32_t i1; - int32_t i2; - }; - - int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - -#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] - - if (ith == 0) { - // initialize matrix_row_counts - memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); - - // group rows by src0 matrix - for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { - for (int id = 0; id < n_ids; ++id) { - const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); - - assert(i02 >= 0 && i02 < n_as); - - MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; - matrix_row_counts[i02] += 1; - } - } - } - - ggml_barrier(params->threadpool); - - // compute each matrix multiplication in sequence - for (int cur_a = 0; cur_a < n_as; ++cur_a) { - const int64_t cne1 = matrix_row_counts[cur_a]; - - if (cne1 == 0) { - continue; - } - - const char * src0_cur = (const char *) src0->data + cur_a*nb02; - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = cne1; // src1 rows - - if (((ggml_n_dims(src0) - 1) == 2) && gemv) { - int64_t src0_cur_start = (ith * ne01) / nth; - int64_t src0_cur_end = ((ith + 1) * ne01) / nth; - src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; - src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; - if (src0_cur_start >= src0_cur_end) return; - - for (int ir1 = 0; ir1 < nr1; ir1++) { - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12)); - - gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, - (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); - } - continue; - } - - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - // threads with no work simply yield (not sure if it helps) - //if (ir010 >= ir011 || ir110 >= ir111) { - // sched_yield(); - // continue; - //} - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - // attempt to reduce false-sharing (does not seem to make a difference) - float tmp[16]; - - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { - const int64_t _i12 = ir1; // logical row index for this expert - - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11)*row_size - : (i11*nb11 + i12*nb12)); - - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); - } - - memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } - } - } - -#undef MMID_MATRIX_ROW -} - -// ggml_compute_forward_out_prod - -static void ggml_compute_forward_out_prod_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne3 == ne13); - GGML_ASSERT(ne03 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // block-tiling attempt - const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); - const int64_t blck_1 = 16; - - for (int64_t bir = ir0; bir < ir1; bir += blck_1) { - const int64_t bir1 = MIN(bir + blck_1, ir1); - for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { - const int64_t bne01 = MIN(bi01 + blck_0, ne01); - for (int64_t ir = bir; ir < bir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - -#if GGML_VEC_MAD_UNROLL > 2 - const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); - for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); - } - for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#else - for (int64_t i01 = bi01; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#endif - } - } - } -} - -static void ggml_compute_forward_out_prod_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 dim0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst dim0 cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int64_t ir = ir0; ir < ir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - - for (int64_t i01 = 0; i01 < ne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - dequantize_row_q(s0, wdata, ne0); - ggml_vec_mad_f32(ne0, d, wdata, *s1); - } - } -} - -static void ggml_compute_forward_out_prod( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_out_prod_q_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } - case GGML_TYPE_F32: - { - ggml_compute_forward_out_prod_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_scale - -static void ggml_compute_forward_scale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - // scale factor - float v; - memcpy(&v, dst->op_params, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const size_t nb01 = src0->nb[1]; - - const size_t nb1 = dst->nb[1]; - - for (int i1 = ir0; i1 < ir1; i1++) { - if (dst->data != src0->data) { - // src0 is same shape as dst => same indices - memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); - } - ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); - } -} - -static void ggml_compute_forward_scale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_scale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_set - -static void ggml_compute_forward_set_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during set - const size_t nb0 = ggml_element_size(src0); - - const int im0 = (ne10 == 0 ? 0 : ne10-1); - const int im1 = (ne11 == 0 ? 0 : ne11-1); - const int im2 = (ne12 == 0 ? 0 : ne12-1); - const int im3 = (ne13 == 0 ? 0 : ne13-1); - - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); - } -} - -static void ggml_compute_forward_set( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_set_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cpy - -static void ggml_compute_forward_cpy( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_cont - -static void ggml_compute_forward_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_reshape - -static void ggml_compute_forward_reshape( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_view - -static void ggml_compute_forward_view( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_permute - -static void ggml_compute_forward_permute( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_transpose - -static void ggml_compute_forward_transpose( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_get_rows - -static void ggml_compute_forward_get_rows_q( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == ggml_type_size(type)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - dequantize_row_q( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_fp16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_fp16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_bf16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_bf16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(float)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), - (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); - } -} - -static void ggml_compute_forward_get_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_get_rows_q(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rows_bf16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_get_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_get_rows_back - -static void ggml_compute_forward_get_rows_back_f32_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - for (int j = 0; j < nc; ++j) { - ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; - ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); - } - } -} - -static void ggml_compute_forward_get_rows_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) src0->data + i*src0->nb[1])); - } -} - -static void ggml_compute_forward_get_rows_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_back_f32_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_get_rows_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_diag - -static void ggml_compute_forward_diag_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - // TODO: handle transposed/permuted matrices - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne00 == ne0); - GGML_ASSERT(ne00 == ne1); - GGML_ASSERT(ne01 == 1); - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne3); - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = 0; i1 < ne1; i1++) { - float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); - for (int i0 = 0; i0 < i1; i0++) { - d[i0] = 0; - } - d[i1] = s[i1]; - for (int i0 = i1+1; i0 < ne0; i0++) { - d[i0] = 0; - } - } - } - } -} - -static void ggml_compute_forward_diag( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_diag_mask_inf - -static void ggml_compute_forward_diag_mask_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const float value) { - - const struct ggml_tensor * src0 = dst->src[0]; - - const int ith = params->ith; - const int nth = params->nth; - - const int n_past = ((int32_t *) dst->op_params)[0]; - const bool inplace = src0->data == dst->data; - - GGML_ASSERT(n_past >= 0); - - if (!inplace) { - if (ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - // TODO: handle transposed/permuted matrices - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - const int nr = src0->ne[1]; - const int nz = n/nr; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int k = 0; k < nz; k++) { - for (int j = ith; j < nr; j += nth) { - for (int i = n_past; i < nc; i++) { - if (i > n_past + j) { - *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; - } - } - } - } -} - -static void ggml_compute_forward_diag_mask_inf( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_diag_mask_zero( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, 0); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_soft_max - -static void ggml_compute_forward_soft_max_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous(dst)); - assert(ggml_are_same_shape(src0, dst)); - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - //const int64_t ne11 = src1 ? src1->ne[1] : 1; - - // TODO: is this supposed to be ceil instead of floor? - // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - for (int i1 = ir0; i1 < ir1; i1++) { - // ALiBi - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); - float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); - - // broadcast the mask across rows - ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - - ggml_vec_cpy_f32 (nc, wp, sp); - ggml_vec_scale_f32(nc, wp, scale); - if (mp_f32) { - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*mp_f32[i]; - } - } - } - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(wp[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, wp); - - ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(nc, dp, sum); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dp[i])); - assert(!isinf(dp[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_soft_max_back - -static void ggml_compute_forward_soft_max_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_are_same_shape(src1, dst)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); - float *y = (float *)((char *) src1->data + i1*src1->nb[1]); - float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(dy[i])); - assert(!isnan(y[i])); - } -#endif - // Jii = yi - yi*yi - // Jij = -yi*yj - // J = diag(y)-y.T*y - // dx = J * dy - // dxk = sum_i(Jki * dyi) - // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*dyk - // dxk = -yk * sum_i(yi * dyi) + yk*dyk - // dxk = -yk * dot(y, dy) + yk*dyk - // dxk = yk * (- dot(y, dy) + dyk) - // dxk = yk * (dyk - dot(y, dy)) - // - // post-order: - // dot_y_dy := dot(y, dy) - // dx := dy - // dx := dx - dot_y_dy - // dx := dx * y - - // linear runtime, no additional memory - float dot_y_dy = 0; - ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); - ggml_vec_cpy_f32 (nc, dx, dy); - ggml_vec_acc1_f32(nc, dx, -dot_y_dy); - ggml_vec_mul_f32 (nc, dx, dx, y); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dx[i])); - assert(!isinf(dx[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_clamp - -static void ggml_compute_forward_clamp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - for (int j = ith; j < n; j += nth) { - float * dst_ptr = (float *) ((char *) dst->data + j*nb1); - float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); - } - } -} - -static void ggml_compute_forward_clamp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_clamp_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / MAX(0.001f, high - low); - return 1 - MIN(1, MAX(0, y)); -} - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); - } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; -} - -// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get -// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { - return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); -} - -static void ggml_rope_cache_init( - float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale) { - // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py - float theta = theta_base; - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; - rope_yarn( - theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] - ); - cache[i0 + 1] *= sin_sign; - - theta *= theta_scale; - } -} - -void ggml_rope_yarn_corr_dims( - int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] -) { - // start and end correction dims - float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); - float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); - dims[0] = MAX(0, start); - dims[1] = MIN(n_dims - 1, end); -} - -static void ggml_compute_forward_rope_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb00 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[1]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -// TODO: deduplicate f16/f32 code -static void ggml_compute_forward_rope_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[1]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -static void ggml_compute_forward_rope( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, true); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, true); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope_back - -static void ggml_compute_forward_rope_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, false); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, false); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_conv_transpose_1d - -static void ggml_compute_forward_conv_transpose_1d_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); - ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // permute source data (src1) from (L x Cin) to (Cin x L) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - ggml_fp16_t * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne02, &v, 0, - (ggml_fp16_t *) wdata_src + i1n, 0, - (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); - float * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // prepare source data (src1) - { - float * const wdata = (float *) params->wdata + nk; - float * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = src[i10]; - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - float * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - float * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f32(ne02, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_transpose_1d_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_f32 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - - -// ggml_compute_forward_im2col_f16 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_im2col( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_im2col_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_im2col_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_back_f32 - -static void ggml_compute_forward_im2col_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne3 : ne2; - const int64_t IC = is_2D ? ne2 : ne1; - const int64_t IH = is_2D ? ne1 : 1; - const int64_t IW = ne0; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne12 : 1; - const int64_t OW = ne11; - - int ofs0 = is_2D ? nb3 : nb2; - int ofs1 = is_2D ? nb2 : nb1; - - GGML_ASSERT(nb0 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - for (int64_t iih = 0; iih < IH; iih++) { - for (int64_t iiw = 0; iiw < IW; iiw++) { - - // micro kernel - float grad = 0.0f; - for (int64_t ikh = 0; ikh < KH; ikh++) { - for (int64_t ikw = 0; ikw < KW; ikw++) { - // For s0 > 1 some values were skipped over in the forward pass. - // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. - const int64_t tmpw = (iiw + p0 - ikw*d0); - if (tmpw % s0 != 0) { - continue; - } - const int64_t iow = tmpw / s0; - - // Equivalent logic as above except for s1. - int64_t ioh; - if (is_2D) { - const int64_t tmph = iih + p1 - ikh*d1; - - if (tmph % s1 != 0) { - continue; - } - - ioh = tmph / s1; - } else { - ioh = 0; - } - - if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { - continue; - } - - const float * const src_data = (const float *) src1->data - + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; - } - } - float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] - dst_data[iih*IW + iiw] = grad; - } - } - } - } - } -} - -// ggml_compute_forward_conv_transpose_2d - -static void ggml_compute_forward_conv_transpose_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02*ne03; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); - ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; - for (int64_t i01 = 0; i01 < ne01; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; - } - } - } - } - } - - // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - for (int i12 = 0; i12 < ne12; i12++) { - for (int i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); - ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; - for (int i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - } - - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t stride = ggml_get_op_params_i32(dst, 0); - - // total patches in dst - const int np = ne2; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i2 = ip0; i2 < ip1; i2++) { // Cout - float * dst_data = (float *)((char *) dst->data + i2*nb2); - ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; - for (int i11 = 0; i11 < ne11; i11++) { - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i11*ne10*ne12 + i10*ne12; - for (int i01 = 0; i01 < ne01; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne03, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); - dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; - } - } - } - } - } -} - -// ggml_compute_forward_pool_1d_sk_p0 - -static void ggml_compute_forward_pool_1d_sk_p0( - const struct ggml_compute_params * params, - const enum ggml_op_pool op, - const int k, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const char * cdata = (const char *)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - float * drow = (float *)dst->data; - - const int64_t rs = dst->ne[0]; - - while (cdata < data_end) { - const void * srow = (const void *)cdata; - int j = 0; - for (int64_t i = 0; i < rs; ++i) { - switch (op) { - case GGML_OP_POOL_AVG: drow[i] = 0; break; - case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - for (int ki = 0; ki < k; ++ki) { - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: drow[i] += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - ++j; - } - switch (op) { - case GGML_OP_POOL_AVG: drow[i] /= k; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - - cdata += src->nb[1]; - drow += rs; - } -} - -// ggml_compute_forward_pool_1d - -static void ggml_compute_forward_pool_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int s0 = opts[2]; - const int p0 = opts[3]; - GGML_ASSERT(p0 == 0); // padding not supported - GGML_ASSERT(k0 == s0); // only s = k supported - - ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); -} - -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - const char * cdata = (const char*)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - - const int64_t px = dst->ne[0]; - const int64_t py = dst->ne[1]; - const int64_t pa = px * py; - - float * dplane = (float *)dst->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - float * const drow = dplane + oy * px; - for (int ox = 0; ox < px; ++ox) { - float * const out = drow + ox; - switch (op) { - case GGML_OP_POOL_AVG: *out = 0; break; - case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; - const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= src->ne[0]) continue; - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: *out += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - switch (op) { - case GGML_OP_POOL_AVG: *out /= ka; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - - cdata += src->nb[2]; - dplane += pa; - } -} - -// ggml_compute_forward_pool_2d_back - -static void ggml_compute_forward_pool_2d_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst - - assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - char * cdata = (char *) dst->data; - const char * cdataf = (const char *) dstf->data; - const char * const data_end = cdata + ggml_nbytes(dst); - - GGML_ASSERT(params->ith == 0); - memset(cdata, 0, ggml_nbytes(dst)); - - const int64_t px = src->ne[0]; - const int64_t py = src->ne[1]; - const int64_t pa = px * py; - - const float * splane = (const float *) src->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - const float * const srow = splane + oy * px; - for (int ox = 0; ox < px; ++ox) { - const float grad0 = srow[ox]; - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - if (op == GGML_OP_POOL_MAX) { - float maxval = -FLT_MAX; - int kxmax = -1; - int kymax = -1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - const float val = dst->type == GGML_TYPE_F32 ? - ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); - if (val <= maxval) { - continue; - } - - maxval = val; - kxmax = kx; - kymax = ky; - } - } - - if (kxmax == -1 || kymax == -1) { - continue; - } - - void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); - const int j = ix + kxmax; - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad0; - } else { - ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); - } - } else if (op == GGML_OP_POOL_AVG) { - const float grad = grad0 / ka; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad; - } else { - ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); - } - } - } - } else { - GGML_ASSERT(false); - } - } - } - - cdata += dst->nb[2]; - cdataf += dst->nb[2]; - splane += pa; - } -} - -// ggml_compute_forward_upscale - -static void ggml_compute_forward_upscale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - // TODO: optimize - - for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3 / sf3; - for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2 / sf2; - for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / sf1; - for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / sf0; - - const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_upscale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_upscale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_pad - -static void ggml_compute_forward_pad_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT( dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float * dst_ptr = (float *) dst->data; - - // TODO: optimize - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = ith; i1 < ne1; i1 += nth) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - for (int64_t i3 = 0; i3 < ne3; ++i3) { - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - - const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - dst_ptr[dst_idx] = *src_ptr; - } else { - dst_ptr[dst_idx] = 0; - } - } - } - } - } -} - -static void ggml_compute_forward_pad( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_pad_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_arange - -static void ggml_compute_forward_arange_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const float start = ggml_get_op_params_f32(dst, 0); - const float stop = ggml_get_op_params_f32(dst, 1); - const float step = ggml_get_op_params_f32(dst, 2); - - const int64_t steps = (int64_t) ceilf((stop - start) / step); - - GGML_ASSERT(ggml_nelements(dst) == steps); - - for (int64_t i = ith; i < steps; i+= nth) { - float value = start + step * i; - ((float *)dst->data)[i] = value; - } -} - -static void ggml_compute_forward_arange( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_arange_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_timestep_embedding_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const int dim = ggml_get_op_params_i32(dst, 0); - const int max_period = ggml_get_op_params_i32(dst, 1); - - int half = dim / 2; - - for (int64_t i = 0; i < ne00; i++) { - float * embed_data = (float *)((char *) dst->data + i*nb1); - for (int64_t j = ith; j < half; j += nth) { - float timestep = ((float *)src0->data)[i]; - float freq = (float)expf(-logf(max_period) * j / half); - float arg = timestep * freq; - embed_data[j] = cosf(arg); - embed_data[j + half] = sinf(arg); - } - if (dim % 2 != 0 && ith == 0) { - embed_data[dim] = 0.f; - } - } -} - -static void ggml_compute_forward_timestep_embedding( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_timestep_embedding_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argsort - -static void ggml_compute_forward_argsort_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); - - for (int64_t i = ith; i < nr; i += nth) { - int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); - const float * src_data = (float *)((char *) src0->data + i*nb01); - - for (int64_t j = 0; j < ne0; j++) { - dst_data[j] = j; - } - - // C doesn't have a functional sort, so we do a bubble sort instead - for (int64_t j = 0; j < ne0; j++) { - for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { - int32_t tmp = dst_data[j]; - dst_data[j] = dst_data[k]; - dst_data[k] = tmp; - } - } - } - } -} - -static void ggml_compute_forward_argsort( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argsort_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_ext - -static void ggml_compute_forward_flash_attn_ext_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne2 == N); - - // input tensor rows must be contiguous - GGML_ASSERT(nbq0 == ggml_type_size(q->type)); - GGML_ASSERT(nbk0 == ggml_type_size(k->type)); - GGML_ASSERT(nbv0 == ggml_type_size(v->type)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nev0 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // broadcast factors - const int64_t rk2 = neq2/nek2; - const int64_t rk3 = neq3/nek3; - - const int64_t rv2 = neq2/nev2; - const int64_t rv3 = neq3/nev3; - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float scale = 1.0f; - float max_bias = 0.0f; - float logit_softcap = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); - - if (logit_softcap != 0) { - scale /= logit_softcap; - } - - const uint32_t n_head = neq2; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - enum ggml_type const k_vec_dot_type = type_traits[k->type].vec_dot_type; - ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; - ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; - ggml_to_float_t const v_to_float = type_traits[v->type].to_float; - - // loop over n_batch and n_head - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - const uint32_t h = iq2; // head index - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float S = 0.0f; // sum - float M = -INFINITY; // maximum KQ value - - float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator - float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer - ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator - ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - - if (v->type == GGML_TYPE_F16) { - memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); - } else { - memset(VKQ32, 0, D*sizeof(float)); - } - - const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; - - // k indices - const int ik3 = iq3 / rk3; - const int ik2 = iq2 / rk2; - - // v indices - const int iv3 = iq3 / rv3; - const int iv2 = iq2 / rv2; - - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); - q_to_vec_dot(pq, Q_q, D); - - // online softmax / attention - // loop over n_kv and n_head_kv - // ref: https://arxiv.org/pdf/2112.05682.pdf - for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; - if (mv == -INFINITY) { - continue; - } - - float s; // KQ value - - const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); - kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - - s = s*scale; // scale KQ value - - if (logit_softcap != 0.0f) { - s = logit_softcap*tanhf(s); - } - - s += mv; // apply mask - - const float Mold = M; - - float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value - float vs = 1.0f; // post-softmax KQ value, expf(s - M) - - const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - - if (v->type == GGML_TYPE_F16) { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, VKQ16, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - // V += v*expf(s - M) - ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); - } else { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f32(D, VKQ32, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - v_to_float(v_data, V32, D); - - // V += v*expf(s - M) - ggml_vec_mad_f32(D, VKQ32, V32, vs); - } - - S = S*ms + vs; // scale and increment sum with partial sum - } - - if (v->type == GGML_TYPE_F16) { - for (int64_t d = 0; d < D; ++d) { - VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); - } - } - - // V /= S - const float S_inv = 1.0f/S; - ggml_vec_scale_f32(D, VKQ32, S_inv); - - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // original - //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); - - // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); - } -} - -static void ggml_compute_forward_flash_attn_ext( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - switch (dst->op_params[3]) { - case GGML_PREC_DEFAULT: - case GGML_PREC_F32: - { - // uses F32 accumulators - ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_back - -static void ggml_compute_forward_flash_attn_back_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - const struct ggml_tensor * d = dst->src[3]; - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ned, d, ne) - GGML_TENSOR_LOCALS(size_t, nbd, d, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - const int mxDM = MAX(D, Mup); - - // GGML_ASSERT(ne0 == D); - // GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned1 == N); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (ith == 0) { - memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); - } - ggml_barrier(params->threadpool); - - const int64_t elem_q = ggml_nelements(q); - const int64_t elem_k = ggml_nelements(k); - - enum ggml_type result_type = dst->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - void * grad_q = (char *) dst->data; - void * grad_k = (char *) dst->data + offs_k; - void * grad_v = (char *) dst->data + offs_v; - - const size_t nbgq1 = nb0*neq0; - const size_t nbgq2 = nb0*neq0*neq1; - const size_t nbgq3 = nb0*neq0*neq1*neq2; - - const size_t nbgk1 = nb0*nek0; - const size_t nbgk2 = nb0*nek0*nek1; - const size_t nbgk3 = nb0*nek0*nek1*neq2; - - const size_t nbgv1 = nb0*nev0; - const size_t nbgv2 = nb0*nev0*nev1; - const size_t nbgv3 = nb0*nev0*nev1*neq2; - - // parallelize by k rows using ggml_vec_dot_f32 - - // total rows in k - const int nr = nek2*nek3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - // how often k2 (and v2) is repeated in q2 - int nrep = neq2/nek2; - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int ik3 = ir/(nek2); - const int ik2 = ir - ik3*nek2; - - const int iq3 = ik3; - const int id3 = ik3; - const int iv3 = ik3; - const int iv2 = ik2; - - for (int irep = 0; irep < nrep; ++irep) { - const int iq2 = ik2 + irep*nek2; - const int id2 = iq2; - - // (ik2 + irep*nek2) % nek2 == ik2 - for (int iq1 = 0; iq1 < neq1; ++iq1) { - const int id1 = iq1; - - // not sure about CACHE_LINE_SIZE_F32.. - // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? - float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); - float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SM values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(SM, 1, &max, SM, 1, Mup); - vvexpf(SM, SM, &Mup); - ggml_vec_sum_f32(Mup, &sum, SM); -#else - sum = ggml_vec_soft_max_f32(Mup, SM, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, SM, sum); - - } - - // step-by-step explanation - { - // forward-process shape grads from backward process - // parallel_for ik2,ik3: - // for irep: - // iq2 = ik2 + irep*nek2 - // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] - // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] - // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] - // for iq1: - // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur - // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur - // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 - // S0 = -Inf [D,1,1,1] - // ~S1[i] = dot(kcur[:D,i], qcur) - // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale - // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) - // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur - // ~S5[i] = dot(vcur[:,i], S4) - // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] - // ~dst[i,iq1,iq2,iq3] = S5[i] ^ - // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] - // dst backward-/ grad[dst] = d - // - // output gradients with their dependencies: - // - // grad[kcur] = grad[S1].T @ qcur - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S4] = grad[S5] @ vcur - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[qcur] = grad[S1] @ kcur - // grad[vcur] = grad[S5].T @ S4 - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // in post-order: - // - // S1 = qcur @ kcur.T - // S2 = S1 * scale - // S3 = diag_mask_inf(S2, P) - // S4 = softmax(S3) - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[qcur] = grad[S1] @ kcur - // grad[kcur] = grad[S1].T @ qcur - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // using less variables (SM=S4): - // - // S = diag_mask_inf(qcur @ kcur.T * scale, P) - // SM = softmax(S) - // S = d[:D,iq1,iq2,iq3] @ vcur - // dot_SM_gradSM = dot(SM, S) - // S = SM * (S - dot(SM, S)) - // S = diag_mask_zero(S, P) * scale - // - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[k][:D,:M,ik2,ik3] += S.T @ qcur - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - } - - // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // for ic: - // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] - // exclude known future zero S[..] values from operation - ggml_vec_set_f32(masked_begin, S, 0); - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - S, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - - // S = SM * (S - dot(SM, S)) - float dot_SM_gradSM = 0; - ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); - ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); - ggml_vec_mul_f32 (masked_begin, S, S, SM); - - // S = diag_mask_zero(S, P) * scale - // already done by above ggml_vec_set_f32 - - // exclude known zero S[..] values from operation - ggml_vec_scale_f32(masked_begin, S, scale); - - // S shape [M,1] - // SM shape [M,1] - // kcur shape [D,M] - // qcur shape [D,1] - // vcur shape [M,D] - - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] - // for ic: - // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), - (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), - S[ic]); - } - - // grad[k][:D,:M,iq2,iq3] += S.T @ qcur - // for ic: - // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] - // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), - S[ic]); - } - - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - // for ic: - // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] - // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] - // exclude known zero SM[..] values from mad - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), - SM, - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - } - } - } -} - -static void ggml_compute_forward_flash_attn_back( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_back_f32(params, masked, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_conv - -static void ggml_compute_forward_ssm_conv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // conv_x - const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; // d_conv - const int ncs = src0->ne[0]; // d_conv - 1 + n_t - const int nr = src0->ne[1]; // d_inner - const int n_t = dst->ne[1]; // tokens per sequence - const int n_s = dst->ne[2]; // number of sequences in the batch - - GGML_ASSERT( dst->ne[0] == nr); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - // {d_conv - 1 + n_t, d_inner, n_seqs} - // sliding window - const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} - const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} - float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} - - // TODO: transpose the output for smaller strides for big batches? - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // rowwise dot product - // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision - float sumf = 0.0f; - - // d_conv - for (int i0 = 0; i0 < nc; ++i0) { - sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; - } - x[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_conv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_conv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_scan - -static void ggml_compute_forward_ssm_scan_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // s - const struct ggml_tensor * src1 = dst->src[1]; // x - const struct ggml_tensor * src2 = dst->src[2]; // dt - const struct ggml_tensor * src3 = dst->src[3]; // A - const struct ggml_tensor * src4 = dst->src[4]; // B - const struct ggml_tensor * src5 = dst->src[5]; // C - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nc = src0->ne[0]; // d_state - const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = src1->ne[1]; // number of tokens per sequence - const int64_t n_s = src0->ne[2]; // number of sequences in the batch - - GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src2->nb[0] == sizeof(float)); - GGML_ASSERT(src3->nb[0] == sizeof(float)); - GGML_ASSERT(src4->nb[0] == sizeof(float)); - GGML_ASSERT(src5->nb[0] == sizeof(float)); - // required for the dot product between s and C - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - // required for per-sequence offsets for states - GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); - // required to get correct offset for state destination (i.e. src1->nb[3]) - GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} - const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} - const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} - const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} - const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} - float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} - - // use the output as the source for the next token-wise iterations - if (i2 > 0) { s0 = s; } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 - float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; - float x_dt = x[i1] * dt_soft_plus; - float sumf = 0.0f; - // d_state - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - // state = prev_state * dA + dB * x - float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); - // y = rowwise_dotprod(state, C) - sumf += state * C[i0]; - s[i] = state; - } - y[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_scan( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_scan_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_part - -static void ggml_compute_forward_win_part_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t w = ((const int32_t *)(dst->op_params))[2]; - - assert(ne00 == ne0); - assert(ne3 == nep0*nep1); - - // TODO: optimize / multi-thread - for (int py = 0; py < nep1; ++py) { - for (int px = 0; px < nep0; ++px) { - const int64_t i3 = py*nep0 + px; - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i02 = py*w + i2; - const int64_t i01 = px*w + i1; - const int64_t i00 = i0; - - const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; - const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; - - if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { - ((float *) dst->data)[i] = 0.0f; - } else { - ((float *) dst->data)[i] = ((float *) src0->data)[j]; - } - } - } - } - } - } -} - -static void ggml_compute_forward_win_part( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_part_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_unpart - -static void ggml_compute_forward_win_unpart_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t w = ((const int32_t *)(dst->op_params))[0]; - - // padding - const int px = (w - ne1%w)%w; - //const int py = (w - ne2%w)%w; - - const int npx = (px + ne1)/w; - //const int npy = (py + ne2)/w; - - assert(ne0 == ne00); - - // TODO: optimize / multi-thread - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int ip2 = i2/w; - const int ip1 = i1/w; - - const int64_t i02 = i2%w; - const int64_t i01 = i1%w; - const int64_t i00 = i0; - - const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; - const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; - - ((float *) dst->data)[j] = ((float *) src0->data)[i]; - } - } - } -} - -static void ggml_compute_forward_win_unpart( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_unpart_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -//gmml_compute_forward_unary - -static void ggml_compute_forward_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const enum ggml_unary_op op = ggml_get_unary_op(dst); - - switch (op) { - case GGML_UNARY_OP_ABS: - { - ggml_compute_forward_abs(params, dst); - } break; - case GGML_UNARY_OP_SGN: - { - ggml_compute_forward_sgn(params, dst); - } break; - case GGML_UNARY_OP_NEG: - { - ggml_compute_forward_neg(params, dst); - } break; - case GGML_UNARY_OP_STEP: - { - ggml_compute_forward_step(params, dst); - } break; - case GGML_UNARY_OP_TANH: - { - ggml_compute_forward_tanh(params, dst); - } break; - case GGML_UNARY_OP_ELU: - { - ggml_compute_forward_elu(params, dst); - } break; - case GGML_UNARY_OP_RELU: - { - ggml_compute_forward_relu(params, dst); - } break; - case GGML_UNARY_OP_SIGMOID: - { - ggml_compute_forward_sigmoid(params, dst); - } break; - case GGML_UNARY_OP_GELU: - { - ggml_compute_forward_gelu(params, dst); - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - ggml_compute_forward_gelu_quick(params, dst); - } break; - case GGML_UNARY_OP_SILU: - { - ggml_compute_forward_silu(params, dst); - } break; - case GGML_UNARY_OP_HARDSWISH: - { - ggml_compute_forward_hardswish(params, dst); - } break; - case GGML_UNARY_OP_HARDSIGMOID: - { - ggml_compute_forward_hardsigmoid(params, dst); - } break; - case GGML_UNARY_OP_EXP: - { - ggml_compute_forward_exp(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_get_rel_pos - -static void ggml_compute_forward_get_rel_pos_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 - - GGML_TENSOR_UNARY_OP_LOCALS - - const int64_t w = ne1; - - ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; - ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - const int64_t pos = (w - i1 - 1) + i2; - for (int64_t i0 = 0; i0 < ne0; ++i0) { - dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; - } - } - } -} - -static void ggml_compute_forward_get_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rel_pos_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add_rel_pos - -static void ggml_compute_forward_add_rel_pos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; - if (!inplace) { - if (params->ith == 0) { - memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 - - float * src1_data = (float *) src1->data; - float * src2_data = (float *) src2->data; - float * dst_data = (float *) dst->data; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int ith = params->ith; - const int nth = params->nth; - - // total patches in dst - const int np = ne13; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - for (int64_t i13 = ip0; i13 < ip1; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t jp0 = jp1 + i10; - const float src1_e = src1_data[jp0]; - const float src2_e = src2_data[jp0]; - - const int64_t jdh = jp0 * ne10; - const int64_t jdw = jdh - (ne10 - 1) * i10; - - for (int64_t j = 0; j < ne10; ++j) { - dst_data[jdh + j ] += src2_e; - dst_data[jdw + j*ne10] += src1_e; - } - } - } - } - } -} - -static void ggml_compute_forward_add_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add_rel_pos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rwkv_wkv - -static void ggml_compute_forward_rwkv_wkv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const size_t T = dst->src[1]->ne[3]; - const size_t C = dst->ne[0]; - const size_t H = dst->src[1]->ne[2]; - const size_t n_seqs = dst->src[5]->ne[1]; - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - if (params->ith != 0) { - return; - } - - memset(dst_data, 0, T * C * sizeof(float)); - - float * k = (float *) dst->src[0]->data; - float * v = (float *) dst->src[1]->data; - float * r = (float *) dst->src[2]->data; - float * time_faaaa = (float *) dst->src[3]->data; - float * time_decay = (float *) dst->src[4]->data; - - size_t t_stride = H * (C / H); - - size_t h_stride = C / H; - size_t h_stride_2d = (C / H) * (C / H); - - // basically fused operations: - // dst = r @ (time_faaaa * (k @ v) + state), - // state = time_decay * state + (k @ v), - // recursive through each token - for (size_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = (C / H) * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (size_t h = 0; h < H; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (size_t i = 0; i < C / H; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - // RWKV v6: different time_decay for each token. - float time_decay_val = time_decay[t_h_i_offset]; - - for (size_t j = 0; j < C / H; j ++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } -} - -static void ggml_compute_forward_rwkv_wkv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rwkv_wkv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_unary - -static void ggml_compute_forward_map_unary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_map_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_unary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_binary - -static void ggml_compute_forward_map_binary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), - (float *) ((char *) src1->data + i*(src1->nb[1]))); - } -} - -static void ggml_compute_forward_map_binary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_binary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom1_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - - if (params->ith != 0) { - return; - } - - fun(dst, a); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom2_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom3_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b, c); -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - - struct ggml_map_custom1_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - struct ggml_map_custom2_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[2]; - - struct ggml_map_custom3_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_cross_entropy_loss - -static void ggml_compute_forward_cross_entropy_loss_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - float * sums = (float *) params->wdata; - float * st = ((float *) params->wdata) + nth + ith*nc; - float sum_thread = 0.0f; - - GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t i1 = ir0; i1 < ir1; ++i1) { - const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); - const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); - assert(sum_softmax >= 0.0); - - ggml_vec_add1_f32(nc, st, st, -sum_softmax); - ggml_vec_mul_f32(nc, st, st, s1); - - float sum_st = 0.0f; - ggml_vec_sum_f32(nc, &sum_st, st); - sum_thread += sum_st; - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(st[i])); - assert(!isinf(st[i])); - } -#endif - } - sums[ith] = sum_thread; - ggml_barrier(params->threadpool); - - if (ith == 0) { - float * dp = (float *) dst->data; - ggml_vec_sum_f32(nth, dp, sums); - dp[0] *= -1.0f / (float) nr; - } -} - -static void ggml_compute_forward_cross_entropy_loss( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cross_entropy_loss_back - -static void ggml_compute_forward_cross_entropy_loss_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * opt0 = dst->src[2]; - - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(opt0)); - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int64_t ith = params->ith; - const int64_t nth = params->nth; - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; - - for (int64_t i1 = ir0; i1 < ir1; i1++) { - float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); - float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); - float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - // soft_max - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); - assert(sum > 0.0); - ggml_vec_scale_f32(nc, ds0, 1.0/sum); - - // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr - ggml_vec_sub_f32(nc, ds0, ds0, s1); - ggml_vec_scale_f32(nc, ds0, d_by_nr); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(ds0[i])); - assert(!isinf(ds0[i])); - } -#endif - } -} - -static void ggml_compute_forward_cross_entropy_loss_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_opt_step_adamw_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src0_grad = dst->src[1]; - const struct ggml_tensor * src0_grad_m = dst->src[2]; - const struct ggml_tensor * src0_grad_v = dst->src[3]; - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - /* const float gnorm = 1.0f; */ - int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); - const float alpha = ggml_get_op_params_f32(dst, 2); - const float beta1 = ggml_get_op_params_f32(dst, 3); - const float beta2 = ggml_get_op_params_f32(dst, 4); - const float eps = ggml_get_op_params_f32(dst, 5); - const float wd = ggml_get_op_params_f32(dst, 6); - - const float beta1h = alpha/(1.0f - powf(beta1, iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); - - for (int ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; - - float * w = (float *) ((char *) src0->data + offset); // weight - const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad - float * m = (float *) ((char *) src0_grad_m->data + offset); - float * v = (float *) ((char *) src0_grad_v->data + offset); - - for (int i00 = 0; i00 < ne00; ++i00) { - m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); - v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); - - const float mh = m[i00]*beta1h; - const float vh = sqrtf(v[i00]*beta2h) + eps; - - // The weight decay is applied independently of the Adam momenta m and v. - // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. - // See: https://arxiv.org/pdf/1711.05101v3.pdf - w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh; - } - } - - ggml_barrier(params->threadpool); - if (ith != 0) { - return; - } - - iter++; - memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); -} - -static void ggml_compute_forward_opt_step_adamw( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_opt_step_adamw_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -///////////////////////////////// - -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { - GGML_ASSERT(params); - - if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { - return; - } - - switch (tensor->op) { - case GGML_OP_DUP: - { - ggml_compute_forward_dup(params, tensor); - } break; - case GGML_OP_ADD: - { - ggml_compute_forward_add(params, tensor); - } break; - case GGML_OP_ADD1: - { - ggml_compute_forward_add1(params, tensor); - } break; - case GGML_OP_ACC: - { - ggml_compute_forward_acc(params, tensor); - } break; - case GGML_OP_SUB: - { - ggml_compute_forward_sub(params, tensor); - } break; - case GGML_OP_MUL: - { - ggml_compute_forward_mul(params, tensor); - } break; - case GGML_OP_DIV: - { - ggml_compute_forward_div(params, tensor); - } break; - case GGML_OP_SQR: - { - ggml_compute_forward_sqr(params, tensor); - } break; - case GGML_OP_SQRT: - { - ggml_compute_forward_sqrt(params, tensor); - } break; - case GGML_OP_LOG: - { - ggml_compute_forward_log(params, tensor); - } break; - case GGML_OP_SIN: - { - ggml_compute_forward_sin(params, tensor); - } break; - case GGML_OP_COS: - { - ggml_compute_forward_cos(params, tensor); - } break; - case GGML_OP_SUM: - { - ggml_compute_forward_sum(params, tensor); - } break; - case GGML_OP_SUM_ROWS: - { - ggml_compute_forward_sum_rows(params, tensor); - } break; - case GGML_OP_MEAN: - { - ggml_compute_forward_mean(params, tensor); - } break; - case GGML_OP_ARGMAX: - { - ggml_compute_forward_argmax(params, tensor); - } break; - case GGML_OP_COUNT_EQUAL: - { - ggml_compute_forward_count_equal(params, tensor); - } break; - case GGML_OP_REPEAT: - { - ggml_compute_forward_repeat(params, tensor); - } break; - case GGML_OP_REPEAT_BACK: - { - ggml_compute_forward_repeat_back(params, tensor); - } break; - case GGML_OP_CONCAT: - { - ggml_compute_forward_concat(params, tensor); - } break; - case GGML_OP_SILU_BACK: - { - ggml_compute_forward_silu_back(params, tensor); - } break; - case GGML_OP_NORM: - { - ggml_compute_forward_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM: - { - ggml_compute_forward_rms_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM_BACK: - { - ggml_compute_forward_rms_norm_back(params, tensor); - } break; - case GGML_OP_GROUP_NORM: - { - ggml_compute_forward_group_norm(params, tensor); - } break; - case GGML_OP_MUL_MAT: - { - ggml_compute_forward_mul_mat(params, tensor); - } break; - case GGML_OP_MUL_MAT_ID: - { - ggml_compute_forward_mul_mat_id(params, tensor); - } break; - case GGML_OP_OUT_PROD: - { - ggml_compute_forward_out_prod(params, tensor); - } break; - case GGML_OP_SCALE: - { - ggml_compute_forward_scale(params, tensor); - } break; - case GGML_OP_SET: - { - ggml_compute_forward_set(params, tensor); - } break; - case GGML_OP_CPY: - { - ggml_compute_forward_cpy(params, tensor); - } break; - case GGML_OP_CONT: - { - ggml_compute_forward_cont(params, tensor); - } break; - case GGML_OP_RESHAPE: - { - ggml_compute_forward_reshape(params, tensor); - } break; - case GGML_OP_VIEW: - { - ggml_compute_forward_view(params, tensor); - } break; - case GGML_OP_PERMUTE: - { - ggml_compute_forward_permute(params, tensor); - } break; - case GGML_OP_TRANSPOSE: - { - ggml_compute_forward_transpose(params, tensor); - } break; - case GGML_OP_GET_ROWS: - { - ggml_compute_forward_get_rows(params, tensor); - } break; - case GGML_OP_GET_ROWS_BACK: - { - ggml_compute_forward_get_rows_back(params, tensor); - } break; - case GGML_OP_DIAG: - { - ggml_compute_forward_diag(params, tensor); - } break; - case GGML_OP_DIAG_MASK_INF: - { - ggml_compute_forward_diag_mask_inf(params, tensor); - } break; - case GGML_OP_DIAG_MASK_ZERO: - { - ggml_compute_forward_diag_mask_zero(params, tensor); - } break; - case GGML_OP_SOFT_MAX: - { - ggml_compute_forward_soft_max(params, tensor); - } break; - case GGML_OP_SOFT_MAX_BACK: - { - ggml_compute_forward_soft_max_back(params, tensor); - } break; - case GGML_OP_ROPE: - { - ggml_compute_forward_rope(params, tensor); - } break; - case GGML_OP_ROPE_BACK: - { - ggml_compute_forward_rope_back(params, tensor); - } break; - case GGML_OP_CLAMP: - { - ggml_compute_forward_clamp(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - ggml_compute_forward_conv_transpose_1d(params, tensor); - } break; - case GGML_OP_IM2COL: - { - ggml_compute_forward_im2col(params, tensor); - } break; - case GGML_OP_IM2COL_BACK: - { - ggml_compute_forward_im2col_back_f32(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - ggml_compute_forward_conv_transpose_2d(params, tensor); - } break; - case GGML_OP_POOL_1D: - { - ggml_compute_forward_pool_1d(params, tensor); - } break; - case GGML_OP_POOL_2D: - { - ggml_compute_forward_pool_2d(params, tensor); - } break; - case GGML_OP_POOL_2D_BACK: - { - ggml_compute_forward_pool_2d_back(params, tensor); - } break; - case GGML_OP_UPSCALE: - { - ggml_compute_forward_upscale(params, tensor); - } break; - case GGML_OP_PAD: - { - ggml_compute_forward_pad(params, tensor); - } break; - case GGML_OP_ARANGE: - { - ggml_compute_forward_arange(params, tensor); - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - ggml_compute_forward_timestep_embedding(params, tensor); - } break; - case GGML_OP_ARGSORT: - { - ggml_compute_forward_argsort(params, tensor); - } break; - case GGML_OP_LEAKY_RELU: - { - ggml_compute_forward_leaky_relu(params, tensor); - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; - ggml_compute_forward_flash_attn_back(params, masked, tensor); - } break; - case GGML_OP_SSM_CONV: - { - ggml_compute_forward_ssm_conv(params, tensor); - } break; - case GGML_OP_SSM_SCAN: - { - ggml_compute_forward_ssm_scan(params, tensor); - } break; - case GGML_OP_WIN_PART: - { - ggml_compute_forward_win_part(params, tensor); - } break; - case GGML_OP_WIN_UNPART: - { - ggml_compute_forward_win_unpart(params, tensor); - } break; - case GGML_OP_UNARY: - { - ggml_compute_forward_unary(params, tensor); - } break; - case GGML_OP_GET_REL_POS: - { - ggml_compute_forward_get_rel_pos(params, tensor); - } break; - case GGML_OP_ADD_REL_POS: - { - ggml_compute_forward_add_rel_pos(params, tensor); - } break; - case GGML_OP_RWKV_WKV: - { - ggml_compute_forward_rwkv_wkv(params, tensor); - } break; - case GGML_OP_MAP_UNARY: - { - ggml_unary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_unary(params, tensor, fun); - } - break; - case GGML_OP_MAP_BINARY: - { - ggml_binary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_binary(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1_F32: - { - ggml_custom1_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom1_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM2_F32: - { - ggml_custom2_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom2_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM3_F32: - { - ggml_custom3_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom3_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1: - { - ggml_compute_forward_map_custom1(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM2: - { - ggml_compute_forward_map_custom2(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM3: - { - ggml_compute_forward_map_custom3(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS: - { - ggml_compute_forward_cross_entropy_loss(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - ggml_compute_forward_cross_entropy_loss_back(params, tensor); - } - break; - case GGML_OP_OPT_STEP_ADAMW: - { - ggml_compute_forward_opt_step_adamw(params, tensor); - } - break; - case GGML_OP_NONE: - { - // nop - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -//////////////////////////////////////////////////////////////////////////////// - struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; @@ -17641,1112 +5020,526 @@ static void ggml_hash_map_free(struct hash_map * map) { GGML_FREE(map); } -// gradient checkpointing +// utility functions to change gradients +// isrc is the index of tensor in cgraph->visited_has_set.keys +// the corresponding gradient (accumulators) are also at position isrc +// if tensor has a gradient accumulator, modify that accumulator in-place +// else if there is no gradient for tensor, set the corresponding value +// else, just add/subtract/etc. the gradients -static struct ggml_tensor * ggml_recompute_graph_node( +static void ggml_add_or_set( struct ggml_context * ctx, - struct ggml_cgraph * graph, - struct hash_map * replacements, - struct ggml_tensor * node) { - - if (node == NULL) { - return NULL; + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add_impl(ctx, cgraph->grads[isrc], tensor, /*inplace =*/ cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = tensor; } - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - return node; - } - - if (!ggml_hash_contains(&graph->visited_hash_set, node)) { - return node; - } - - int count_children = 0; - for (int k = 0; k < GGML_MAX_SRC; ++k) { - if (node->src[k]) { - ++count_children; - } - } - - if (count_children == 0) { - return node; - } - - size_t i = ggml_hash_find(&replacements->set, node); - GGML_ASSERT(i != GGML_HASHSET_FULL); // assert that not full - if (replacements->set.keys[i] == node) { - return replacements->vals[i]; - } - - struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, GGML_MAX_DIMS, node->ne); - - // insert clone into replacements - GGML_ASSERT(replacements->set.keys[i] == NULL); // assert that we don't overwrite - replacements->set.keys[i] = node; - replacements->vals[i] = clone; - - clone->op = node->op; - clone->grad = node->grad; - clone->flags = node->flags; - clone->extra = node->extra; - for (int k = 0; k < GGML_MAX_DIMS; ++k) { - clone->nb[k] = node->nb[k]; - } - for (int k = 0; k < GGML_MAX_SRC; ++k) { - clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]); - } - if (node->view_src != NULL) { - clone->data = (node->view_src->data == NULL) - ? NULL // view_src not yet allocated - : (char *) node->view_src->data // view_src already allocated - + node->view_offs; - clone->view_src = node->view_src; - clone->view_offs = node->view_offs; - } - - GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t))); - GGML_ASSERT(sizeof(node->name) == GGML_MAX_NAME); - memcpy(clone->op_params, node->op_params, sizeof(node->op_params)); - ggml_format_name(clone, "%s (clone)", ggml_get_name(node)); - - return clone; + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); } -void ggml_build_backward_gradient_checkpointing( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - struct ggml_cgraph * gb_tmp, - struct ggml_tensor * * checkpoints, - int n_checkpoints) { - ggml_graph_cpy(gf, gb_tmp); - ggml_build_backward_expand(ctx, gf, gb_tmp, false); +static void ggml_acc_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor, + const size_t nb1, + const size_t nb2, + const size_t nb3, + const size_t offset) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_acc_impl(ctx, cgraph->grads[isrc], tensor, nb1, nb2, nb3, offset, cgraph->grad_accs[isrc]); + } else { + struct ggml_tensor * a_zero = ggml_scale(ctx, src, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN + cgraph->grads[isrc] = ggml_acc_impl(ctx, a_zero, tensor, nb1, nb2, nb3, offset, false); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", cgraph->visited_hash_set.keys[isrc]->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} - if (n_checkpoints <= 0) { - ggml_graph_cpy(gb_tmp, gb); +static void ggml_add1_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add1_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_repeat(ctx, tensor, src); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_sub_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_sub_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_neg(ctx, tensor); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_compute_backward( + struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i, bool * grads_needed) { + struct ggml_tensor * tensor = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, tensor); + + if (!grad) { return; } - struct hash_map * replacements = ggml_new_hash_map(gf->n_nodes + gf->n_leafs + n_checkpoints); - - // insert checkpoints in replacements - for (int i = 0; i < n_checkpoints; ++i) { - size_t k = ggml_hash_find(&replacements->set, checkpoints[i]); - GGML_ASSERT(k != GGML_HASHSET_FULL); // assert that not full - GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite - replacements->set.keys[k] = checkpoints[i]; - replacements->vals[k] = checkpoints[i]; - } - - ggml_graph_cpy(gf, gb); - // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], - // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), - // by recomputing them from checkpoints - for (int i = gf->n_nodes; in_nodes; ++i) { - struct ggml_tensor * node = gb_tmp->nodes[i]; - for (int k = 0; k < GGML_MAX_SRC; ++k) { - // insert new tensors recomputing src, reusing already made replacements, - // remember replacements: remember new tensors with mapping from corresponding gf nodes - // recurse for input tensors, - // unless (i.e. terminating when) input tensors are replacements (like checkpoints) - node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]); - } - // insert rewritten backward node with replacements made into resulting backward graph gb - ggml_build_forward_expand(gb, node); - } - - ggml_hash_map_free(replacements); -} - -// utility functions to change gradients -// if a is in acc_table, modify gradients in-place and mark result as gradient accumulator -// else if a is in zero_table, replace a -// else, just add/subtract/etc. the gradients - -static struct ggml_tensor * ggml_add_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_add_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return b; - } - return ggml_add_impl(ctx, a, b, false); -} - -static struct ggml_tensor * ggml_acc_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - const size_t nb1, - const size_t nb2, - const size_t nb3, - const size_t offset, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN - return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); - } - return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); -} - -static struct ggml_tensor * ggml_add1_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_add1_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return ggml_repeat(ctx, b, a); - } - return ggml_add1_impl(ctx, a, b, false); -} - -static struct ggml_tensor * ggml_sub_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_sub_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return ggml_neg(ctx, b); - } - return ggml_sub_impl(ctx, a, b, false); -} - -static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set * zero_table, struct ggml_hash_set * acc_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; struct ggml_tensor * src2 = tensor->src[2]; + struct ggml_hash_set * hash_set = &cgraph->visited_hash_set; + const size_t isrc0 = src0 ? ggml_hash_find(hash_set, src0) : (size_t) -1; + const size_t isrc1 = src1 ? ggml_hash_find(hash_set, src1) : (size_t) -1; + const size_t isrc2 = src2 ? ggml_hash_find(hash_set, src2) : (size_t) -1; + const bool src0_needs_grads = src0 && isrc0 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc0) && grads_needed[isrc0]; + const bool src1_needs_grads = src1 && isrc1 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc1) && grads_needed[isrc1]; + const bool src2_needs_grads = src2 && isrc2 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc2) && grads_needed[isrc2]; switch (tensor->op) { - case GGML_OP_DUP: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_ADD: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - if (ggml_are_same_shape(src0, src1)) { - src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table); - } else { - src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_repeat_back(ctx, tensor->grad, src1), zero_table, acc_table); - } - } - } break; - case GGML_OP_ADD1: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - src1->grad = ggml_add_or_set(ctx, - src1->grad, - ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean - zero_table, acc_table); - } - } break; - case GGML_OP_ACC: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - const size_t nb1 = ((int32_t *) tensor->op_params)[0]; - const size_t nb2 = ((int32_t *) tensor->op_params)[1]; - const size_t nb3 = ((int32_t *) tensor->op_params)[2]; - const size_t offset = ((int32_t *) tensor->op_params)[3]; - - struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, - tensor->grad, - src1->grad->ne[0], - src1->grad->ne[1], - src1->grad->ne[2], - src1->grad->ne[3], - nb1, nb2, nb3, offset); - - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_reshape(ctx, - ggml_cont(ctx, tensor_grad_view), - src1->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_SUB: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_MUL: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, src1, tensor->grad), - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_mul(ctx, src0, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_DIV: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_div(ctx, tensor->grad, src1), - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_sub_or_set(ctx, - src1->grad, - ggml_mul(ctx, - tensor->grad, - ggml_div(ctx, tensor, src1)), - zero_table, acc_table); - } - } break; - case GGML_OP_SQR: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale(ctx, - ggml_mul(ctx, src0, tensor->grad), - 2.0f), - zero_table, acc_table); - } - } break; - case GGML_OP_SQRT: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale(ctx, - ggml_div(ctx, - tensor->grad, - tensor), - 0.5f), - zero_table, acc_table); - } - } break; - case GGML_OP_LOG: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_div(ctx, - tensor->grad, - src0), - zero_table, acc_table); - } - } break; - case GGML_OP_SIN: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - tensor->grad, - ggml_cos(ctx, src0)), - zero_table, acc_table); - } - } break; - case GGML_OP_COS: - { - if (src0->grad) { - src0->grad = - ggml_sub_or_set(ctx, - src0->grad, - ggml_mul(ctx, - tensor->grad, - ggml_sin(ctx, src0)), - zero_table, acc_table); - } - } break; - case GGML_OP_SUM: - { - if (src0->grad) { - src0->grad = - ggml_add1_or_set(ctx, - src0->grad, - tensor->grad, - zero_table, acc_table); - } - } break; - case GGML_OP_SUM_ROWS: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_repeat(ctx, - tensor->grad, - src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_COUNT_EQUAL: - { - GGML_ABORT("fatal error"); // TODO: implement + case GGML_OP_DUP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_REPEAT: - { - // necessary for llama - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_repeat_back(ctx, tensor->grad, src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_REPEAT_BACK: - { - if (src0->grad) { - // TODO: test this - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_repeat(ctx, tensor->grad, src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_CONCAT: - { - GGML_ABORT("fatal error"); // TODO: implement + } break; + case GGML_OP_ADD: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_SILU_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + struct ggml_tensor * tmp = grad; + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); + } + ggml_add_or_set(ctx, cgraph, isrc1, tmp); } - case GGML_OP_NORM: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_ADD1: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_RMS_NORM: - { - // necessary for llama - if (src0->grad) { - float eps; - memcpy(&eps, tensor->op_params, sizeof(float)); - - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rms_norm_back(ctx, src0, tensor->grad, eps), - zero_table, acc_table); - } - } break; - case GGML_OP_RMS_NORM_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_mean(ctx, grad)); // TODO: should probably be sum instead of mean } - case GGML_OP_GROUP_NORM: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_ACC: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_MUL_MAT: - { - // https://cs231n.github.io/optimization-2/#staged - // # forward pass - // s0 = np.random.randn(5, 10) - // s1 = np.random.randn(10, 3) - // t = s0.dot(s1) + if (src1_needs_grads) { + const size_t nb1 = ((int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((int32_t *) tensor->op_params)[2]; + const size_t offset = ((int32_t *) tensor->op_params)[3]; - // # now suppose we had the gradient on t from above in the circuit - // dt = np.random.randn(*t.shape) # same shape as t - // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix - // ds1 = t.T.dot(dt) + struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); - // tensor.shape [m,p,qq,rr] - // src0.shape [n,m,q1,r1] - // src1.shape [n,p,qq,rr] - - // necessary for llama - if (src0->grad) { - struct ggml_tensor * s1_tg = - ggml_out_prod(ctx, // [n,m,qq,rr] - src1, // [n,p,qq,rr] - tensor->grad); // [m,p,qq,rr] - const int64_t qq = s1_tg->ne[2]; - const int64_t rr = s1_tg->ne[3]; - const int64_t q1 = src0->ne[2]; - const int64_t r1 = src0->ne[3]; - const bool ne2_broadcasted = qq > q1; - const bool ne3_broadcasted = rr > r1; - if (ne2_broadcasted || ne3_broadcasted) { - // sum broadcast repetitions of s1_tg into shape of src0 - s1_tg = ggml_repeat_back(ctx, s1_tg, src0); - } - src0->grad = - ggml_add_or_set(ctx, - src0->grad, // [n,m,q1,r1] - s1_tg, // [n,m,q1,r1] - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, // [n,p,qq,rr] - // ggml_mul_mat(ctx, // [n,p,qq,rr] - // ggml_cont(ctx, // [m,n,q1,r1] - // ggml_transpose(ctx, src0)), // [m,n,q1,r1] - // tensor->grad), // [m,p,qq,rr] - - // // when src0 is bigger than tensor->grad (this is mostly the case in llama), - // // avoid transpose of src0, rather transpose smaller tensor->grad - // // and then use ggml_out_prod - ggml_out_prod(ctx, // [n,p,qq,rr] - src0, // [n,m,q1,r1] - ggml_transpose(ctx, // [p,m,qq,rr] - tensor->grad)), // [m,p,qq,rr] - zero_table, acc_table); - } - } break; - case GGML_OP_MUL_MAT_ID: - { - GGML_ABORT("fatal error"); // TODO: not implemented + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); } - case GGML_OP_OUT_PROD: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_SUB: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_SCALE: - { - // necessary for llama - if (src0->grad) { - float s; - memcpy(&s, tensor->op_params, sizeof(float)); - - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale_impl(ctx, tensor->grad, s, false), - zero_table, acc_table); - } - } break; - case GGML_OP_SET: - { - const size_t nb1 = ((int32_t *) tensor->op_params)[0]; - const size_t nb2 = ((int32_t *) tensor->op_params)[1]; - const size_t nb3 = ((int32_t *) tensor->op_params)[2]; - const size_t offset = ((int32_t *) tensor->op_params)[3]; - - struct ggml_tensor * tensor_grad_view = NULL; - - if (src0->grad || src1->grad) { - GGML_ASSERT(src0->type == tensor->type); - GGML_ASSERT(tensor->grad->type == tensor->type); - GGML_ASSERT(!src1->grad || src1->grad->type == tensor->grad->type); - - tensor_grad_view = ggml_view_4d(ctx, - tensor->grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], - nb1, nb2, nb3, offset); - } - - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_acc_impl(ctx, - tensor->grad, - ggml_neg(ctx, tensor_grad_view), - nb1, nb2, nb3, offset, false), - zero_table, acc_table); - } - - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_reshape(ctx, - ggml_cont(ctx, tensor_grad_view), - src1->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_CPY: - { - // necessary for llama - // cpy overwrites value of src1 by src0 and returns view(src1) - // the overwriting is mathematically equivalent to: - // tensor = src0 * 1 + src1 * 0 - if (src0->grad) { - // dsrc0 = dtensor * 1 - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - // dsrc1 = dtensor * 0 -> noop - } - } break; - case GGML_OP_CONT: - { - // same as cpy - if (src0->grad) { - GGML_ASSERT(ggml_is_contiguous(src0->grad)); - GGML_ASSERT(ggml_is_contiguous(tensor->grad)); - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_RESHAPE: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_reshape(ctx, - ggml_is_contiguous(tensor->grad) - ? tensor->grad - : ggml_cont(ctx, tensor->grad), - src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_VIEW: - { - // necessary for llama - if (src0->grad) { - size_t offset; - - memcpy(&offset, tensor->op_params, sizeof(offset)); - - size_t nb1 = tensor->nb[1]; - size_t nb2 = tensor->nb[2]; - size_t nb3 = tensor->nb[3]; - - if (src0->type != src0->grad->type) { - // gradient is typically F32, but src0 could be other type - size_t ng = ggml_element_size(src0->grad); - size_t n0 = ggml_element_size(src0); - GGML_ASSERT(offset % n0 == 0); - GGML_ASSERT(nb1 % n0 == 0); - GGML_ASSERT(nb2 % n0 == 0); - GGML_ASSERT(nb3 % n0 == 0); - offset = (offset / n0) * ng; - nb1 = (nb1 / n0) * ng; - nb2 = (nb2 / n0) * ng; - nb3 = (nb3 / n0) * ng; - } - - src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table, acc_table); - } - } break; - case GGML_OP_PERMUTE: - { - // necessary for llama - if (src0->grad) { - int32_t * axes = (int32_t *) tensor->op_params; - int axis0 = axes[0] & 0x3; - int axis1 = axes[1] & 0x3; - int axis2 = axes[2] & 0x3; - int axis3 = axes[3] & 0x3; - int axes_backward[4] = {0,0,0,0}; - axes_backward[axis0] = 0; - axes_backward[axis1] = 1; - axes_backward[axis2] = 2; - axes_backward[axis3] = 3; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_permute(ctx, - tensor->grad, - axes_backward[0], - axes_backward[1], - axes_backward[2], - axes_backward[3]), - zero_table, acc_table); - } - } break; - case GGML_OP_TRANSPOSE: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_transpose(ctx, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_GET_ROWS: - { - // necessary for llama (only for tokenizer) - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - // last ggml_get_rows_back argument src0->grad is only - // necessary to setup correct output shape - ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad), - zero_table, acc_table); - } - if (src1->grad) { - // noop - } - } break; - case GGML_OP_GET_ROWS_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, grad); } - case GGML_OP_DIAG: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_MUL: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, src1, grad)); } - case GGML_OP_DIAG_MASK_INF: - { - // necessary for llama - if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - /* ggml_diag_mask_inf_impl() shouldn't be here */ - /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ - ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), - zero_table, acc_table); + if (src1_needs_grads) { + struct ggml_tensor * tmp = ggml_mul(ctx, src0, grad); + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); } - } break; - case GGML_OP_DIAG_MASK_ZERO: - { - // necessary for llama - if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), - zero_table, acc_table); - } - } break; - case GGML_OP_SOFT_MAX: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_soft_max_back(ctx, tensor->grad, tensor), - zero_table, acc_table); - } - GGML_ASSERT((!src1 || !src1->grad) && "backward pass for softmax mask not implemented"); - } break; - case GGML_OP_SOFT_MAX_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + ggml_add_or_set(ctx, cgraph, isrc1, tmp); } - case GGML_OP_ROPE: - { - // necessary for llama - if (src0->grad) { - //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - //const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + } break; + case GGML_OP_DIV: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src1)); + } + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, ggml_mul(ctx, grad, ggml_div(ctx, tensor, src1))); + } + } break; + case GGML_OP_SQR: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_mul(ctx, src0, grad), 2.0f)); + } + } break; + case GGML_OP_SQRT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_div(ctx, grad, tensor), 0.5f)); + } + } break; + case GGML_OP_LOG: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src0)); + } + } break; + case GGML_OP_SIN: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_cos(ctx, src0))); + } + } break; + case GGML_OP_COS: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_sin(ctx, src0))); + } + } break; + case GGML_OP_SUM: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_OP_SUM_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_MEAN: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], false)); + } + } break; + case GGML_OP_REPEAT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat_back(ctx, grad, src0)); + } + } break; + case GGML_OP_REPEAT_BACK: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_RMS_NORM: { + if (src0_needs_grads) { + float eps; + memcpy(&eps, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, src0, grad, eps)); + } + } break; + case GGML_OP_MUL_MAT: { + // https://cs231n.github.io/optimization-2/#staged + // # forward pass + // s0 = np.random.randn(5, 10) + // s1 = np.random.randn(10, 3) + // t = s0.dot(s1) - memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + // # now suppose we had the gradient on t from above in the circuit + // dt = np.random.randn(*t.shape) # same shape as t + // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix + // ds1 = t.T.dot(dt) - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rope_back(ctx, - tensor->grad, - src1, - src2, - n_dims, - mode, - n_ctx_orig, - freq_base, - freq_scale, - ext_factor, - attn_factor, - beta_fast, - beta_slow), - zero_table, acc_table); + // tensor.shape [m,p,qq,rr] + // src0.shape [n,m,q1,r1] + // src1.shape [n,p,qq,rr] + + if (src0_needs_grads) { + struct ggml_tensor * s1_tg = + ggml_out_prod(ctx, // [n,m,qq,rr] + src1, // [n,p,qq,rr] + grad); // [m,p,qq,rr] + const int64_t qq = s1_tg->ne[2]; + const int64_t rr = s1_tg->ne[3]; + const int64_t q1 = src0->ne[2]; + const int64_t r1 = src0->ne[3]; + const bool ne2_broadcasted = qq > q1; + const bool ne3_broadcasted = rr > r1; + if (ne2_broadcasted || ne3_broadcasted) { + // sum broadcast repetitions of s1_tg into shape of src0 + s1_tg = ggml_repeat_back(ctx, s1_tg, src0); } - GGML_ASSERT((!src2 || !src2->grad) && "gradients for freq factors not implemented"); - } break; - case GGML_OP_ROPE_BACK: - { - if (src0->grad) { - //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - //const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + ggml_add_or_set(ctx, cgraph, isrc0, s1_tg /*= [n,m,q1,r1]*/); + } + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, + // ggml_mul_mat(ctx, // [n,p,qq,rr] + // ggml_cont(ctx, // [m,n,q1,r1] + // ggml_transpose(ctx, src0)), // [m,n,q1,r1] + // grad), // [m,p,qq,rr] - memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + // when src0 is bigger than tensor->grad (this is mostly the case in llama), + // avoid transpose of src0, rather transpose smaller tensor->grad + // and then use ggml_out_prod + ggml_out_prod(ctx, // [n,p,qq,rr] + src0, // [n,m,q1,r1] + ggml_transpose(ctx, // [p,m,qq,rr] + grad))); // [m,p,qq,rr] + } + } break; + case GGML_OP_SCALE: { + if (src0_needs_grads) { + float s; + memcpy(&s, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, s, false)); + } + } break; + case GGML_OP_SET: { + const size_t nb1 = ((const int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((const int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((const int32_t *) tensor->op_params)[2]; + const size_t offset = ((const int32_t *) tensor->op_params)[3]; - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rope_impl(ctx, - tensor->grad, - src1, - src2, - n_dims, - mode, - n_ctx_orig, - freq_base, - freq_scale, - ext_factor, - attn_factor, - beta_fast, - beta_slow, - false), - zero_table, acc_table); - } - } break; - case GGML_OP_CLAMP: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_CONV_TRANSPOSE_1D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_IM2COL: - { - if (src1->grad) { - const int32_t s0 = ggml_get_op_params_i32(tensor, 0); - const int32_t s1 = ggml_get_op_params_i32(tensor, 1); - const int32_t p0 = ggml_get_op_params_i32(tensor, 2); - const int32_t p1 = ggml_get_op_params_i32(tensor, 3); - const int32_t d0 = ggml_get_op_params_i32(tensor, 4); - const int32_t d1 = ggml_get_op_params_i32(tensor, 5); - const bool is_2D = ggml_get_op_params_i32(tensor, 6) == 1; + struct ggml_tensor * tensor_grad_view = NULL; - src1->grad = ggml_add_or_set(ctx, - src1->grad, - ggml_im2col_back(ctx, src0, tensor->grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D), - zero_table, acc_table); - } - } break; - case GGML_OP_IM2COL_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_CONV_TRANSPOSE_2D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_POOL_1D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_POOL_2D: - { - if (src0->grad) { - const enum ggml_op_pool op = ggml_get_op_params_i32(tensor, 0); - const int32_t k0 = ggml_get_op_params_i32(tensor, 1); - const int32_t k1 = ggml_get_op_params_i32(tensor, 2); - const int32_t s0 = ggml_get_op_params_i32(tensor, 3); - const int32_t s1 = ggml_get_op_params_i32(tensor, 4); - const int32_t p0 = ggml_get_op_params_i32(tensor, 5); - const int32_t p1 = ggml_get_op_params_i32(tensor, 6); + if (src0_needs_grads || src1_needs_grads) { + GGML_ASSERT(src0->type == tensor->type); + GGML_ASSERT(!cgraph->grads[isrc0] || cgraph->grads[isrc0]->type == grad->type); + GGML_ASSERT(!cgraph->grads[isrc1] || !src1_needs_grads || cgraph->grads[isrc1]->type == grad->type); - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_pool_2d_back(ctx, tensor->grad, src0, op, k0, k1, s0, s1, p0, p1), - zero_table, acc_table); - } - } break; - case GGML_OP_POOL_2D_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); } - case GGML_OP_UPSCALE: - { - GGML_ABORT("fatal error"); // TODO: not implemented + + if (src0_needs_grads) { + struct ggml_tensor * tmp = ggml_neg(ctx, tensor_grad_view); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_acc_impl(ctx, grad, tmp, nb1, nb2, nb3, offset, false)); } - case GGML_OP_PAD: - { - GGML_ABORT("fatal error"); // TODO: not implemented + + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); } - case GGML_OP_ARANGE: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_CPY: { + // cpy overwrites value of src1 by src0 and returns view(src1) + // the overwriting is mathematically equivalent to: + // tensor = src0 * 1 + src1 * 0 + if (src0_needs_grads) { + // dsrc0 = dtensor * 1 + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_TIMESTEP_EMBEDDING: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + // dsrc1 = dtensor * 0 -> noop } - case GGML_OP_ARGSORT: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_CONT: { + // same as cpy + if (src0_needs_grads) { + GGML_ASSERT(!cgraph->grads[isrc0] || ggml_is_contiguous(cgraph->grads[isrc0])); + GGML_ASSERT(ggml_is_contiguous(grad)); + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_LEAKY_RELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_RESHAPE: { + if (src0_needs_grads) { + struct ggml_tensor * grad_cont = ggml_is_contiguous(grad) ? grad : ggml_cont(ctx, grad); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad_cont, src0)); } - case GGML_OP_FLASH_ATTN_EXT: - { - GGML_ABORT("FA backward pass not adapted after rework"); - struct ggml_tensor * flash_grad = NULL; - if (src0->grad || src1->grad || tensor->src[2]->grad) { - int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; - flash_grad = - ggml_flash_attn_back(ctx, - src0, - src1, - tensor->src[2], - tensor->grad, - masked); + } break; + case GGML_OP_VIEW: { + if (src0_needs_grads) { + size_t offset; + + memcpy(&offset, tensor->op_params, sizeof(offset)); + + size_t nb1 = tensor->nb[1]; + size_t nb2 = tensor->nb[2]; + size_t nb3 = tensor->nb[3]; + + if (cgraph->grads[isrc0] && src0->type != cgraph->grads[isrc0]->type) { + // gradient is typically F32, but src0 could be other type + size_t ng = ggml_element_size(cgraph->grads[isrc0]); + size_t n0 = ggml_element_size(src0); + GGML_ASSERT(offset % n0 == 0); + GGML_ASSERT(nb1 % n0 == 0); + GGML_ASSERT(nb2 % n0 == 0); + GGML_ASSERT(nb3 % n0 == 0); + offset = (offset / n0) * ng; + nb1 = (nb1 / n0) * ng; + nb2 = (nb2 / n0) * ng; + nb3 = (nb3 / n0) * ng; } - const int64_t elem_q = ggml_nelements(src0); - const int64_t elem_k = ggml_nelements(src1); - const int64_t elem_v = ggml_nelements(src2); - - enum ggml_type result_type = flash_grad->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - if (src0->grad) { - struct ggml_tensor * view_q = ggml_view_1d(ctx, flash_grad, elem_q, offs_q); - struct ggml_tensor * grad_q = ggml_reshape(ctx, view_q, src0); - src0->grad = ggml_add_or_set(ctx, - src0->grad, - grad_q, - zero_table, acc_table); - } - if (src1->grad) { - struct ggml_tensor * view_k = ggml_view_1d(ctx, flash_grad, elem_k, offs_k); - struct ggml_tensor * grad_k = ggml_reshape(ctx, view_k, src1); - src1->grad = ggml_add_or_set(ctx, - src1->grad, - grad_k, - zero_table, acc_table); - } - if (src2->grad) { - struct ggml_tensor * view_v = ggml_view_1d(ctx, flash_grad, elem_v, offs_v); - struct ggml_tensor * grad_v = ggml_reshape(ctx, view_v, src2); - src2->grad = ggml_add_or_set(ctx, - src2->grad, - grad_v, - zero_table, acc_table); - } - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - GGML_ABORT("fatal error"); // not supported + ggml_acc_or_set(ctx, cgraph, isrc0, grad, nb1, nb2, nb3, offset); } - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_PERMUTE: { + if (src0_needs_grads) { + const int32_t * axes = (const int32_t *) tensor->op_params; + const int axis0 = axes[0] & 0x3; + const int axis1 = axes[1] & 0x3; + const int axis2 = axes[2] & 0x3; + const int axis3 = axes[3] & 0x3; + int axb[4] = {0,0,0,0}; // axes backward + axb[axis0] = 0; + axb[axis1] = 1; + axb[axis2] = 2; + axb[axis3] = 3; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_permute(ctx, grad, axb[0], axb[1], axb[2], axb[3])); } + } break; + case GGML_OP_TRANSPOSE: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_transpose(ctx, grad)); + } + } break; + case GGML_OP_GET_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_get_rows_back(ctx, grad, src1, src0)); + } + if (src1_needs_grads) { + // noop + } + } break; + case GGML_OP_DIAG_MASK_INF: { + if (src0_needs_grads) { + /* ggml_diag_mask_inf_impl() shouldn't be here */ + /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_DIAG_MASK_ZERO: { + if (src0_needs_grads) { + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_SOFT_MAX: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_soft_max_back(ctx, grad, tensor)); + } + GGML_ASSERT((!src1 || !src1_needs_grads) && "backward pass for softmax mask not implemented"); + } break; + case GGML_OP_ROPE: { + if (src0_needs_grads) { + //const int n_past = ((int32_t *) tensor->op_params)[0]; + const int n_dims = ((const int32_t *) tensor->op_params)[1]; + const int mode = ((const int32_t *) tensor->op_params)[2]; + //const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig = ((const int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + memcpy(&freq_base, (const float *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (const float *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (const float *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (const float *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (const float *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (const float *) tensor->op_params + 10, sizeof(float)); + + ggml_add_or_set(ctx, cgraph, isrc0, + ggml_rope_back(ctx, grad, src1, src2, n_dims, mode, n_ctx_orig, freq_base, + freq_scale, ext_factor, attn_factor, beta_fast, beta_slow)); + } + GGML_ASSERT((!src2 || !src2_needs_grads) && "gradients for freq factors not implemented"); + } break; + case GGML_OP_IM2COL: { + if (src1_needs_grads) { + const int32_t s0 = ggml_get_op_params_i32(tensor, 0); + const int32_t s1 = ggml_get_op_params_i32(tensor, 1); + const int32_t p0 = ggml_get_op_params_i32(tensor, 2); + const int32_t p1 = ggml_get_op_params_i32(tensor, 3); + const int32_t d0 = ggml_get_op_params_i32(tensor, 4); + const int32_t d1 = ggml_get_op_params_i32(tensor, 5); + const bool is_2D = ggml_get_op_params_i32(tensor, 6) == 1; + + ggml_add_or_set(ctx, cgraph, isrc1, ggml_im2col_back(ctx, src0, grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D)); + } + } break; + case GGML_OP_POOL_2D: { + if (src0_needs_grads) { + const enum ggml_op_pool op = ggml_get_op_params_i32(tensor, 0); + const int32_t k0 = ggml_get_op_params_i32(tensor, 1); + const int32_t k1 = ggml_get_op_params_i32(tensor, 2); + const int32_t s0 = ggml_get_op_params_i32(tensor, 3); + const int32_t s1 = ggml_get_op_params_i32(tensor, 4); + const int32_t p0 = ggml_get_op_params_i32(tensor, 5); + const int32_t p1 = ggml_get_op_params_i32(tensor, 6); + + ggml_add_or_set(ctx, cgraph, isrc0, ggml_pool_2d_back(ctx, grad, src0, op, k0, k1, s0, s1, p0, p1)); + } + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: - case GGML_OP_UNARY: - { - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_ABS: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - ggml_sgn(ctx, src0), - tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_SGN: - { - if (src0->grad) { - // noop - } - } break; - case GGML_UNARY_OP_NEG: - { - if (src0->grad) { - src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_STEP: - { - if (src0->grad) { - // noop - } - } break; - case GGML_UNARY_OP_TANH: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_ELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_RELU: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - ggml_step(ctx, src0), - tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_SIGMOID: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_GELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_GELU_QUICK: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_SILU: - { - // necessary for llama - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_silu_back(ctx, src0, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_EXP: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, tensor, tensor->grad), - zero_table, acc_table); - } - } break; - default: - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_GET_REL_POS: - case GGML_OP_ADD_REL_POS: - case GGML_OP_RWKV_WKV: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - case GGML_OP_MAP_CUSTOM1: - case GGML_OP_MAP_CUSTOM2: - case GGML_OP_MAP_CUSTOM3: - { - GGML_ABORT("fatal error"); // not supported + case GGML_OP_UNARY: { + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_ABS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_sgn(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SGN: { + // noop + } break; + case GGML_UNARY_OP_NEG: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_UNARY_OP_STEP: { + // noop + } break; + case GGML_UNARY_OP_RELU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_step(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SILU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, src0, grad)); + } + } break; + case GGML_UNARY_OP_EXP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, tensor, grad)); + } + } break; + default: { + fprintf(stderr, "%s: unsupported unary op for backward pass: %s\n", + __func__, ggml_unary_op_name(ggml_get_unary_op(tensor))); + GGML_ABORT("fatal error"); + } //break; } - case GGML_OP_CROSS_ENTROPY_LOSS: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_cross_entropy_loss_back(ctx, - src0, - src1, - tensor->grad), - zero_table, acc_table); - } - GGML_ASSERT(!src1->grad && "backward pass for labels not implemented"); - } break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - GGML_ABORT("fatal error"); // not supported + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_cross_entropy_loss_back(ctx, src0, src1, grad)); } - case GGML_OP_OPT_STEP_ADAMW: - { - GGML_ABORT("fatal error"); // not supported - } - case GGML_OP_NONE: - { - // nop - } break; + GGML_ASSERT(!src1_needs_grads && "backward pass for labels not implemented"); + } break; + case GGML_OP_NONE: { + // noop + } break; case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } + default: { + fprintf(stderr, "%s: unsupported ggml op for backward pass: %s\n", __func__, ggml_op_name(tensor->op)); + GGML_ABORT("fatal error"); + } //break; } - for (int i = 0; i < GGML_MAX_SRC; ++i) { - if (tensor->src[i] && tensor->src[i]->grad) { - GGML_ASSERT(ggml_are_same_shape(tensor->src[i], tensor->src[i]->grad)); - } - } + GGML_ASSERT(!src0_needs_grads || ggml_are_same_shape(src0, cgraph->grads[isrc0])); + GGML_ASSERT(!src1_needs_grads || ggml_are_same_shape(src1, cgraph->grads[isrc1])); + GGML_ASSERT(!src2_needs_grads || ggml_are_same_shape(src2, cgraph->grads[isrc2])); } static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) { - if (node->grad == NULL) { - // this usually happens when we generate intermediate nodes from constants in the backward pass - // it can also happen during forward pass, if the user performs computations with constants - if (node->op != GGML_OP_NONE) { - //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op); - } - } - // check if already visited if (ggml_hash_insert(&cgraph->visited_hash_set, node) == GGML_HASHSET_ALREADY_EXISTS) { return; @@ -18807,18 +5600,41 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * ggml_build_forward_impl(cgraph, tensor, true); } -void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate) { - GGML_ASSERT(gf->n_nodes > 0); - GGML_ASSERT(gf->grads); +void ggml_build_backward_expand( + struct ggml_context * ctx_static, + struct ggml_context * ctx_compute, + struct ggml_cgraph * cgraph, + bool accumulate) { + GGML_ASSERT(cgraph->n_nodes > 0); + GGML_ASSERT(cgraph->grads); + GGML_ASSERT(cgraph->grad_accs); - for (int i = 0; i < gf->n_nodes; ++i) { - struct ggml_tensor * node = gf->nodes[i]; + const int n_nodes_f = cgraph->n_nodes; + + memset(cgraph->grads, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + bool * grads_needed = calloc(cgraph->visited_hash_set.size, sizeof(bool)); + + { + bool any_params = false; + bool any_loss = false; + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; + any_params = any_params || (node->flags & GGML_TENSOR_FLAG_PARAM); + any_loss = any_loss || (node->flags & GGML_TENSOR_FLAG_LOSS); + } + GGML_ASSERT(any_params && "no trainable parameters found, did you forget to call ggml_set_param?"); + GGML_ASSERT(any_loss && "no training loss found, did you forget to call ggml_set_loss?"); + } + + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; if (node->type == GGML_TYPE_I32) { continue; } - bool needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM; + bool node_needs_grad = (node->flags & GGML_TENSOR_FLAG_PARAM) || (node->flags & GGML_TENSOR_FLAG_LOSS); bool ignore_src[GGML_MAX_SRC] = {false}; switch (node->op) { // gradients in node->src[0] for one reason or another have no effect on output gradients @@ -18835,7 +5651,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } break; // gradients in node->src[1] for one reason or another have no effect on output gradients - case GGML_OP_CPY: // gradients in CPY target are irrelevant + case GGML_OP_CPY: // gradients in CPY target are irrelevant case GGML_OP_GET_ROWS: // row indices not differentiable case GGML_OP_GET_ROWS_BACK: // same as for GET_ROWS case GGML_OP_ROPE: // positions not differentiable @@ -18846,14 +5662,14 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * break; } for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (!node->src[j] || !node->src[j]->grad || ignore_src[j]) { + if (!node->src[j] || ignore_src[j] || !grads_needed[ggml_hash_find(&cgraph->visited_hash_set, node->src[j])]) { continue; } GGML_ASSERT(node->src[j]->type == GGML_TYPE_F32 || node->src[j]->type == GGML_TYPE_F16); - needs_grad = true; + node_needs_grad = true; break; } - if (!needs_grad) { + if (!node_needs_grad) { continue; } @@ -18861,76 +5677,26 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE); - // create a new tensor with the same type and shape as the node and set it as grad - node->grad = ggml_dup_tensor(ctx, node); - } - - // keep tables of original gradients for replacement/accumulation logic - struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size); - struct ggml_hash_set acc_table = ggml_hash_set_new(gf->size); - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->grad) { - { - const size_t insert_result = ggml_hash_insert(&zero_table, node->grad); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - } - - // only gradients of trainable parameters should be accumulated - if (accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) { - const size_t insert_result = ggml_hash_insert(&acc_table, node->grad); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - } + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + GGML_ASSERT(igrad != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad)); + if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) { + cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node); + cgraph->grads[igrad] = cgraph->grad_accs[igrad]; + ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name); } + grads_needed[igrad] = true; } - for (int i = gf->n_nodes - 1; i >= 0; i--) { - struct ggml_tensor * node = gf->nodes[i]; - + for (int i = n_nodes_f - 1; i >= 0; --i) { // inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation // use allocator to automatically make inplace operations - if (node->grad) { - ggml_compute_backward(ctx, node, &zero_table, &acc_table); - } + ggml_compute_backward(ctx_compute, cgraph, i, grads_needed); } - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - ggml_build_forward_expand(gb, node->grad); - } - } - - ggml_hash_set_free(&zero_table); - ggml_hash_set_free(&acc_table); + free(grads_needed); } -void ggml_build_opt_adamw( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - float alpha, - float beta1, - float beta2, - float eps, - float wd) { - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - struct ggml_tensor * opt_step = ggml_opt_step_adamw(ctx, node, node->grad, alpha, beta1, beta2, eps, wd); - ggml_build_forward_expand(gb, opt_step); - } - } -} - - static void * incr_ptr_aligned(void ** p, size_t size, size_t align) { void * ptr = *p; ptr = (void *) GGML_PAD((uintptr_t) ptr, align); @@ -18946,7 +5712,8 @@ static size_t ggml_graph_nbytes(size_t size, bool grads) { incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // leafs incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // hash keys if (grads) { - incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grad_accs } incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); @@ -18972,10 +5739,12 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz void * p = cgraph + 1; - struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + struct ggml_tensor ** grad_accs_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + ggml_bitset_t * hash_used = incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); // check that we allocated the correct amount of memory @@ -18987,12 +5756,17 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz /*.n_leafs =*/ 0, /*.nodes =*/ nodes_ptr, /*.grads =*/ grads_ptr, + /*.grad_accs =*/ grad_accs_ptr, /*.leafs =*/ leafs_ptr, /*.hash_table =*/ { hash_size, hash_used, hash_keys_ptr }, /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, }; ggml_hash_set_reset(&cgraph->visited_hash_set); + if (grads) { + memset(cgraph->grads, 0, hash_size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, hash_size*sizeof(struct ggml_tensor *)); + } return cgraph; } @@ -19003,14 +5777,15 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) { struct ggml_cgraph cgraph = { - /*.size =*/ 0, - /*.n_nodes =*/ i1 - i0, - /*.n_leafs =*/ 0, - /*.nodes =*/ cgraph0->nodes + i0, - /*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL, - /*.leafs =*/ NULL, - /*.hash_table =*/ { 0, NULL, NULL }, - /*.order =*/ cgraph0->order, + /*.size =*/ 0, + /*.n_nodes =*/ i1 - i0, + /*.n_leafs =*/ 0, + /*.nodes =*/ cgraph0->nodes + i0, + /*.grads =*/ NULL, // gradients would need visited_hash_set + /*.grad_accs =*/ NULL, + /*.leafs =*/ NULL, + /*.visited_hash_set =*/ { 0, NULL, NULL }, + /*.order =*/ cgraph0->order, }; return cgraph; @@ -19033,19 +5808,33 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { dst->nodes[i] = src->nodes[i]; } - if (src->grads) { - GGML_ASSERT(dst->grads != NULL); - for (int i = 0; i < src->n_nodes; ++i) { - dst->grads[i] = src->grads[i]; - } - } - for (size_t i = 0; i < src->visited_hash_set.size; ++i) { // copy all hashset keys (tensors) that are in use if (ggml_bitset_get(src->visited_hash_set.used, i)) { ggml_hash_insert(&dst->visited_hash_set, src->visited_hash_set.keys[i]); } } + + if (dst->grads) { + memset(dst->grads, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(dst->grad_accs, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + } + if (src->grads) { + GGML_ASSERT(dst->grads != NULL); + GGML_ASSERT(dst->grad_accs != NULL); + for (int i = 0; i < src->n_nodes; ++i) { + const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]); + const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]); + + GGML_ASSERT(igrad_src != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src)); + GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst)); + + dst->grads[igrad_dst] = src->grads[igrad_src]; + dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src]; + } + } } struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { @@ -19054,33 +5843,49 @@ struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgrap return result; } +struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { + if (ggml_is_empty(tensor)) { + return tensor; + } + if (tensor->buffer) { + ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor)); + } else { + GGML_ASSERT(tensor->data); + memset(tensor->data, 0, ggml_nbytes(tensor)); + } + return tensor; +} + void ggml_graph_reset(struct ggml_cgraph * cgraph) { GGML_ASSERT(cgraph->grads != NULL); for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; + struct ggml_tensor * node = cgraph->nodes[i]; + struct ggml_tensor * grad_acc = ggml_graph_get_grad_acc(cgraph, node); - // initial gradients of loss should be 1, 0 otherwise - if (node->grad) { - if (node->flags & GGML_TENSOR_FLAG_LOSS) { - GGML_ASSERT(node->grad->buffer); - GGML_ASSERT(node->type == GGML_TYPE_F32); - GGML_ASSERT(ggml_is_scalar(node)); - - const float onef = 1.0f; - ggml_backend_tensor_set(node->grad, &onef, 0, ggml_nbytes(node->grad)); - } else { - ggml_set_zero(node->grad); - } - } - - GGML_ASSERT(node); if (node->op == GGML_OP_OPT_STEP_ADAMW) { - // set iteration to 1 and clear momenta - ggml_set_op_params_i32(node, 0, 1); + // clear momenta ggml_set_zero(node->src[2]); ggml_set_zero(node->src[3]); } + + // initial gradients of loss should be 1, 0 otherwise + if (grad_acc) { + if (node->flags & GGML_TENSOR_FLAG_LOSS) { + GGML_ASSERT(grad_acc->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_scalar(grad_acc)); + + const float onef = 1.0f; + if (grad_acc->buffer) { + ggml_backend_tensor_set(grad_acc, &onef, 0, sizeof(float)); + } else { + GGML_ASSERT(grad_acc->data); + *((float *) grad_acc->data) = onef; + } + } else { + ggml_set_zero(grad_acc); + } + } } } @@ -19118,1095 +5923,7 @@ void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tenso cgraph->n_nodes++; } -// Android's libc implementation "bionic" does not support setting affinity -#if defined(__gnu_linux__) -static void set_numa_thread_affinity(int thread_n) { - if (!ggml_is_numa()) { - return; - } - - int node_num; - int rv; - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - switch(g_state.numa.numa_strategy) { - case GGML_NUMA_STRATEGY_DISTRIBUTE: - // run thread on node_num thread_n / (threads per node) - node_num = thread_n % g_state.numa.n_nodes; - break; - case GGML_NUMA_STRATEGY_ISOLATE: - // run thread on current_node - node_num = g_state.numa.current_node; - break; - case GGML_NUMA_STRATEGY_NUMACTL: - // use the cpuset that numactl gave us - rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); - } - return; - default: - return; - } - - struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (size_t i = 0; i < node->n_cpus; ++i) { - CPU_SET_S(node->cpus[i], setsize, cpus); - } - - rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} - -static void clear_numa_thread_affinity(void) { - if (!ggml_is_numa()) { - return; - } - - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { - CPU_SET_S(i, setsize, cpus); - } - - int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} -#else -// TODO: Windows etc. -// (the linux implementation may also work on BSD, someone should test) -static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } -static void clear_numa_thread_affinity(void) {} -#endif - -static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { - int n_tasks = 0; - - if (ggml_is_empty(node)) { - // no need to multi-thread a no-op - n_tasks = 1; - return n_tasks; - } - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - case GGML_OP_CONT: - case GGML_OP_ADD: - case GGML_OP_ADD1: - case GGML_OP_ACC: - { - n_tasks = n_threads; - } break; - case GGML_OP_SUB: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_LOG: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT_EQUAL: - { - n_tasks = n_threads; - } break; - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - case GGML_OP_LEAKY_RELU: - { - n_tasks = 1; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SGN: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_ELU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_EXP: - { - n_tasks = 1; - } break; - - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - { - n_tasks = n_threads; - } break; - default: - GGML_ABORT("fatal error"); - } - break; - case GGML_OP_SILU_BACK: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_GROUP_NORM: - case GGML_OP_CONCAT: - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_OUT_PROD: - { - n_tasks = n_threads; - } break; - case GGML_OP_GET_ROWS: - { - // FIXME: get_rows can use additional threads, but the cost of launching additional threads - // decreases performance with GPU offloading - //n_tasks = n_threads; - n_tasks = 1; - } break; - case GGML_OP_SCALE: - case GGML_OP_SET: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS_BACK: - case GGML_OP_DIAG: - { - n_tasks = 1; - } break; - case GGML_OP_DIAG_MASK_ZERO: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_ADD_REL_POS: - { - n_tasks = n_threads; - } break; - case GGML_OP_CLAMP: - { - n_tasks = 1; //TODO - } break; - case GGML_OP_SOFT_MAX: - { - n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); - } break; - case GGML_OP_IM2COL: - case GGML_OP_IM2COL_BACK: - case GGML_OP_CONV_TRANSPOSE_1D: - case GGML_OP_CONV_TRANSPOSE_2D: - { - n_tasks = n_threads; - } break; - case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: - case GGML_OP_POOL_2D_BACK: - { - n_tasks = 1; - } break; - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_ARGSORT: - case GGML_OP_FLASH_ATTN_EXT: - case GGML_OP_FLASH_ATTN_BACK: - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - { - n_tasks = n_threads; - } break; - case GGML_OP_WIN_PART: - case GGML_OP_WIN_UNPART: - case GGML_OP_GET_REL_POS: - case GGML_OP_RWKV_WKV: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - { - n_tasks = 1; - } break; - case GGML_OP_MAP_CUSTOM1: - { - struct ggml_map_custom1_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM2: - { - struct ggml_map_custom2_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM3: - { - struct ggml_map_custom3_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_CROSS_ENTROPY_LOSS: - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - case GGML_OP_OPT_STEP_ADAMW: - { - n_tasks = n_threads; - } break; - case GGML_OP_NONE: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - { - fprintf(stderr, "%s: op not implemented: ", __func__); - if (node->op < GGML_OP_COUNT) { - fprintf(stderr, "%s\n", ggml_op_name(node->op)); - } else { - fprintf(stderr, "%d\n", node->op); - } - GGML_ABORT("fatal error"); - } - } - - assert(n_tasks > 0); - - return n_tasks; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data); - -#if defined(_WIN32) -#include "windows.h" - -// TODO: support > 64 CPUs -bool ggml_thread_apply_affinity(bool * mask) { - HANDLE h = GetCurrentThread(); - uint64_t bitmask = 0ULL; - - assert(GGML_MAX_N_THREADS >= 64); - - for (int32_t i = 0; i < 8; i++) { - int32_t idx = i * 8; - uint8_t val = 0; - val |= mask[idx + 0] << 0; - val |= mask[idx + 1] << 1; - val |= mask[idx + 2] << 2; - val |= mask[idx + 3] << 3; - val |= mask[idx + 4] << 4; - val |= mask[idx + 5] << 5; - val |= mask[idx + 6] << 6; - val |= mask[idx + 7] << 7; - bitmask |= (uint64_t)val << idx; - } - - for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); - break; - } - } - - DWORD_PTR m = (DWORD_PTR)bitmask; - - m = SetThreadAffinityMask(h, m); - - return m != 0; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. - // This is up to the applications. - DWORD p = THREAD_PRIORITY_NORMAL; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; - case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; - case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; - case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - if (!SetThreadPriority(GetCurrentThread(), p)) { - fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); - return false; - } - - return true; -} - -#elif defined(__APPLE__) -#include -#include - -static bool ggml_thread_apply_affinity(const bool * mask) { - // Not supported on Apple platforms - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#elif defined(__gnu_linux__) -// TODO: this may not work on BSD, to be verified - -static bool ggml_thread_apply_affinity(const bool * mask) { - cpu_set_t cpuset; - int err; - - CPU_ZERO(&cpuset); - - for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); - CPU_SET(i, &cpuset); - } - } - -#ifdef __ANDROID__ - err = sched_setaffinity(0, sizeof(cpuset), &cpuset); - if (err < 0) { - err = errno; - } -#else - err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); -#endif - if (err != 0) { - fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); - return false; - } - - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#else // unsupported platforms - -static bool ggml_thread_apply_affinity(const bool * mask) { - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - UNUSED(prio); - return true; -} - -#endif - -static bool ggml_thread_cpumask_is_valid(const bool * mask) { - for (int i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { return true; } - } - return false; -} - -static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { - if (!strict) { - memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); - return; - } else { - memset(local_mask, 0, GGML_MAX_N_THREADS); - int32_t base_idx = *iter; - for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - int32_t idx = base_idx + i; - if (idx >= GGML_MAX_N_THREADS) { - // Just a cheaper modulo - idx -= GGML_MAX_N_THREADS; - } - if (global_mask[idx]) { - local_mask[idx] = 1; - *iter = idx + 1; - return; - } - } - } -} - -void ggml_threadpool_free(struct ggml_threadpool* threadpool) { - if (!threadpool) return; - -#ifndef GGML_USE_OPENMP - struct ggml_compute_state* workers = threadpool->workers; - const int n_threads = threadpool->n_threads_max; - - ggml_mutex_lock(&threadpool->mutex); - - threadpool->stop = true; - threadpool->pause = false; - - ggml_cond_broadcast(&threadpool->cond); - ggml_mutex_unlock(&threadpool->mutex); - - for (int j = 1; j < n_threads; j++) { - int32_t rc = ggml_thread_join(workers[j].thrd, NULL); - GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); - UNUSED(rc); - } - - ggml_mutex_destroy(&threadpool->mutex); - ggml_cond_destroy(&threadpool->cond); -#endif // GGML_USE_OPENMP - - GGML_ALIGNED_FREE(threadpool->workers); - GGML_ALIGNED_FREE(threadpool); -} - -#ifndef GGML_USE_OPENMP -// pause/resume must be called under mutex -static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Pausing threadpool\n"); - threadpool->pause = true; - ggml_cond_broadcast(&threadpool->cond); -} - -static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Resuming threadpool\n"); - threadpool->pause = false; - ggml_cond_broadcast(&threadpool->cond); -} -#endif - -void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (!threadpool->pause) { - ggml_threadpool_pause_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (threadpool->pause) { - ggml_threadpool_resume_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -struct ggml_cplan ggml_graph_plan( - const struct ggml_cgraph * cgraph, - int n_threads, - struct ggml_threadpool * threadpool) { - - if (threadpool == NULL) { - GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - } - if (n_threads <= 0) { - n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; - } - - size_t work_size = 0; - - struct ggml_cplan cplan; - memset(&cplan, 0, sizeof(struct ggml_cplan)); - - int max_tasks = 1; - - // thread scheduling for the different operations + work buffer size estimation - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - const int n_tasks = ggml_get_n_tasks(node, n_threads); - - max_tasks = MAX(max_tasks, n_tasks); - - size_t cur = 0; - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - { - if (ggml_is_quantized(node->type) || - // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 - (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || - (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } - } break; - case GGML_OP_ADD: - case GGML_OP_ADD1: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_ACC: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; - } - } break; - case GGML_OP_COUNT_EQUAL: - { - cur = ggml_type_size(node->type)*n_tasks; - } break; - case GGML_OP_MUL_MAT: - { - const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; - - if (node->src[1]->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); - } - } break; - case GGML_OP_MUL_MAT_ID: - { - cur = 0; - const struct ggml_tensor * src0 = node->src[0]; - const struct ggml_tensor * src1 = node->src[1]; - const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type; - if (src1->type != vec_dot_type) { - cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); - } - const int n_as = src0->ne[2]; - cur += GGML_PAD(cur, sizeof(int64_t)); // align - cur += n_as * sizeof(int64_t); // matrix_row_counts - cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows - } break; - case GGML_OP_OUT_PROD: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - GGML_ASSERT(node->src[0]->ne[3] == 1); - GGML_ASSERT(node->src[1]->ne[2] == 1); - GGML_ASSERT(node->src[1]->ne[3] == 1); - - const int64_t ne00 = node->src[0]->ne[0]; // K - const int64_t ne01 = node->src[0]->ne[1]; // Cout - const int64_t ne02 = node->src[0]->ne[2]; // Cin - - const int64_t ne10 = node->src[1]->ne[0]; // L - const int64_t ne11 = node->src[1]->ne[1]; // Cin - - if ((node->src[0]->type == GGML_TYPE_F16 || - node->src[0]->type == GGML_TYPE_BF16) && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; - cur += sizeof(ggml_fp16_t)*ne10*ne11; - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(float)*ne00*ne01*ne02; - cur += sizeof(float)*ne10*ne11; - } else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - const int64_t ne00 = node->src[0]->ne[0]; // W - const int64_t ne01 = node->src[0]->ne[1]; // H - const int64_t ne02 = node->src[0]->ne[2]; // Channels Out - const int64_t ne03 = node->src[0]->ne[3]; // Channels In - - const int64_t ne10 = node->src[1]->ne[0]; // W - const int64_t ne11 = node->src[1]->ne[1]; // H - const int64_t ne12 = node->src[1]->ne[2]; // Channels In - - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; - cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - const int64_t ne00 = node->src[0]->ne[0]; // D - - cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - const int64_t D = node->src[0]->ne[0]; - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } - } break; - - case GGML_OP_CROSS_ENTROPY_LOSS: - { - cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - break; - } - - work_size = MAX(work_size, cur); - } - - if (work_size > 0) { - work_size += CACHE_LINE_SIZE*(n_threads); - } - - cplan.threadpool = threadpool; - cplan.n_threads = MIN(max_tasks, n_threads); - cplan.work_size = work_size; - cplan.work_data = NULL; - - return cplan; -} - -static thread_ret_t ggml_graph_compute_thread(void * data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * tp = state->threadpool; - - const struct ggml_cgraph * cgraph = tp->cgraph; - const struct ggml_cplan * cplan = tp->cplan; - - set_numa_thread_affinity(state->ith); - - struct ggml_compute_params params = { - /*.ith =*/ state->ith, - /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), - /*.wsize =*/ cplan->work_size, - /*.wdata =*/ cplan->work_data, - /*.threadpool=*/ tp, - }; - - for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { - struct ggml_tensor * node = cgraph->nodes[node_n]; - - ggml_compute_forward(¶ms, node); - - if (state->ith == 0 && cplan->abort_callback && - cplan->abort_callback(cplan->abort_callback_data)) { - tp->abort = true; - tp->ec = GGML_STATUS_ABORTED; - } - - ggml_barrier(state->threadpool); - } - - return 0; -} - -#ifndef GGML_USE_OPENMP - -// check if thread is active -static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); - return (state->ith < n_threads); -} - -// check if thread is ready to proceed (exit from polling or sleeping) -static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (state->pending || threadpool->stop || threadpool->pause) { return true; } - - // check for new graph/work - int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); - if (new_graph != state->last_graph) { - state->pending = ggml_graph_compute_thread_active(state); - state->last_graph = new_graph; - } - - return state->pending; -} - -// sync thread state after polling -static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif - UNUSED(state); -} - -static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - // Skip polling for unused threads - if (!ggml_graph_compute_thread_active(state)) { - return state->pending; - } - - // This seems to make 0 ... 100 a decent range for polling level across modern processors. - // Perhaps, we can adjust it dynamically based on load and things. - const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; - - for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { - // No new work. Keep polling. - ggml_thread_cpu_relax(); - } - - return state->pending; -} - -static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (ggml_graph_compute_poll_for_work(state)) { - ggml_graph_compute_thread_sync(state); - return state->pending; - } - - ggml_mutex_lock_shared(&threadpool->mutex); - while (!ggml_graph_compute_thread_ready(state)) { - // No new work. Wait for the signal. - GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - ggml_mutex_unlock_shared(&threadpool->mutex); - - return state->pending; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * threadpool = state->threadpool; - - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(state->cpumask)) { - ggml_thread_apply_affinity(state->cpumask); - } - - while (true) { - // Check if we need to sleep - while (threadpool->pause) { - GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); - ggml_mutex_lock_shared(&threadpool->mutex); - if (threadpool->pause) { - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); - ggml_mutex_unlock_shared(&threadpool->mutex); - } - - // This needs to be checked for after the cond_wait - if (threadpool->stop) break; - - // Check if there is new work - // The main thread is the only one that can dispatch new work - - ggml_graph_compute_check_for_work(state); - if (state->pending) { - state->pending = false; - - ggml_graph_compute_thread(state); - } - } - - return (thread_ret_t) 0; -} - -// Start processing new graph -static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) -{ - // Always take the mutex here because the worker threads are doing hybrid poll/wait - - ggml_mutex_lock(&threadpool->mutex); - - GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); - - // Update the number of active threads - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - - // Indicate the graph is ready to be processed - // We need the full seq-cst fence here because of the polling threads (used in thread_sync) - atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); - - if (threadpool->pause) { - // Update main thread prio and affinity to match the threadpool settings - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - - // resume does cond broadcast - ggml_threadpool_resume_locked(threadpool); - } else { - ggml_cond_broadcast(&threadpool->cond); - } - - ggml_mutex_unlock(&threadpool->mutex); -} - -#endif // GGML_USE_OPENMP - -void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { - p->n_threads = n_threads; - p->prio = 0; // default priority (usually means normal or inherited) - p->poll = 50; // hybrid-polling enabled - p->strict_cpu = false; // no strict placement (all threads share same cpumask) - p->paused = false; // threads are ready to go - memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) -} - -struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { - struct ggml_threadpool_params p; - ggml_threadpool_params_init(&p, n_threads); - return p; -} - -bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { - if (p0->n_threads != p1->n_threads ) return false; - if (p0->prio != p1->prio ) return false; - if (p0->poll != p1->poll ) return false; - if (p0->strict_cpu != p1->strict_cpu ) return false; - return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; -} - -static struct ggml_threadpool * ggml_threadpool_new_impl( - struct ggml_threadpool_params * tpp, - struct ggml_cgraph * cgraph, - struct ggml_cplan * cplan) { - - struct ggml_threadpool * threadpool = - GGML_ALIGNED_MALLOC(sizeof(struct ggml_threadpool)); - { - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->n_graph = 0; - threadpool->n_barrier = 0; - threadpool->n_barrier_passed = 0; - threadpool->current_chunk = 0; - threadpool->stop = false; - threadpool->pause = tpp->paused; - threadpool->abort = false; - threadpool->workers = NULL; - threadpool->n_threads_max = tpp->n_threads; - threadpool->n_threads_cur = tpp->n_threads; - threadpool->poll = tpp->poll; - threadpool->prio = tpp->prio; - threadpool->ec = GGML_STATUS_SUCCESS; - } - - // Allocate and init workers state - const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; - struct ggml_compute_state * workers = GGML_ALIGNED_MALLOC(workers_size); - - memset(workers, 0, workers_size); - for (int j = 0; j < tpp->n_threads; j++) { - workers[j].threadpool = threadpool; - workers[j].ith = j; - } - - threadpool->workers = workers; - -#ifndef GGML_USE_OPENMP - ggml_mutex_init(&threadpool->mutex); - ggml_cond_init(&threadpool->cond); - - // Spin the threads for all workers, and update CPU placements. - // Place the main thread last (towards the higher numbered CPU cores). - - int32_t cpumask_iter = 0; - - for (int j = 1; j < tpp->n_threads; j++) { - ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); - - int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); - GGML_ASSERT(rc == 0); - } - - ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); - - if (!threadpool->pause) { - // Update main thread prio and affinity at the start, otherwise we'll do it in resume - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - } -#endif // GGML_USE_OPENMP - - return threadpool; -} - -struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { - return ggml_threadpool_new_impl(tpp, NULL, NULL); -} - -enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { - GGML_ASSERT(cplan); - GGML_ASSERT(cplan->n_threads > 0); - GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); - - int n_threads = cplan->n_threads; - struct ggml_threadpool * threadpool = cplan->threadpool; - - bool disposable_threadpool = false; - - if (threadpool == NULL) { - GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - disposable_threadpool = true; - - struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); - threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); - } else { - // Reset some of the parameters that need resetting - // No worker threads should be accessing the parameters below at this stage - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->current_chunk = 0; - threadpool->abort = false; - threadpool->ec = GGML_STATUS_SUCCESS; - } - -#ifdef GGML_USE_OPENMP - if (n_threads > 1) { - #pragma omp parallel num_threads(n_threads) - { - #pragma omp single - { - // update the number of threads from the actual number of threads that we got from OpenMP - n_threads = omp_get_num_threads(); - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - } - - ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); - } - } else { - atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); - ggml_graph_compute_thread(&threadpool->workers[0]); - } -#else - if (n_threads > threadpool->n_threads_max) { - GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); - n_threads = threadpool->n_threads_max; - } - - // Kick all threads to start the new graph - ggml_graph_compute_kickoff(threadpool, n_threads); - - // This is a work thread too - ggml_graph_compute_thread(&threadpool->workers[0]); -#endif - - // don't leave affinity set on the main thread - clear_numa_thread_affinity(); - - enum ggml_status ret = threadpool->ec; - - if (disposable_threadpool) { - ggml_threadpool_free(threadpool); - } - - return ret; -} - -enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { - struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); - - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - return ggml_graph_compute(cgraph, &cplan); -} - -struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { +struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, const char * name) { for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * leaf = cgraph->leafs[i]; @@ -20226,489 +5943,14 @@ struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const ch return NULL; } -static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) { - const int64_t * ne = tensor->ne; - const size_t * nb = tensor->nb; - - fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", - ggml_type_name(tensor->type), - ggml_op_name (tensor->op), - ggml_n_dims(tensor), - ne[0], ne[1], ne[2], ne[3], - nb[0], nb[1], nb[2], nb[3], - tensor->data, - tensor->name); +struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL; } -static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) { - const int64_t * ne = tensor->ne; - const size_t * nb = tensor->nb; - - fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", - arg, - ggml_type_name(tensor->type), - ggml_op_name (tensor->op), - ggml_n_dims(tensor), - ne[0], ne[1], ne[2], ne[3], - nb[0], nb[1], nb[2], nb[3], - tensor->data, - tensor->name); -} - -void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { - uint64_t size_eval = 0; - - // compute size of intermediate results - // TODO: does not take into account scratch buffers !!!! - for (int i = 0; i < cgraph->n_nodes; ++i) { - size_eval += ggml_nbytes_pad(cgraph->nodes[i]); - } - - // print - { - FILE * fout = stdout; - - fprintf(fout, "\n"); - fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); - fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); - fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); - fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); - fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval); - - // header - fprintf(fout, "\n"); - fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n", - "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME"); - - for (int i = 0; i < cgraph->n_leafs; ++i) { - ggml_graph_export_leaf(cgraph->leafs[i], fout); - - GGML_ASSERT(cgraph->leafs[i]->op == GGML_OP_NONE); - GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL); - GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL); - } - - // header - fprintf(fout, "\n"); - fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n", - "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME"); - - for (int i = 0; i < cgraph->n_nodes; ++i) { - ggml_graph_export_node(cgraph->nodes[i], "DST", fout); - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (cgraph->nodes[i]->src[j]) { - ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout); - } - } - - fprintf(fout, "\n"); - } - - fprintf(fout, "\n"); - } - - // write binary data - { - FILE * fout = ggml_fopen(fname, "wb"); - - if (!fout) { - fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno)); - return; - } - - // header - { - const uint32_t magic = GGML_FILE_MAGIC; - const uint32_t version = GGML_FILE_VERSION; - const uint32_t n_leafs = cgraph->n_leafs; - const uint32_t n_nodes = cgraph->n_nodes; - - fwrite(&magic, sizeof(uint32_t), 1, fout); - fwrite(&version, sizeof(uint32_t), 1, fout); - fwrite(&n_leafs, sizeof(uint32_t), 1, fout); - fwrite(&n_nodes, sizeof(uint32_t), 1, fout); - fwrite(&size_eval, sizeof(uint64_t), 1, fout); - } - - // leafs - { - for (int i = 0; i < cgraph->n_leafs; ++i) { - const struct ggml_tensor * tensor = cgraph->leafs[i]; - - const uint32_t type = tensor->type; - const uint32_t op = tensor->op; - const int32_t flags = tensor->flags; - - fwrite(&type, sizeof(uint32_t), 1, fout); - fwrite(&op, sizeof(uint32_t), 1, fout); - fwrite(&flags, sizeof(int32_t), 1, fout); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - const uint64_t ne = tensor->ne[j]; - const uint64_t nb = tensor->nb[j]; - - fwrite(&ne, sizeof(uint64_t), 1, fout); - fwrite(&nb, sizeof(uint64_t), 1, fout); - } - - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); - fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); - - // dump the data - // TODO: pad this to 32 byte boundary - { - const size_t size = ggml_nbytes(tensor); - - fwrite(tensor->data, sizeof(char), size, fout); - } - } - } - - // nodes - { - for (int i = 0; i < cgraph->n_nodes; ++i) { - const struct ggml_tensor * tensor = cgraph->nodes[i]; - - const uint32_t type = tensor->type; - const uint32_t op = tensor->op; - const int32_t flags = tensor->flags; - - fwrite(&type, sizeof(uint32_t), 1, fout); - fwrite(&op, sizeof(uint32_t), 1, fout); - fwrite(&flags, sizeof(int32_t), 1, fout); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - const uint64_t ne = tensor->ne[j]; - const uint64_t nb = tensor->nb[j]; - - fwrite(&ne, sizeof(uint64_t), 1, fout); - fwrite(&nb, sizeof(uint64_t), 1, fout); - } - - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); - fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); - - // output the op arguments - { - struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - args[j] = tensor->src[j]; - } - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (args[j]) { - int32_t idx = -1; - - // check if leaf - { - for (int k = 0; k < cgraph->n_leafs; ++k) { - if (args[j] == cgraph->leafs[k]) { - idx = k; - break; - } - } - } - - // check if node - if (idx == -1) { - for (int k = 0; k < cgraph->n_nodes; ++k) { - if (args[j] == cgraph->nodes[k]) { - idx = cgraph->n_leafs + k; - break; - } - } - } - - if (idx == -1) { - fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i); - fclose(fout); - return; - } - - fwrite(&idx, sizeof(int32_t), 1, fout); - } else { - const int32_t nul = -1; - - fwrite(&nul, sizeof(int32_t), 1, fout); - } - } - } - - // dump the data - // TODO: pad this to 32 byte boundary - if ((flags & GGML_TENSOR_FLAG_PARAM)) { - const size_t size = ggml_nbytes(tensor); - - fwrite(tensor->data, sizeof(char), size, fout); - } - } - } - - fclose(fout); - } -} - -struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { - assert(*ctx_data == NULL); - assert(*ctx_eval == NULL); - - struct ggml_cgraph * result = NULL; - - struct ggml_tensor * data = NULL; - - // read file into data - { - FILE * fin = ggml_fopen(fname, "rb"); - if (!fin) { - fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno)); - return result; - } - - size_t fsize = 0; - - fseek(fin, 0, SEEK_END); - fsize = ftell(fin); - fseek(fin, 0, SEEK_SET); - - // create the data context - { - const size_t overhead = 1*ggml_tensor_overhead(); - - struct ggml_init_params params = { - .mem_size = fsize + overhead, - .mem_buffer = NULL, - .no_alloc = false, - }; - - *ctx_data = ggml_init(params); - - if (!*ctx_data) { - fprintf(stderr, "%s: failed to create ggml context\n", __func__); - fclose(fin); - return result; - } - } - - data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize); - - { - const size_t ret = fread(data->data, sizeof(char), fsize, fin); - if (ret != fsize) { - fprintf(stderr, "%s: failed to read %s\n", __func__, fname); - fclose(fin); - return result; - } - } - - fclose(fin); - } - - // populate result - { - char * ptr = (char *) data->data; - - const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic); - - if (magic != GGML_FILE_MAGIC) { - fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic); - return result; - } - - const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version); - - if (version != GGML_FILE_VERSION) { - fprintf(stderr, "%s: invalid version number\n", __func__); - return result; - } - - const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); - const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); - const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); - const int graph_size = MAX(n_leafs, n_nodes); - - // create the data context - { - const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false); - - struct ggml_init_params params = { - .mem_size = size_eval + overhead, - .mem_buffer = NULL, - .no_alloc = true, - }; - - *ctx_eval = ggml_init(params); - - if (!*ctx_eval) { - fprintf(stderr, "%s: failed to create ggml context\n", __func__); - return result; - } - } - - result = ggml_new_graph_custom(*ctx_eval, graph_size, false); - - result->n_leafs = n_leafs; - result->n_nodes = n_nodes; - - - // leafs - { - uint32_t type; - uint32_t op; - int32_t flags; - - for (uint32_t i = 0; i < n_leafs; ++i) { - type = *(const uint32_t *) ptr; ptr += sizeof(type); - op = *(const uint32_t *) ptr; ptr += sizeof(op); - flags = *(const int32_t *) ptr; ptr += sizeof(flags); - - int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - uint64_t ne_cur; - uint64_t nb_cur; - - ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); - nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); - - ne[j] = ne_cur; - nb[j] = nb_cur; - } - - struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); - - tensor->op = (enum ggml_op) op; - tensor->flags = flags; - - memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; - memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - tensor->nb[j] = nb[j]; - } - - tensor->data = (void *) ptr; ptr += ggml_nbytes(tensor); - - result->leafs[i] = tensor; - - fprintf(stderr, "%s: loaded leaf %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); - } - } - - ggml_set_no_alloc(*ctx_eval, false); - - // nodes - { - uint32_t type; - uint32_t op; - int32_t flags; - - for (uint32_t i = 0; i < n_nodes; ++i) { - type = *(const uint32_t *) ptr; ptr += sizeof(type); - op = *(const uint32_t *) ptr; ptr += sizeof(op); - flags = *(const int32_t *) ptr; ptr += sizeof(flags); - - enum ggml_op eop = (enum ggml_op) op; - - int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - uint64_t ne_cur; - uint64_t nb_cur; - - ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); - nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); - - ne[j] = ne_cur; - nb[j] = nb_cur; - } - - const char * ptr_name = ptr; ptr += GGML_MAX_NAME; - const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS; - - const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t); - - struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; - - // parse args - for (int j = 0; j < GGML_MAX_SRC; ++j) { - const int32_t arg_idx = ptr_arg_idx[j]; - - if (arg_idx == -1) { - continue; - } - - if (arg_idx < result->n_leafs) { - args[j] = result->leafs[arg_idx]; - } else { - args[j] = result->nodes[arg_idx - result->n_leafs]; - } - } - - // create the tensor - // "view" operations are handled differently - // TODO: handle inplace ops - currently a copy is always made - - struct ggml_tensor * tensor = NULL; - - switch (eop) { - // TODO: implement other view ops - case GGML_OP_RESHAPE: - { - tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]); - } break; - case GGML_OP_VIEW: - { - tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); - - size_t offs; - memcpy(&offs, ptr_op_params, sizeof(offs)); - - tensor->data = ((char *) tensor->data) + offs; - } break; - case GGML_OP_TRANSPOSE: - { - tensor = ggml_transpose(*ctx_eval, args[0]); - } break; - case GGML_OP_PERMUTE: - { - tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); - } break; - default: - { - tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); - - tensor->op = eop; - } break; - } - - memcpy(tensor->name, ptr_name, GGML_MAX_NAME); - memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - tensor->nb[j] = nb[j]; - } - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - tensor->src[j] = args[j]; - } - - result->nodes[i] = tensor; - - // TODO tensor data is be duplicated due to ggml_new_tensor call above - if (flags & GGML_TENSOR_FLAG_PARAM) { - tensor->data = (void *) ptr; ptr += ggml_nbytes(tensor); - } - - fprintf(stderr, "%s: loaded node %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); - } - } - } - - return result; +struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL; } void ggml_graph_print(const struct ggml_cgraph * cgraph) { @@ -20721,7 +5963,8 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) { GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n", i, node->ne[0], node->ne[1], node->ne[2], - ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " "); + ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : + ggml_graph_get_grad(cgraph, node) ? "g" : " "); } GGML_LOG_INFO("n_leafs = %d\n", cgraph->n_leafs); @@ -20756,8 +5999,9 @@ static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, parent); - if (parent->grad == node) { + if (grad == node) { return parent; } } @@ -20797,6 +6041,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(gb, node); if (ggml_graph_get_parent(gb, node) != NULL) { continue; @@ -20804,7 +6049,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph if (node->flags & GGML_TENSOR_FLAG_PARAM) { snprintf(color, sizeof(color), "yellow"); - } else if (node->grad) { + } else if (grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { @@ -20831,8 +6076,8 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op)); } - if (node->grad) { - fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(node->grad->op)); + if (grad) { + fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(grad->op)); } else { fprintf(fp, "\"; ]\n"); } @@ -20858,15 +6103,17 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph if (ggml_nelements(node) < 5 && node->data != NULL) { fprintf(fp, " | ("); for (int j = 0; j < ggml_nelements(node); j++) { - if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { - fprintf(fp, "%d", ggml_get_i32_1d(node, j)); - } - else if (node->type == GGML_TYPE_F32 || - node->type == GGML_TYPE_F16 || - node->type == GGML_TYPE_BF16) { - fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); - } - else { + // FIXME: use ggml-backend to obtain the tensor data + //if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { + // fprintf(fp, "%d", ggml_get_i32_1d(node, j)); + //} + //else if (node->type == GGML_TYPE_F32 || + // node->type == GGML_TYPE_F16 || + // node->type == GGML_TYPE_BF16) { + // fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); + //} + //else + { fprintf(fp, "#"); } if (j < ggml_nelements(node) - 1) { @@ -20911,918 +6158,6 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph //////////////////////////////////////////////////////////////////////////////// -static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { - int i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to set tensor from array - for (int64_t j = 0; j < ne; ++j) { - ggml_set_f32_1d(ps[p], j, x[i++]); - } - } -} - -static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { - int i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - x[i++] = ggml_get_f32_1d(ps[p], j); - } - } -} - -static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - g[i++] = ggml_get_f32_1d(ps[p]->grad, j); - } - } -} - -static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g, float scale) { - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - g[i++] += ggml_get_f32_1d(ps[p]->grad, j) * scale; - } - } -} - -// -// Using AdamW - ref: https://arxiv.org/pdf/1711.05101v3.pdf -// -// (Original Adam - ref: https://arxiv.org/pdf/1412.6980.pdf) -// - -static enum ggml_opt_result ggml_opt_adam( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - GGML_ASSERT(ggml_is_scalar(f)); - GGML_ASSERT(f->type == GGML_TYPE_F32); - - // these will store the parameters we want to optimize - struct ggml_tensor * ps[GGML_MAX_PARAMS]; - - int np = 0; - int64_t nx = 0; - for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); - - GGML_ASSERT(np < GGML_MAX_PARAMS); - - ps[np++] = gf->nodes[i]; - nx += ggml_nelements(gf->nodes[i]); - } - } - - if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) { - int iter = opt->iter; - ggml_opt_init(opt->ctx, opt, params, nx); - opt->iter = iter; - } - - // constants - float sched = params.adam.sched; - const float alpha = params.adam.alpha; - const float decay = params.adam.decay * alpha; - const float beta1 = params.adam.beta1; - const float beta2 = params.adam.beta2; - const float eps = params.adam.eps; - const float gclip = params.adam.gclip; - const int decay_min_ndim = params.adam.decay_min_ndim; - const int n_accum = MAX(1, params.n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - float * g = opt->adam.g->data; // gradients - float * m = opt->adam.m->data; // first moment - float * v = opt->adam.v->data; // second moment - - float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values - - struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads, NULL); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - bool cancel = false; - - // compute the function value - float fx = 0; - ggml_set_zero(opt->adam.g); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->adam.fx_prev = fx; - opt->adam.fx_best = opt->adam.fx_prev; - if (pf) { - pf[opt->iter % params.past] = opt->adam.fx_prev; - } - - opt->loss_before = opt->adam.fx_prev; - opt->loss_after = opt->adam.fx_prev; - - // initialize - if (opt->just_initialized) { - opt->adam.n_no_improvement = 0; - opt->just_initialized = false; - } - - float * fx_best = &opt->adam.fx_best; - float * fx_prev = &opt->adam.fx_prev; - int * n_no_improvement = &opt->adam.n_no_improvement; - - int iter0 = opt->iter; - - // run the optimizer - for (int t = 0; t < params.adam.n_iter; ++t) { - opt->iter = iter0 + t + 1; - GGML_PRINT_DEBUG ("=== iter %d ===\n", t); - - GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); - GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); - GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); - - for (int i = 0; i < np; ++i) { - GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, - ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); - } - - const int64_t t_start_wall = ggml_time_us(); - const int64_t t_start_cpu = ggml_cycles(); - UNUSED(t_start_wall); - UNUSED(t_start_cpu); - - { - float gnorm = 1.0f; - if (gclip > 0.0f) { - // gradient clipping - ggml_float sum = 0.0; - for (int64_t i = 0; i < nx; ++i) { - sum += (ggml_float)(g[i]*g[i]); - } - ggml_float norm = sqrt(sum); - if (norm > (ggml_float) gclip) { - gnorm = (float) ((ggml_float) gclip / norm); - } - } - const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, opt->iter)); - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]); - const float p_decay = ((ggml_n_dims(ps[p]) >= decay_min_ndim) ? decay : 0.0f) * sched; - for (int64_t j = 0; j < ne; ++j) { - float x = ggml_get_f32_1d(ps[p], j); - float g_ = g[i]*gnorm; - m[i] = m[i]*beta1 + g_*(1.0f - beta1); - v[i] = v[i]*beta2 + g_*g_*(1.0f - beta2); - float mh = m[i]*beta1h; - float vh = v[i]*beta2h; - vh = sqrtf(vh) + eps; - x = x*(1.0f - p_decay) - mh/vh; - ggml_set_f32_1d(ps[p], j, x); - ++i; - } - } - } - - fx = 0; - ggml_set_zero(opt->adam.g); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL;; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->loss_after = fx; - - // check convergence - if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { - GGML_PRINT_DEBUG("converged\n"); - - return GGML_OPT_RESULT_OK; - } - - // delta-based convergence test - if (pf != NULL) { - // need at least params.past iterations to start checking for convergence - if (params.past <= iter0 + t) { - const float rate = (pf[(iter0 + t)%params.past] - fx)/fx; - - if (fabsf(rate) < params.delta) { - return GGML_OPT_RESULT_OK; - } - } - - pf[(iter0 + t)%params.past] = fx; - } - - // check for improvement - if (params.max_no_improvement > 0) { - if (fx_best[0] > fx) { - fx_best[0] = fx; - n_no_improvement[0] = 0; - } else { - ++n_no_improvement[0]; - - if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_RESULT_OK; - } - } - } - - fx_prev[0] = fx; - - { - const int64_t t_end_cpu = ggml_cycles(); - GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); - UNUSED(t_end_cpu); - - const int64_t t_end_wall = ggml_time_us(); - GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); - UNUSED(t_end_wall); - } - } - - return GGML_OPT_RESULT_DID_NOT_CONVERGE; -} - -// -// L-BFGS -// -// the L-BFGS implementation below is based on the following implementation: -// -// https://github.com/chokkan/liblbfgs -// - -struct ggml_lbfgs_iteration_data { - float alpha; - float ys; - float * s; - float * y; -}; - -static enum ggml_opt_result linesearch_backtracking( - const struct ggml_opt_params * params, - int nx, - float * x, - float * fx, - float * g, - float * d, - float * step, - const float * xp, - struct ggml_tensor * f, - struct ggml_cgraph * gb, - struct ggml_cplan * cplan, - const int np, - struct ggml_tensor * ps[], - bool * cancel, - ggml_opt_callback callback, - void * callback_data) { - int count = 0; - - float width = 0.0f; - float dg = 0.0f; - float finit = 0.0f; - float dginit = 0.0f; - float dgtest = 0.0f; - - const float dec = 0.5f; - const float inc = 2.1f; - - const int n_accum = MAX(1, params->n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - if (*step <= 0.f) { - return GGML_LINESEARCH_INVALID_PARAMETERS; - } - - // compute the initial gradient in the search direction - ggml_vec_dot_f32(nx, &dginit, 0, g, 0, d, 0, 1); - - // make sure that d points to a descent direction - if (0 < dginit) { - return GGML_LINESEARCH_FAIL; - } - - // initialize local variables - finit = *fx; - dgtest = params->lbfgs.ftol*dginit; - - while (true) { - ggml_vec_cpy_f32(nx, x, xp); - ggml_vec_mad_f32(nx, x, d, *step); - - // evaluate the function and gradient values - { - ggml_opt_set_params(np, ps, x); - - *fx = 0; - memset(g, 0, sizeof(float)*nx); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - // LBFG-S does not support learning rate -> ignore learning schedule - float sched = 0; - callback(callback_data, accum_step, &sched, cancel); - if (*cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - *fx += ggml_get_f32_1d(f, 0); - } - *fx *= accum_norm; - - } - - ++count; - - if (*fx > finit + (*step)*dgtest) { - width = dec; - } else { - // Armijo condition is satisfied - if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) { - return count; - } - - ggml_vec_dot_f32(nx, &dg, 0, g, 0, d, 0, 1); - - // check the Wolfe condition - if (dg < params->lbfgs.wolfe * dginit) { - width = inc; - } else { - if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) { - // regular Wolfe conditions - return count; - } - - if(dg > -params->lbfgs.wolfe*dginit) { - width = dec; - } else { - // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) - return count; - } - } - } - - if (*step < params->lbfgs.min_step) { - return GGML_LINESEARCH_MINIMUM_STEP; - } - if (*step > params->lbfgs.max_step) { - return GGML_LINESEARCH_MAXIMUM_STEP; - } - if (params->lbfgs.max_linesearch <= count) { - return GGML_LINESEARCH_MAXIMUM_ITERATIONS; - } - - (*step) *= width; - } - - GGML_ABORT("line search failed"); - - //return GGML_LINESEARCH_FAIL; -} - -static enum ggml_opt_result ggml_opt_lbfgs( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || - params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { - if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { - return GGML_OPT_RESULT_INVALID_WOLFE; - } - } - - const int m = params.lbfgs.m; - - // these will store the parameters we want to optimize - struct ggml_tensor * ps[GGML_MAX_PARAMS]; - - int np = 0; - int nx = 0; - for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); - - GGML_ASSERT(np < GGML_MAX_PARAMS); - - ps[np++] = gf->nodes[i]; - nx += ggml_nelements(gf->nodes[i]); - } - } - - if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) { - int iter = opt->iter; - ggml_opt_init(ctx, opt, params, nx); - opt->iter = iter; - } - - struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads, NULL); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - float * x = opt->lbfgs.x->data; // current parameters - float * xp = opt->lbfgs.xp->data; // previous parameters - float * g = opt->lbfgs.g->data; // current gradient - float * gp = opt->lbfgs.gp->data; // previous gradient - float * d = opt->lbfgs.d->data; // search direction - - float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values - - const int n_accum = MAX(1, params.n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - float fx = 0.0f; // cost function value - float xnorm = 0.0f; // ||x|| - float gnorm = 0.0f; // ||g|| - - // initialize x from the graph nodes - ggml_opt_get_params(np, ps, x); - - // the L-BFGS memory - float * lm_alpha = opt->lbfgs.lmal->data; - float * lm_ys = opt->lbfgs.lmys->data; - float * lm_s = opt->lbfgs.lms->data; - float * lm_y = opt->lbfgs.lmy->data; - - bool cancel = false; - - // evaluate the function value and its gradient - { - ggml_opt_set_params(np, ps, x); - - fx = 0; - memset(g, 0, sizeof(float)*nx); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - // LBFG-S does not support learning rate -> ignore learning schedule - float sched = 0; - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->loss_before = fx; - opt->loss_after = fx; - } - - // search direction = -gradient - ggml_vec_neg_f32(nx, d, g); - - // ||x||, ||g|| - ggml_vec_norm_f32(nx, &xnorm, x); - ggml_vec_norm_f32(nx, &gnorm, g); - - if (xnorm < 1.0f) { - xnorm = 1.0f; - } - - // already optimized - if (gnorm/xnorm <= params.lbfgs.eps) { - return GGML_OPT_RESULT_OK; - } - - if (opt->just_initialized) { - if (pf) { - pf[0] = fx; - } - opt->lbfgs.fx_best = fx; - - // initial step - ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d); - opt->lbfgs.j = 0; - opt->lbfgs.k = 1; - opt->lbfgs.end = 0; - opt->lbfgs.n_no_improvement = 0; - opt->just_initialized = false; - } - - float * fx_best = &opt->lbfgs.fx_best; - float * step = &opt->lbfgs.step; - int * j = &opt->lbfgs.j; - int * k = &opt->lbfgs.k; - int * end = &opt->lbfgs.end; - int * n_no_improvement = &opt->lbfgs.n_no_improvement; - - int ls = 0; - int bound = 0; - - float ys = 0.0f; - float yy = 0.0f; - float beta = 0.0f; - - int it = 0; - - while (true) { - // store the current position and gradient vectors - ggml_vec_cpy_f32(nx, xp, x); - ggml_vec_cpy_f32(nx, gp, g); - - // TODO: instead of passing &cancel here, use the return code of the linesearch - // to determine if the optimization should be cancelled - // this is a simple change, but not doing this atm, since I don't have a nice - // way to test and don't want to break something with so many changes lined up - ls = linesearch_backtracking(¶ms, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - - if (ls < 0) { - // linesearch failed - go back to the previous point and return - ggml_vec_cpy_f32(nx, x, xp); - ggml_vec_cpy_f32(nx, g, gp); - - return ls; - } - - opt->loss_after = fx; - - ggml_vec_norm_f32(nx, &xnorm, x); - ggml_vec_norm_f32(nx, &gnorm, g); - - GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); - - if (xnorm < 1.0f) { - xnorm = 1.0f; - } - if (gnorm/xnorm <= params.lbfgs.eps) { - // converged - return GGML_OPT_RESULT_OK; - } - - // delta-based convergence test - if (pf != NULL) { - // need at least params.past iterations to start checking for convergence - if (params.past <= k[0]) { - const float rate = (pf[k[0]%params.past] - fx)/fx; - - if (fabsf(rate) < params.delta) { - return GGML_OPT_RESULT_OK; - } - } - - pf[k[0]%params.past] = fx; - } - - // check for improvement - if (params.max_no_improvement > 0) { - if (fx < fx_best[0]) { - fx_best[0] = fx; - n_no_improvement[0] = 0; - } else { - n_no_improvement[0]++; - - if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_RESULT_OK; - } - } - } - - if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) { - // reached the maximum number of iterations - return GGML_OPT_RESULT_DID_NOT_CONVERGE; - } - - // update vectors s and y: - // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. - // y_{k+1} = g_{k+1} - g_{k}. - // - ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp); - ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp); - - // compute scalars ys and yy: - // ys = y^t \cdot s -> 1 / \rho. - // yy = y^t \cdot y. - // - ggml_vec_dot_f32(nx, &ys, 0, &lm_y[end[0]*nx], 0, &lm_s[end[0]*nx], 0, 1); - ggml_vec_dot_f32(nx, &yy, 0, &lm_y[end[0]*nx], 0, &lm_y[end[0]*nx], 0, 1); - - lm_ys[end[0]] = ys; - - // find new search direction - // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS - - bound = (m <= k[0]) ? m : k[0]; - k[0]++; - it++; - end[0] = (end[0] + 1)%m; - - // initialize search direction with -g - ggml_vec_neg_f32(nx, d, g); - - j[0] = end[0]; - for (int i = 0; i < bound; ++i) { - j[0] = (j[0] + m - 1) % m; - // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} - ggml_vec_dot_f32(nx, &lm_alpha[j[0]], 0, &lm_s[j[0]*nx], 0, d, 0, 1); - lm_alpha[j[0]] /= lm_ys[j[0]]; - // q_{i} = q_{i+1} - \alpha_{i} y_{i} - ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]); - } - - ggml_vec_scale_f32(nx, d, ys/yy); - - for (int i = 0; i < bound; ++i) { - // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} - ggml_vec_dot_f32(nx, &beta, 0, &lm_y[j[0]*nx], 0, d, 0, 1); - beta /= lm_ys[j[0]]; - // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} - ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta); - j[0] = (j[0] + 1)%m; - } - - step[0] = 1.0; - } - - GGML_ABORT("lbfgs failed"); - - //return GGML_OPT_RESULT_DID_NOT_CONVERGE; -} - -struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { - struct ggml_opt_params result; - - switch (type) { - case GGML_OPT_TYPE_ADAM: - { - result = (struct ggml_opt_params) { - .type = GGML_OPT_TYPE_ADAM, - .graph_size = GGML_DEFAULT_GRAPH_SIZE, - .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? - .past = 0, - .delta = 1e-5f, - - .max_no_improvement = 100, - - .print_forward_graph = true, - .print_backward_graph = true, - - .n_gradient_accumulation = 1, - - .adam = { - .n_iter = 10000, - .sched = 1.000f, - .decay = 0.0f, - .decay_min_ndim = 2, - .alpha = 0.001f, - .beta1 = 0.9f, - .beta2 = 0.999f, - .eps = 1e-8f, - .eps_f = 1e-5f, - .eps_g = 1e-3f, - .gclip = 0.0f, - }, - }; - } break; - case GGML_OPT_TYPE_LBFGS: - { - result = (struct ggml_opt_params) { - .type = GGML_OPT_TYPE_LBFGS, - .graph_size = GGML_DEFAULT_GRAPH_SIZE, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, - - .max_no_improvement = 0, - - .print_forward_graph = true, - .print_backward_graph = true, - - .n_gradient_accumulation = 1, - - .lbfgs = { - .m = 6, - .n_iter = 100, - .max_linesearch = 20, - - .eps = 1e-5f, - .ftol = 1e-4f, - .wolfe = 0.9f, - .min_step = 1e-20f, - .max_step = 1e+20f, - - .linesearch = GGML_LINESEARCH_DEFAULT, - }, - }; - } break; - } - - return result; -} - -GGML_API void ggml_opt_init( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - int64_t nx) { - opt->ctx = ctx; - opt->params = params; - opt->iter = 0; - opt->nx = nx; - opt->just_initialized = true; - if (opt->ctx == NULL) { - struct ggml_init_params ctx_opt_params; - if (opt->params.type == GGML_OPT_TYPE_ADAM) { - ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3; - if (opt->params.past > 0) { - ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; - } - } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) { - ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2); - if (opt->params.past > 0) { - ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; - } - } - ctx_opt_params.mem_buffer = NULL; - ctx_opt_params.no_alloc = false; - - opt->ctx = ggml_init(ctx_opt_params); - } - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - opt->adam.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.m = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.v = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.pf = params.past > 0 - ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) - : NULL; - ggml_set_zero(opt->adam.m); - ggml_set_zero(opt->adam.v); - if (opt->adam.pf) { - ggml_set_zero(opt->adam.pf); - } - } break; - case GGML_OPT_TYPE_LBFGS: - { - opt->lbfgs.x = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.gp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.d = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.pf = params.past > 0 - ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) - : NULL; - opt->lbfgs.lmal = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); - opt->lbfgs.lmys = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); - opt->lbfgs.lms = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); - opt->lbfgs.lmy = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); - ggml_set_zero(opt->lbfgs.x); - ggml_set_zero(opt->lbfgs.xp); - ggml_set_zero(opt->lbfgs.g); - ggml_set_zero(opt->lbfgs.gp); - ggml_set_zero(opt->lbfgs.d); - if (opt->lbfgs.pf) { - ggml_set_zero(opt->lbfgs.pf); - } - ggml_set_zero(opt->lbfgs.lmal); - ggml_set_zero(opt->lbfgs.lmys); - ggml_set_zero(opt->lbfgs.lms); - ggml_set_zero(opt->lbfgs.lmy); - } break; - } -} - -enum ggml_opt_result ggml_opt( - struct ggml_context * ctx, - struct ggml_opt_params params, - struct ggml_tensor * f) { - bool free_ctx = false; - if (ctx == NULL) { - struct ggml_init_params params_ctx = { - .mem_size = 16*1024*1024, - .mem_buffer = NULL, - .no_alloc = false, - }; - - ctx = ggml_init(params_ctx); - if (ctx == NULL) { - return GGML_OPT_RESULT_NO_CONTEXT; - } - - free_ctx = true; - } - - enum ggml_opt_result result = GGML_OPT_RESULT_OK; - - struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context)); - - ggml_opt_init(ctx, opt, params, 0); - result = ggml_opt_resume(ctx, opt, f); - - if (free_ctx) { - ggml_free(ctx); - } - - return result; -} - -enum ggml_opt_result ggml_opt_resume( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f) { - - // build forward + backward compute graphs - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, opt->params.graph_size, true); - ggml_build_forward_expand(gf, f); - - struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); - ggml_build_backward_expand(ctx, gf, gb, false); - - return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); -} - -enum ggml_opt_result ggml_opt_resume_g( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - - GGML_ASSERT(f->grad && "ggml_set_param must be called for at least one ancestor"); - - // build forward + backward compute graphs - enum ggml_opt_result result = GGML_OPT_RESULT_OK; - - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data); - } break; - case GGML_OPT_TYPE_LBFGS: - { - result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data); - } break; - } - - if (opt->params.print_forward_graph) { - ggml_graph_print (gf); - ggml_graph_dump_dot(gf, NULL, "opt-forward.dot"); - } - - if (opt->params.print_backward_graph) { - ggml_graph_print (gb); - ggml_graph_dump_dot(gb, gf, "opt-backward.dot"); - } - - return result; -} - -//////////////////////////////////////////////////////////////////////////////// - void ggml_set_input(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_INPUT; } @@ -22070,18 +6405,46 @@ static size_t gguf_type_size(enum gguf_type type) { return GGUF_TYPE_SIZE[type]; } -static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { - GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS); - GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT); +static bool gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { + if (info->n_dims > GGML_MAX_DIMS) { + fprintf(stderr, "%s: invalid number of dimensions (%" PRIu32 ")\n", __func__, info->n_dims); + return false; + } + + if (info->type < 0 || info->type >= GGML_TYPE_COUNT) { + fprintf(stderr, "%s: invalid type (%d)\n", __func__, info->type); + return false; + } + + if (strlen(info->name.data) >= GGML_MAX_NAME) { + fprintf(stderr, "%s: tensor '%s' name is too long\n", __func__, info->name.data); + return false; + } for (uint32_t i = 0; i < info->n_dims; ++i) { - GGML_ASSERT(info->ne[i] > 0); + if (info->ne[i] <= 0) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[i]); + return false; + } } // prevent overflow for total number of elements - GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]); - GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]); - GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]); + if (INT64_MAX/info->ne[1] <= info->ne[0]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[1]); + return false; + } + + if (INT64_MAX/info->ne[2] <= info->ne[0]*info->ne[1]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[2]); + return false; + } + + if (INT64_MAX/info->ne[3] <= info->ne[0]*info->ne[1]*info->ne[2]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[3]); + return false; + } + + return true; } static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) { @@ -22104,7 +6467,11 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { return false; } - p->data = GGML_CALLOC(p->n + 1, 1); + p->data = calloc(p->n + 1, 1); + if (!p->data) { + fprintf(stderr, "%s: failed to allocate memory for string of length %" PRIu64 "\n", __func__, p->n); + return false; + } ok = ok && gguf_fread_el(file, p->data, p->n, offset); @@ -22138,7 +6505,11 @@ static void gguf_free_kv(struct gguf_kv * kv) { } struct gguf_context * gguf_init_empty(void) { - struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); + struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context)); + if (!ctx) { + fprintf(stderr, "%s: failed to allocate memory for context\n", __func__); + return NULL; + } memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic)); ctx->header.version = GGUF_VERSION; @@ -22184,7 +6555,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p bool ok = true; - struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); + struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context)); + if (!ctx) { + fprintf(stderr, "%s: failed to allocate memory for context\n", __func__); + fclose(file); + return NULL; + } // read the header { @@ -22223,9 +6599,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p { const uint64_t n_kv = ctx->header.n_kv; - // header.n_kv will hold the actual value of pairs that were successfully read in the loop below - ctx->header.n_kv = 0; - ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv)); + ctx->kv = calloc(n_kv, sizeof(struct gguf_kv)); + if (!ctx->kv) { + fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t i = 0; i < n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; @@ -22276,7 +6656,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); + kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); + if (!kv->value.arr.data) { + fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset); } break; @@ -22290,24 +6676,36 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str)); + kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str)); + if (!kv->value.arr.data) { + fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); } } break; case GGUF_TYPE_ARRAY: - default: GGML_ABORT("invalid type"); + default: + { + fprintf(stderr, "%s: invalid array type %d\n", __func__, kv->value.arr.type); + ok = false; + } break; } } break; - default: GGML_ABORT("invalid type"); + default: + { + fprintf(stderr, "%s: invalid type %d\n", __func__, kv->type); + ok = false; + } break; } if (!ok) { break; } - - ctx->header.n_kv++; } if (!ok) { @@ -22320,7 +6718,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the tensor infos if (ctx->header.n_tensors > 0) { - ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); + ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); + if (!ctx->infos) { + fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; @@ -22341,8 +6745,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); - // TODO: return an error instead of crashing with GGML_ASSERT - gguf_tensor_info_sanitize(info); + ok = ok && gguf_tensor_info_sanitize(info); // make sure there is no duplicated tensor names for (uint64_t j = 0; j < i && ok; ++j) { @@ -23164,246 +7567,7 @@ void gguf_get_meta_data(const struct gguf_context * ctx, void * data) { gguf_buf_free(buf); } -//////////////////////////////////////////////////////////////////////////////// - -int ggml_cpu_has_avx(void) { -#if defined(__AVX__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx_vnni(void) { -#if defined(__AVXVNNI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx2(void) { -#if defined(__AVX2__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512(void) { -#if defined(__AVX512F__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_vbmi(void) { -#if defined(__AVX512VBMI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_vnni(void) { -#if defined(__AVX512VNNI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_bf16(void) { -#if defined(__AVX512BF16__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_fma(void) { -#if defined(__FMA__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_neon(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_neon; -#else - return 0; -#endif -} - -int ggml_cpu_has_sve(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_sve; -#else - return 0; -#endif -} - -int ggml_cpu_has_arm_fma(void) { -#if defined(__ARM_FEATURE_FMA) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_riscv_v(void) { -#if defined(__riscv_v_intrinsic) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_metal(void) { -#if defined(GGML_USE_METAL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_f16c(void) { -#if defined(__F16C__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_fp16_va(void) { -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_wasm_simd(void) { -#if defined(__wasm_simd128__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_cuda(void) { -#if defined(GGML_USE_CUDA) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_vulkan(void) { -#if defined(GGML_USE_VULKAN) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_kompute(void) { -#if defined(GGML_USE_KOMPUTE) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_sycl(void) { -#if defined(GGML_USE_SYCL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_rpc(void) { -#if defined(GGML_USE_RPC) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_cann(void) { -#if defined(GGML_USE_CANN) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_llamafile(void) { -#if defined(GGML_USE_LLAMAFILE) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); -} - -int ggml_cpu_has_sse3(void) { -#if defined(__SSE3__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_ssse3(void) { -#if defined(__SSSE3__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_vsx(void) { -#if defined(__POWER9_VECTOR__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_matmul_int8(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_i8mm; -#else - return 0; -#endif -} - -int ggml_cpu_get_sve_cnt(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.sve_cnt; -#else - return 0; -#endif -} - void ggml_log_set(ggml_log_callback log_callback, void * user_data) { g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default; g_logger_state.log_callback_user_data = user_data; } -//////////////////////////////////////////////////////////////////////////////// diff --git a/ggml/src/vulkan-shaders/add.comp b/ggml/src/vulkan-shaders/add.comp deleted file mode 100644 index 3974845d63..0000000000 --- a/ggml/src/vulkan-shaders/add.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/dequant_funcs.comp b/ggml/src/vulkan-shaders/dequant_funcs.comp deleted file mode 100644 index d5b989735b..0000000000 --- a/ggml/src/vulkan-shaders/dequant_funcs.comp +++ /dev/null @@ -1,68 +0,0 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif - -#if defined(DATA_A_F32) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_F16) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_Q4_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; -} -#endif - -#if defined(DATA_A_Q4_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(vui & 0xF, vui >> 4) * d + m; -} -#endif - -#if defined(DATA_A_Q5_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; -} -#endif - -#if defined(DATA_A_Q5_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint uint_qh = data_a[a_offset + ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; -} -#endif - -#if defined(DATA_A_Q8_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; -} -#endif - -#if defined(DATA_A_IQ4_NL) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; -} -#endif diff --git a/ggml/src/vulkan-shaders/div.comp b/ggml/src/vulkan-shaders/div.comp deleted file mode 100644 index 8cfce58b15..0000000000 --- a/ggml/src/vulkan-shaders/div.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) / FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/generic_binary_head.comp b/ggml/src/vulkan-shaders/generic_binary_head.comp deleted file mode 100644 index b6beaff1cf..0000000000 --- a/ggml/src/vulkan-shaders/generic_binary_head.comp +++ /dev/null @@ -1,52 +0,0 @@ -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; - uint d_offset; - float param1; float param2; int param3; -} p; - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -uint get_idx() { - return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; -} - -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint src1_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - - return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; -} - -uint dst_idx(uint idx) { - const uint i23 = idx / (p.ne22*p.ne21*p.ne20); - const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; - const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); - const uint i22_offset = i22*p.ne21*p.ne20; - const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; - const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; - return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; -} diff --git a/ggml/src/vulkan-shaders/mul.comp b/ggml/src/vulkan-shaders/mul.comp deleted file mode 100644 index bfb61c92d6..0000000000 --- a/ggml/src/vulkan-shaders/mul.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec.comp b/ggml/src/vulkan-shaders/mul_mat_vec.comp deleted file mode 100644 index d3ccba7fcb..0000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec.comp +++ /dev/null @@ -1,56 +0,0 @@ -#version 450 - -#ifdef FLOAT16 -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require -#endif - -#include "mul_mat_vec_base.comp" - -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (constant_id = 0) const uint BLOCK_SIZE = 32; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - const uint tid = gl_LocalInvocationID.x; - - // There are not enough cols to use all threads - if (tid >= p.ncols) { - return; - } - - const uint block_size = min(p.ncols, BLOCK_SIZE); - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - tmp[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint i = 0; i < p.ncols/block_size; i += 2) { - const uint col = i*block_size + 2*tid; - const uint ib = (row*p.ncols + col)/QUANT_K; // block index - const uint iqs = (col%QUANT_K)/QUANT_R; // quant index - const uint iybs = col - col%QUANT_K; // y block start index - - vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); - - // matrix multiplication - tmp[tid] = fma(FLOAT_TYPE(v.x), FLOAT_TYPE(data_b[b_offset + iybs + iqs]), fma(FLOAT_TYPE(v.y), FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]), tmp[tid])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = block_size/2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp deleted file mode 100644 index d91e00e100..0000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp +++ /dev/null @@ -1,118 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const uint il = tid/step; // 0...3 - const uint ir = tid - step*il; // 0...7 or 0...3 - const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = n * (2 * ir + v_in); // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - -#if K_QUANTS_PER_ITERATION == 2 - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx]), q4_0, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), q4_1, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3))); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_4, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), q4_5, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), q4_6, FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7))); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx]), q4_8, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), q4_9, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), q4_10, FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11))); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_12, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), q4_13, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), q4_14, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 3]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 35]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 3]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7))))))))))))))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); -#else - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), q4_0, FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), q4_4, FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7))))))); - - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + - sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, (fma(sx, FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f), fma(sy, FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f), - fma(sz, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)), fma(sw, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))))))), fma(-dmin, smin, tmp[tmp_idx])); -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp deleted file mode 100644 index 2306785af4..0000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp +++ /dev/null @@ -1,109 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - - const uint il = tid/4; // 0...3 - const uint ir = tid - 4*il; // 0...7 or 0...3 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = 4*ir + 2*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - const uint8_t hm1 = uint8_t(1 << (2*v_im)); - const uint8_t hm2 = uint8_t(hm1 << 4); - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); - - const FLOAT_TYPE sx = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))))); - const FLOAT_TYPE sy = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE sz = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))))); - const FLOAT_TYPE sw = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3, - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6, - (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp deleted file mode 100644 index 95c286eeb1..0000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp +++ /dev/null @@ -1,79 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const uint l0 = v_in; // 0...15 - const uint is = 0; -#else - const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const uint is = v_in / 4; -#endif - - const uint ql_offset = 64*v_im + l0; - const uint qh_offset = 32*v_im + l0; - const uint s_offset = 8*v_im + is; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - -#if K_QUANTS_PER_ITERATION == 1 - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx])))))))); -#else - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum)))); - } - tmp[16 * ix + tid] += sum; -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/scale.comp b/ggml/src/vulkan-shaders/scale.comp deleted file mode 100644 index 5cd2f668d0..0000000000 --- a/ggml/src/vulkan-shaders/scale.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(p.param1)); -} diff --git a/ggml/src/vulkan-shaders/soft_max.comp b/ggml/src/vulkan-shaders/soft_max.comp deleted file mode 100644 index 0bd51ecab5..0000000000 --- a/ggml/src/vulkan-shaders/soft_max.comp +++ /dev/null @@ -1,106 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float scale; - float max_bias; - float m0; - float m1; - uint n_head_log2; -} p; - -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE vals[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint rowy = rowx % p.KY; - - float slope = 1.0f; - - // ALiBi - if (p.max_bias > 0.0f) { - const uint h = rowx/p.KY; // head index - - const float base = h < p.n_head_log2 ? p.m0 : p.m1; - const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; - - slope = pow(base, exp); - } - - // Find max - FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); - } - vals[tid] = max_val; - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] = max(vals[tid], vals[tid + s]); - } - barrier(); - } - - max_val = vals[0]; - barrier(); - - // Sum up values - vals[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); - vals[tid] += val; - data_d[i] = D_TYPE(val); - } - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] += vals[tid + s]; - } - barrier(); - } - - const D_TYPE divisor = D_TYPE(vals[0]); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - data_d[rowx*p.KX + col] /= divisor; - } -} diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index d7a877c587..9afddbeadd 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -64,15 +64,27 @@ class Keys: BASE_MODEL_AUTHOR = "general.base_model.{id}.author" BASE_MODEL_VERSION = "general.base_model.{id}.version" BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization" + BASE_MODEL_DESCRIPTION = "general.base_model.{id}.description" BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper BASE_MODEL_DOI = "general.base_model.{id}.doi" BASE_MODEL_UUID = "general.base_model.{id}.uuid" BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...) + # Dataset Source + DATASET_COUNT = "general.dataset.count" + DATASET_NAME = "general.dataset.{id}.name" + DATASET_AUTHOR = "general.dataset.{id}.author" + DATASET_VERSION = "general.dataset.{id}.version" + DATASET_ORGANIZATION = "general.dataset.{id}.organization" + DATASET_DESCRIPTION = "general.dataset.{id}.description" + DATASET_URL = "general.dataset.{id}.url" # Model Website/Paper + DATASET_DOI = "general.dataset.{id}.doi" + DATASET_UUID = "general.dataset.{id}.uuid" + DATASET_REPO_URL = "general.dataset.{id}.repo_url" # Model Source Repository (git/svn/etc...) + # Array based KV stores TAGS = "general.tags" LANGUAGES = "general.languages" - DATASETS = "general.datasets" class LLM: VOCAB_SIZE = "{arch}.vocab_size" @@ -232,6 +244,7 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + OLMO_1124 = auto() OLMOE = auto() OPENELM = auto() ARCTIC = auto() @@ -397,6 +410,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OLMO_1124: "olmo_1124", MODEL_ARCH.OLMOE: "olmoe", MODEL_ARCH.OPENELM: "openelm", MODEL_ARCH.ARCTIC: "arctic", @@ -1093,6 +1107,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.OLMO_1124: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.OLMOE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 0d8d8a0b08..7a55d12965 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -568,6 +568,9 @@ class GGUFWriter: def add_base_model_organization(self, source_id: int, organization: str) -> None: self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization) + def add_base_model_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.BASE_MODEL_DESCRIPTION.format(id=source_id), description) + def add_base_model_url(self, source_id: int, url: str) -> None: self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url) @@ -580,15 +583,42 @@ class GGUFWriter: def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None: self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url) + def add_dataset_count(self, source_count: int) -> None: + self.add_uint32(Keys.General.DATASET_COUNT, source_count) + + def add_dataset_name(self, source_id: int, name: str) -> None: + self.add_string(Keys.General.DATASET_NAME.format(id=source_id), name) + + def add_dataset_author(self, source_id: int, author: str) -> None: + self.add_string(Keys.General.DATASET_AUTHOR.format(id=source_id), author) + + def add_dataset_version(self, source_id: int, version: str) -> None: + self.add_string(Keys.General.DATASET_VERSION.format(id=source_id), version) + + def add_dataset_organization(self, source_id: int, organization: str) -> None: + self.add_string(Keys.General.DATASET_ORGANIZATION.format(id=source_id), organization) + + def add_dataset_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.DATASET_DESCRIPTION.format(id=source_id), description) + + def add_dataset_url(self, source_id: int, url: str) -> None: + self.add_string(Keys.General.DATASET_URL.format(id=source_id), url) + + def add_dataset_doi(self, source_id: int, doi: str) -> None: + self.add_string(Keys.General.DATASET_DOI.format(id=source_id), doi) + + def add_dataset_uuid(self, source_id: int, uuid: str) -> None: + self.add_string(Keys.General.DATASET_UUID.format(id=source_id), uuid) + + def add_dataset_repo_url(self, source_id: int, repo_url: str) -> None: + self.add_string(Keys.General.DATASET_REPO_URL.format(id=source_id), repo_url) + def add_tags(self, tags: Sequence[str]) -> None: self.add_array(Keys.General.TAGS, tags) def add_languages(self, languages: Sequence[str]) -> None: self.add_array(Keys.General.LANGUAGES, languages) - def add_datasets(self, datasets: Sequence[str]) -> None: - self.add_array(Keys.General.DATASETS, datasets) - def add_tensor_data_layout(self, layout: str) -> None: self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py index db318542a2..962c27b204 100644 --- a/gguf-py/gguf/metadata.py +++ b/gguf-py/gguf/metadata.py @@ -41,7 +41,7 @@ class Metadata: base_models: Optional[list[dict]] = None tags: Optional[list[str]] = None languages: Optional[list[str]] = None - datasets: Optional[list[str]] = None + datasets: Optional[list[dict]] = None @staticmethod def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata: @@ -91,9 +91,11 @@ class Metadata: # Base Models is received here as an array of models metadata.base_models = metadata_override.get("general.base_models", metadata.base_models) + # Datasets is received here as an array of datasets + metadata.datasets = metadata_override.get("general.datasets", metadata.datasets) + metadata.tags = metadata_override.get(Keys.General.TAGS, metadata.tags) metadata.languages = metadata_override.get(Keys.General.LANGUAGES, metadata.languages) - metadata.datasets = metadata_override.get(Keys.General.DATASETS, metadata.datasets) # Direct Metadata Override (via direct cli argument) if model_name is not None: @@ -346,12 +348,12 @@ class Metadata: use_model_card_metadata("author", "model_creator") use_model_card_metadata("basename", "model_type") - if "base_model" in model_card: + if "base_model" in model_card or "base_models" in model_card or "base_model_sources" in model_card: # This represents the parent models that this is based on # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges) # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md metadata_base_models = [] - base_model_value = model_card.get("base_model", None) + base_model_value = model_card.get("base_model", model_card.get("base_models", model_card.get("base_model_sources", None))) if base_model_value is not None: if isinstance(base_model_value, str): @@ -364,18 +366,106 @@ class Metadata: for model_id in metadata_base_models: # NOTE: model size of base model is assumed to be similar to the size of the current model - model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) base_model = {} - if model_full_name_component is not None: - base_model["name"] = Metadata.id_to_title(model_full_name_component) - if org_component is not None: - base_model["organization"] = Metadata.id_to_title(org_component) - if version is not None: - base_model["version"] = version - if org_component is not None and model_full_name_component is not None: - base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + if isinstance(model_id, str): + if model_id.startswith("http://") or model_id.startswith("https://") or model_id.startswith("ssh://"): + base_model["repo_url"] = model_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in model_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", model_id) + if match: + model_id_component = match.group(1) + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id_component, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + + else: + # Likely a Hugging Face ID + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + if org_component is not None and model_full_name_component is not None: + base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + + elif isinstance(model_id, dict): + base_model = model_id + + else: + logger.error(f"base model entry '{str(model_id)}' not in a known format") + metadata.base_models.append(base_model) + if "datasets" in model_card or "dataset" in model_card or "dataset_sources" in model_card: + # This represents the datasets that this was trained from + metadata_datasets = [] + dataset_value = model_card.get("datasets", model_card.get("dataset", model_card.get("dataset_sources", None))) + + if dataset_value is not None: + if isinstance(dataset_value, str): + metadata_datasets.append(dataset_value) + elif isinstance(dataset_value, list): + metadata_datasets.extend(dataset_value) + + if metadata.datasets is None: + metadata.datasets = [] + + for dataset_id in metadata_datasets: + # NOTE: model size of base model is assumed to be similar to the size of the current model + dataset = {} + if isinstance(dataset_id, str): + if dataset_id.startswith(("http://", "https://", "ssh://")): + dataset["repo_url"] = dataset_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in dataset_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", dataset_id) + if match: + dataset_id_component = match.group(1) + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id_component, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + + else: + # Likely a Hugging Face ID + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + if org_component is not None and dataset_name_component is not None: + dataset["repo_url"] = f"https://huggingface.co/{org_component}/{dataset_name_component}" + + elif isinstance(dataset_id, dict): + dataset = dataset_id + + else: + logger.error(f"dataset entry '{str(dataset_id)}' not in a known format") + + metadata.datasets.append(dataset) + use_model_card_metadata("license", "license") use_model_card_metadata("license_name", "license_name") use_model_card_metadata("license_link", "license_link") @@ -386,9 +476,6 @@ class Metadata: use_array_model_card_metadata("languages", "languages") use_array_model_card_metadata("languages", "language") - use_array_model_card_metadata("datasets", "datasets") - use_array_model_card_metadata("datasets", "dataset") - # Hugging Face Parameter Heuristics #################################### @@ -458,7 +545,10 @@ class Metadata: gguf_writer.add_size_label(self.size_label) if self.license is not None: - gguf_writer.add_license(self.license) + if isinstance(self.license, list): + gguf_writer.add_license(",".join(self.license)) + else: + gguf_writer.add_license(self.license) if self.license_name is not None: gguf_writer.add_license_name(self.license_name) if self.license_link is not None: @@ -493,6 +583,8 @@ class Metadata: gguf_writer.add_base_model_version(key, base_model_entry["version"]) if "organization" in base_model_entry: gguf_writer.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + gguf_writer.add_base_model_description(key, base_model_entry["description"]) if "url" in base_model_entry: gguf_writer.add_base_model_url(key, base_model_entry["url"]) if "doi" in base_model_entry: @@ -502,9 +594,29 @@ class Metadata: if "repo_url" in base_model_entry: gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"]) + if self.datasets is not None: + gguf_writer.add_dataset_count(len(self.datasets)) + for key, dataset_entry in enumerate(self.datasets): + if "name" in dataset_entry: + gguf_writer.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + gguf_writer.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + gguf_writer.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + gguf_writer.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + gguf_writer.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + gguf_writer.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + gguf_writer.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + gguf_writer.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + gguf_writer.add_dataset_repo_url(key, dataset_entry["repo_url"]) + if self.tags is not None: gguf_writer.add_tags(self.tags) if self.languages is not None: gguf_writer.add_languages(self.languages) - if self.datasets is not None: - gguf_writer.add_datasets(self.datasets) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 647f477b13..09a1a65df7 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -13,7 +13,7 @@ class TensorNameMap: "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone "transformer.word_embeddings", # falcon "word_embeddings", # bloom - "model.embed_tokens", # llama-hf nemotron olmoe + "model.embed_tokens", # llama-hf nemotron olmoe olmo_1124 "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert nomic-bert "language_model.embedding.word_embeddings", # persimmon @@ -54,7 +54,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo_1124 "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -66,7 +66,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon jais exaone - "model.norm", # llama-hf baichuan internlm2 olmoe + "model.norm", # llama-hf baichuan internlm2 olmoe olmo_1124 "norm", # llama-pth "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 @@ -145,7 +145,7 @@ class TensorNameMap: # Attention query MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe olmo_1124 "layers.{bid}.attention.wq", # llama-pth "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j @@ -157,7 +157,7 @@ class TensorNameMap: # Attention key MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe olmo_1124 "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j @@ -170,7 +170,7 @@ class TensorNameMap: # Attention value MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe olmo_1124 "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j @@ -188,7 +188,7 @@ class TensorNameMap: "transformer.blocks.{bid}.attn.out_proj", # mpt "transformer.h.{bid}.self_attention.dense", # falcon "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo_1124 "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j @@ -215,7 +215,7 @@ class TensorNameMap: ), MODEL_TENSOR.ATTN_POST_NORM: ( - "model.layers.{bid}.post_attention_layernorm", # gemma2 + "model.layers.{bid}.post_attention_layernorm", # gemma2 olmo_1124 ), # Rotary embeddings @@ -252,7 +252,7 @@ class TensorNameMap: # Post feed-forward norm MODEL_TENSOR.FFN_POST_NORM: ( - "model.layers.{bid}.post_feedforward_layernorm", # gemma2 + "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo_1124 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -276,7 +276,7 @@ class TensorNameMap: "transformer.blocks.{bid}.ffn.up_proj", # mpt "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon "h.{bid}.mlp.dense_h_to_4h", # bloom - "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron + "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron olmo_1124 "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j @@ -318,7 +318,7 @@ class TensorNameMap: # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( - "model.layers.{bid}.mlp.gate_proj", # llama-hf refact + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact olmo_1124 "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "transformer.h.{bid}.mlp.c_fc2", # jais @@ -351,7 +351,7 @@ class TensorNameMap: "transformer.blocks.{bid}.ffn.down_proj", # mpt "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon "h.{bid}.mlp.dense_4h_to_h", # bloom - "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron + "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron olmo_1124 "layers.{bid}.feed_forward.w2", # llama-pth "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j @@ -389,7 +389,7 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", "model.layers.{bid}.self_attn.q_layernorm", # persimmon - "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon + "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon olmo_1124 "transformer.blocks.{bid}.attn.q_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 "transformer.layers.{bid}.attn.q_norm", # openelm @@ -398,7 +398,7 @@ class TensorNameMap: MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", "model.layers.{bid}.self_attn.k_layernorm", # persimmon - "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon + "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon olmo_1124 "transformer.blocks.{bid}.attn.k_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 "transformer.layers.{bid}.attn.k_norm", # openelm diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py index 81a2a30ae6..40d484f4ea 100755 --- a/gguf-py/tests/test_metadata.py +++ b/gguf-py/tests/test_metadata.py @@ -182,8 +182,43 @@ class TestMetadataMethod(unittest.TestCase): expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}] expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'] expect.languages=['en'] - expect.datasets=['teknium/OpenHermes-2.5'] + expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}] + self.assertEqual(got, expect) + # Base Model spec is inferred from model id + model_card = {'base_models': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is only url + model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is given directly + model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is inferred from model id + model_card = {'datasets': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is only url + model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is given directly + model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) self.assertEqual(got, expect) def test_apply_metadata_heuristic_from_hf_parameters(self): diff --git a/grammars/README.md b/grammars/README.md index 4e8b4e2fcf..4e57bca5f3 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -124,7 +124,7 @@ You can use GBNF grammars: - In [llama-cli](../examples/main), passed as the `--json` / `-j` flag - To convert to a grammar ahead of time: - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) - - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) + - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) Take a look at [tests](../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555). diff --git a/include/llama.h b/include/llama.h index 510e862caa..fdb645be3d 100644 --- a/include/llama.h +++ b/include/llama.h @@ -2,6 +2,7 @@ #define LLAMA_H #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-backend.h" #include @@ -205,7 +206,7 @@ extern "C" { enum llama_split_mode { LLAMA_SPLIT_MODE_NONE = 0, // single GPU LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs - LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs + LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported }; // TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979) @@ -217,6 +218,7 @@ extern "C" { typedef struct llama_token_data_array { // TODO: consider SoA + // NOTE: this pointer can be modified by the samplers llama_token_data * data; size_t size; int64_t selected; // this is the index in the data array (i.e. not the token id) @@ -232,8 +234,11 @@ extern "C" { // - token : the token ids of the input (used when embd is NULL) // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence + // (if set to NULL, the token position will be tracked automatically by llama_decode) // - seq_id : the sequence to which the respective token belongs + // (if set to NULL, the sequence ID will be assumed to be 0) // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output + // (if set to NULL, only the logits for last token will be returned) // typedef struct llama_batch { int32_t n_tokens; @@ -244,15 +249,6 @@ extern "C" { int32_t * n_seq_id; llama_seq_id ** seq_id; int8_t * logits; // TODO: rename this to "output" - - // NOTE: helpers for smooth API transition - can be deprecated in the future - // for future-proof code, use the above fields instead and ignore everything below - // - // pos[i] = all_pos_0 + i*all_pos_1 - // - llama_pos all_pos_0; // used if pos == NULL - llama_pos all_pos_1; // used if pos == NULL - llama_seq_id all_seq_id; // used if seq_id == NULL } llama_batch; enum llama_model_kv_override_type { @@ -279,10 +275,7 @@ extern "C" { int32_t n_gpu_layers; // number of layers to store in VRAM enum llama_split_mode split_mode; // how to split the model across multiple GPUs - // main_gpu interpretation depends on split_mode: - // LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model - // LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results - // LLAMA_SPLIT_MODE_LAYER: ignored + // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE int32_t main_gpu; // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() @@ -683,6 +676,9 @@ extern "C" { // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) LLAMA_API void llama_kv_cache_update(struct llama_context * ctx); + // Check if the context supports KV cache shifting + LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx); + // // State / sessions // @@ -785,15 +781,15 @@ extern "C" { // Decoding // - // Return batch for single sequence of tokens starting at pos_0 + // Return batch for single sequence of tokens + // The sequence ID will be fixed to 0 + // The position of the tokens will be tracked automatically by llama_decode // // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it // LLAMA_API struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id); + int32_t n_tokens); // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens // Each token can be assigned up to n_seq_max sequence ids @@ -813,7 +809,7 @@ extern "C" { // Processes a batch of tokens with the ecoder part of the encoder-decoder model. // Stores the encoder output internally for later use by the decoder cross-attention layers. // 0 - success - // < 0 - error + // < 0 - error. the KV cache state is restored to the state before this call LLAMA_API int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch); @@ -821,7 +817,7 @@ extern "C" { // Positive return values does not mean a fatal error, but rather a warning. // 0 - success // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) - // < 0 - error + // < 0 - error. the KV cache state is restored to the state before this call LLAMA_API int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch); @@ -1084,12 +1080,13 @@ extern "C" { // available samplers: - LLAMA_API struct llama_sampler * llama_sampler_init_greedy (void); - LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); + LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void); + LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first. - LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void); + DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void), + "will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)"); /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); @@ -1100,16 +1097,18 @@ extern "C" { /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841 LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep); - /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/. - LLAMA_API struct llama_sampler * llama_sampler_init_tail_free (float z, size_t min_keep); - /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep); + + /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t); /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext (float t, float delta, float exponent); + /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335 + LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed); + /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. @@ -1149,11 +1148,43 @@ extern "C" { bool penalize_nl, // consider newlines as a repeatable token bool ignore_eos); // ignore the end-of-sequence token + /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 + LLAMA_API struct llama_sampler * llama_sampler_init_dry( + const struct llama_model * model, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const char ** seq_breakers, + size_t num_breakers); + LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( int32_t n_vocab, int32_t n_logit_bias, const llama_logit_bias * logit_bias); + // this sampler is meant to be used for fill-in-the-middle infilling + // it's supposed to be used after top_k + top_p sampling + // + // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG + // 2. combine probs of tokens that have the same prefix + // + // example: + // + // - before: + // "hel": 0.5 + // "hell": 0.2 + // "hello": 0.1 + // "dummy": 0.1 + // + // - after: + // "hel": 0.8 + // "dummy": 0.1 + // + // 3. discard non-EOG tokens with low prob + // 4. if no tokens are left -> pick EOT + // + LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model); // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl); @@ -1225,8 +1256,6 @@ extern "C" { LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); - LLAMA_API void llama_perf_dump_yaml(FILE * stream, const struct llama_context * ctx); - #ifdef __cplusplus } #endif diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp index 131d7c177c..3df6e1f421 100644 --- a/pocs/vdot/q8dot.cpp +++ b/pocs/vdot/q8dot.cpp @@ -11,6 +11,7 @@ #include #include +#include constexpr int kVecSize = 1 << 16; @@ -136,7 +137,7 @@ int main(int argc, char** argv) { auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1; - const auto * funcs = ggml_get_type_traits(ggml_type); + const auto * funcs = ggml_get_type_traits_cpu(ggml_type); Stat simple, ggml; diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 88e66ea136..2dca62848b 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -9,6 +9,7 @@ #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -236,7 +237,7 @@ int main(int argc, char** argv) { int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64); int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64); - const auto * funcs = useQ4_1 ? ggml_get_type_traits(GGML_TYPE_Q4_1) : ggml_get_type_traits(GGML_TYPE_Q4_0); + const auto * funcs_cpu = ggml_get_type_traits_cpu(useQ4_1 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q4_0); std::vector q40; std::vector q41; @@ -261,9 +262,9 @@ int main(int argc, char** argv) { // Note, we do not include this in the timing as in practical application // we already have the quantized model weights. if (useQ4_1) { - funcs->from_float(x1.data(), q41.data(), kVecSize); + funcs_cpu->from_float(x1.data(), q41.data(), kVecSize); } else { - funcs->from_float(x1.data(), q40.data(), kVecSize); + funcs_cpu->from_float(x1.data(), q40.data(), kVecSize); } // Now measure time the dot product needs using the "scalar" version above @@ -282,10 +283,10 @@ int main(int argc, char** argv) { dot_q4_q8(kVecSize, &result, q40.data(), q8.data()); } else { - const auto * vdot = ggml_get_type_traits(funcs->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(funcs_cpu->vec_dot_type); vdot->from_float(y1.data(), q8.data(), kVecSize); - if (useQ4_1) funcs->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1); - else funcs->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1); + if (useQ4_1) funcs_cpu->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1); + else funcs_cpu->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1); } sumq += result; t2 = std::chrono::high_resolution_clock::now(); diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index e45e83ce8e..5069ae6382 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -19,22 +19,22 @@ logger = logging.getLogger("compare-llama-bench") # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", - "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads", - "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "backends", "n_gpu_layers", "model_filename", "model_type", "n_batch", "n_ubatch", + "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", "use_mmap", "no_kv_offload", + "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: -BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"] +BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "flash_attn"] # Header names for the table: PRETTY_NAMES = { - "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC", - "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", - "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", - "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode", - "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", - "use_mmap": "Use mmap", "embeddings": "Embeddings", + "cpu_info": "CPU", "gpu_info": "GPU", "backends": "Backends", "n_gpu_layers": "GPU layers", + "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", + "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", + "embeddings": "Embeddings", "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", + "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "split_mode": "Split mode", "main_gpu": "Main GPU", + "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", "use_mmap": "Use mmap", } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. @@ -303,14 +303,11 @@ else: show = [] # Show CPU and/or GPU by default even if the hardware for all results is the same: - if "gpu_blas" not in properties_different and "n_gpu_layers" not in properties_different: - gpu_blas = bool(rows_full[0][KEY_PROPERTIES.index("gpu_blas")]) + if "n_gpu_layers" not in properties_different: ngl = int(rows_full[0][KEY_PROPERTIES.index("n_gpu_layers")]) - if not gpu_blas or ngl != 99 and "cpu_info" not in properties_different: + if ngl != 99 and "cpu_info" not in properties_different: show.append("cpu_info") - if gpu_blas and "gpu_info" not in properties_different: - show.append("gpu_info") show += properties_different diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py deleted file mode 100755 index ee21eab371..0000000000 --- a/scripts/run-with-preset.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import argparse -import os -import subprocess -import sys - -import yaml - -logger = logging.getLogger("run-with-preset") - -CLI_ARGS_LLAMA_CLI_PERPLEXITY = [ - "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape", - "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag", - "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", - "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base", - "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", - "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q", - "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", - "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n", - "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed", - "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical", - "verbose-prompt" -] - -CLI_ARGS_LLAMA_BENCH = [ - "batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers", - "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose" -] - -CLI_ARGS_LLAMA_SERVER = [ - "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base", - "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q", - "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split", - "threads", "verbose" -] - -description = """Run llama.cpp binaries with presets from YAML file(s). -To specify which binary should be run, specify the "binary" property (llama-cli, llama-perplexity, llama-bench, and llama-server are supported). -To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument. - -Formatting considerations: -- The YAML property names are the same as the CLI argument names of the corresponding binary. -- Properties must use the long name of their corresponding llama.cpp CLI arguments. -- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores. -- Flags must be defined as ": true" to be effective. -- To define the logit_bias property, the expected format is ": " in the "logit_bias" namespace. -- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings. -- To define a tensor split, pass a list of floats. -""" -usage = "run-with-preset.py [-h] [yaml_files ...] [-- ...]" -epilog = (" -- specify additional CLI ars to be passed to the binary (override all preset files). " - "Unknown args will be ignored.") - -parser = argparse.ArgumentParser( - description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter) -parser.add_argument("-bin", "--binary", help="The binary to run.") -parser.add_argument("yaml_files", nargs="*", - help="Arbitrary number of YAML files from which to read preset values. " - "If two files specify the same values the later one will be used.") -parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - -known_args, unknown_args = parser.parse_known_args() - -if not known_args.yaml_files and not unknown_args: - parser.print_help() - sys.exit(0) - -logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO) - -props = dict() - -for yaml_file in known_args.yaml_files: - with open(yaml_file, "r") as f: - props.update(yaml.load(f, yaml.SafeLoader)) - -props = {prop.replace("_", "-"): val for prop, val in props.items()} - -binary = props.pop("binary", "llama-cli") -if known_args.binary: - binary = known_args.binary - -if os.path.exists(f"./{binary}"): - binary = f"./{binary}" - -if binary.lower().endswith("llama-cli") or binary.lower().endswith("llama-perplexity"): - cli_args = CLI_ARGS_LLAMA_CLI_PERPLEXITY -elif binary.lower().endswith("llama-bench"): - cli_args = CLI_ARGS_LLAMA_BENCH -elif binary.lower().endswith("llama-server"): - cli_args = CLI_ARGS_LLAMA_SERVER -else: - logger.error(f"Unknown binary: {binary}") - sys.exit(1) - -command_list = [binary] - -for cli_arg in cli_args: - value = props.pop(cli_arg, None) - - if not value or value == -1: - continue - - if cli_arg == "logit-bias": - for token, bias in value.items(): - command_list.append("--logit-bias") - command_list.append(f"{token}{bias:+}") - continue - - if cli_arg == "reverse-prompt" and not isinstance(value, str): - for rp in value: - command_list.append("--reverse-prompt") - command_list.append(str(rp)) - continue - - command_list.append(f"--{cli_arg}") - - if cli_arg == "tensor-split": - command_list.append(",".join([str(v) for v in value])) - continue - - value = str(value) - - if value != "True": - command_list.append(str(value)) - -num_unused = len(props) -if num_unused > 10: - logger.info(f"The preset file contained a total of {num_unused} unused properties.") -elif num_unused > 0: - logger.info("The preset file contained the following unused properties:") - for prop, value in props.items(): - logger.info(f" {prop}: {value}") - -command_list += unknown_args - -sp = subprocess.Popen(command_list) - -while sp.returncode is None: - try: - sp.wait() - except KeyboardInterrupt: - pass - -sys.exit(sp.returncode) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index ffce2aab09..d0815cf89e 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -73,16 +73,20 @@ while read c; do src/ggml*.h \ src/ggml*.c \ src/ggml*.cpp \ - src/ggml*.m \ - src/ggml*.metal \ - src/ggml*.cu \ + src/ggml-amx/* \ + src/ggml-blas/* \ src/ggml-cann/* \ + src/ggml-cpu/* \ src/ggml-cuda/* \ + src/ggml-hip/* \ + src/ggml-kompute/* \ + src/ggml-metal/* \ + src/ggml-musa/* \ + src/ggml-rpc/* \ src/ggml-sycl/* \ - src/vulkan-shaders/* \ + src/ggml-vulkan/* \ include/ggml*.h \ tests/test-opt.cpp \ - tests/test-grad0.cpp \ tests/test-quantize-fns.cpp \ tests/test-quantize-perf.cpp \ tests/test-backend-ops.cpp \ @@ -113,49 +117,29 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # replace filenames: # - # CMakelists.txt -> ggml/CMakeLists.txt - # src/CMakeLists.txt -> ggml/src/CMakeLists.txt - # cmake/FindSIMD.cmake -> ggml/cmake/FindSIMD.cmake + # CMakelists.txt -> ggml/CMakeLists.txt + # src/CMakeLists.txt -> ggml/src/CMakeLists.txt + # cmake/FindSIMD.cmake -> ggml/cmake/FindSIMD.cmake # - # src/ggml.c -> ggml/src/ggml.c - # src/ggml-aarch64.c -> ggml/src/ggml-aarch64.c - # src/ggml-aarch64.h -> ggml/src/ggml-aarch64.h - # src/ggml-alloc.c -> ggml/src/ggml-alloc.c - # src/ggml-backend-impl.h -> ggml/src/ggml-backend-impl.h - # src/ggml-backend.cpp -> ggml/src/ggml-backend.cpp - # src/ggml-cann/* -> ggml/src/ggml-cann/ - # src/ggml-cann.cpp -> ggml/src/ggml-cann.cpp - # src/ggml-common.h -> ggml/src/ggml-common.h - # src/ggml-cuda/* -> ggml/src/ggml-cuda/ - # src/ggml-cuda.cu -> ggml/src/ggml-cuda.cu - # src/ggml-impl.h -> ggml/src/ggml-impl.h - # src/ggml-kompute.cpp -> ggml/src/ggml-kompute.cpp - # src/ggml-metal.m -> ggml/src/ggml-metal.m - # src/ggml-quants.c -> ggml/src/ggml-quants.c - # src/ggml-quants.h -> ggml/src/ggml-quants.h - # src/ggml-rpc.cpp -> ggml/src/ggml-rpc.cpp - # src/ggml-sycl/* -> ggml/src/ggml-sycl/ - # src/ggml-sycl.cpp -> ggml/src/ggml-sycl.cpp - # src/ggml-vulkan.cpp -> ggml/src/ggml-vulkan.cpp - # src/vulkan-shaders/* -> ggml/src/vulkan-shaders/ + # src/ggml*.c -> ggml/src/ggml*.c + # src/ggml*.cpp -> ggml/src/ggml*.cpp + # src/ggml*.h -> ggml/src/ggml*.h + # src/ggml-amx/* -> ggml/src/ggml-amx/* + # src/ggml-blas/* -> ggml/src/ggml-blas/* + # src/ggml-cann/* -> ggml/src/ggml-cann/* + # src/ggml-cpu/* -> ggml/src/ggml-cpu/* + # src/ggml-cuda/* -> ggml/src/ggml-cuda/* + # src/ggml-hip/* -> ggml/src/ggml-hip/* + # src/ggml-kompute/* -> ggml/src/ggml-kompute/* + # src/ggml-metal/* -> ggml/src/ggml-metal/* + # src/ggml-musa/* -> ggml/src/ggml-musa/* + # src/ggml-rpc/* -> ggml/src/ggml-rpc/* + # src/ggml-sycl/* -> ggml/src/ggml-sycl/* + # src/ggml-vulkan/* -> ggml/src/ggml-vulkan/* # - # include/ggml.h -> ggml/include/ggml.h - # include/ggml-alloc.h -> ggml/include/ggml-alloc.h - # include/ggml-backend.h -> ggml/include/ggml-backend.h - # include/ggml-blas.h -> ggml/include/ggml-blas.h - # include/ggml-cann.h -> ggml/include/ggml-cann.h - # include/ggml-cuda.h -> ggml/include/ggml-cuda.h - # include/ggml-kompute.h -> ggml/include/ggml-kompute.h - # include/ggml-metal.h -> ggml/include/ggml-metal.h - # include/ggml-rpc.h -> ggml/include/ggml-rpc.h - # include/ggml-sycl.h -> ggml/include/ggml-sycl.h - # include/ggml-vulkan.h -> ggml/include/ggml-vulkan.h + # include/ggml*.h -> ggml/include/ggml*.h # - # tests/test-opt.cpp -> tests/test-opt.cpp - # tests/test-grad0.cpp -> tests/test-grad0.cpp - # tests/test-quantize-fns.cpp -> tests/test-quantize-fns.cpp - # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp - # tests/test-backend-ops.cpp -> tests/test-backend-ops.cpp + # tests/test*.cpp -> tests/ # # LICENSE -> LICENSE # scripts/gen-authors.sh -> scripts/gen-authors.sh @@ -164,42 +148,23 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/([[:space:]]|[ab]\/)CMakeLists.txt/\1ggml\/CMakeLists.txt/g' \ -e 's/([[:space:]]|[ab]\/)src\/CMakeLists.txt/\1ggml\/src\/CMakeLists.txt/g' \ -e 's/([[:space:]]|[ab]\/)cmake\/FindSIMD.cmake/\1ggml\/cmake\/FindSIMD.cmake/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml\.c/\1ggml\/src\/ggml.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.c/\1ggml\/src\/ggml-aarch64.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.h/\1ggml\/src\/ggml-aarch64.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-alloc\.c/\1ggml\/src\/ggml-alloc.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-backend-impl\.h/\1ggml\/src\/ggml-backend-impl.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-backend\.cpp/\1ggml\/src\/ggml-backend.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.c/\1ggml\/src\/ggml\2.c/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.cpp/\1ggml\/src\/ggml\2.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.h/\1ggml\/src\/ggml\2.h/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-amx\//\1ggml\/src\/ggml-amx\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-blas\//\1ggml\/src\/ggml-blas\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\.cpp/\1ggml\/src\/ggml-cann.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-common\.h/\1ggml\/src\/ggml-common.h/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-cpu\//\1ggml\/src\/ggml-cpu\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\//\1ggml\/src\/ggml-cuda\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\.cu/\1ggml\/src\/ggml-cuda.cu/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-impl\.h/\1ggml\/src\/ggml-impl.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\.cpp/\1ggml\/src\/ggml-kompute.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\.m/\1ggml\/src\/ggml-metal.m/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.c/\1ggml\/src\/ggml-quants.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.h/\1ggml\/src\/ggml-quants.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\.cpp/\1ggml\/src\/ggml-rpc.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-hip\//\1ggml\/src\/ggml-hip\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\//\1ggml\/src\/ggml-kompute\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\//\1ggml\/src\/ggml-metal\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-musa\//\1ggml\/src\/ggml-musa\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\//\1ggml\/src\/ggml-rpc\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\.cpp/\1ggml\/src\/ggml-sycl.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\.cpp/\1ggml\/src\/ggml-vulkan.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/vulkan-shaders\//\1ggml\/src\/vulkan-shaders\//g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-alloc\.h/\1ggml\/include\/ggml-alloc.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-backend\.h/\1ggml\/include\/ggml-backend.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-blas\.h/\1ggml\/include\/ggml-blas.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-cann\.h/\1ggml\/include\/ggml-cann.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-cuda\.h/\1ggml\/include\/ggml-cuda.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-kompute\.h/\1ggml\/include\/ggml-kompute.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-metal\.h/\1ggml\/include\/ggml-metal.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-rpc\.h/\1ggml\/include\/ggml-rpc.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-sycl\.h/\1ggml\/include\/ggml-sycl.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-vulkan\.h/\1ggml\/include\/ggml-vulkan.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common\.h/\1examples\/common.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common\.cpp/\1examples\/common.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.h/\1examples\/common-ggml.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.cpp/\1examples\/common-ggml.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\//\1ggml\/src\/ggml-vulkan\//g' \ + -e 's/([[:space:]]|[ab]\/)include\/ggml(.*)\.h/\1ggml\/include\/ggml\2.h/g' \ + -e 's/([[:space:]]|[ab]\/)tests\/(.*)\.cpp/\1tests\/\2.cpp/g' \ -e 's/([[:space:]]|[ab]\/)LICENSE/\1LICENSE/g' \ -e 's/([[:space:]]|[ab]\/)scripts\/gen-authors\.sh/\1scripts\/gen-authors.sh/g' \ > ggml-src.patch.tmp diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 3cca9cc2fd..d101d2b57f 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -564f42082f858f9674b2a2e06e9e779d9ed2c754 +6fcbd60bc72ac3f7ad43f78c87e535f2e6206f58 diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index f6ff5e6835..000270afbf 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -4,43 +4,25 @@ cp -rpv ../ggml/CMakeLists.txt ./ggml/CMakeLists.txt cp -rpv ../ggml/src/CMakeLists.txt ./ggml/src/CMakeLists.txt cp -rpv ../ggml/cmake/FindSIMD.cmake ./ggml/cmake/FindSIMD.cmake -cp -rpv ../ggml/src/ggml.c ./ggml/src/ggml.c -cp -rpv ../ggml/src/ggml-aarch64.c ./ggml/src/ggml-aarch64.c -cp -rpv ../ggml/src/ggml-aarch64.h ./ggml/src/ggml-aarch64.h -cp -rpv ../ggml/src/ggml-alloc.c ./ggml/src/ggml-alloc.c -cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml/src/ggml-backend-impl.h -cp -rpv ../ggml/src/ggml-backend.cpp ./ggml/src/ggml-backend.cpp -cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/ -cp -rpv ../ggml/src/ggml-cann.cpp ./ggml/src/ggml-cann.cpp -cp -rpv ../ggml/src/ggml-common.h ./ggml/src/ggml-common.h -cp -rpv ../ggml/src/ggml-cuda/* ./ggml/src/ggml-cuda/ -cp -rpv ../ggml/src/ggml-cuda.cu ./ggml/src/ggml-cuda.cu -cp -rpv ../ggml/src/ggml-impl.h ./ggml/src/ggml-impl.h -cp -rpv ../ggml/src/ggml-kompute.cpp ./ggml/src/ggml-kompute.cpp -cp -rpv ../ggml/src/ggml-metal.m ./ggml/src/ggml-metal.m -cp -rpv ../ggml/src/ggml-metal.metal ./ggml/src/ggml-metal.metal -cp -rpv ../ggml/src/ggml-quants.c ./ggml/src/ggml-quants.c -cp -rpv ../ggml/src/ggml-quants.h ./ggml/src/ggml-quants.h -cp -rpv ../ggml/src/ggml-rpc.cpp ./ggml/src/ggml-rpc.cpp -cp -rpv ../ggml/src/ggml-sycl/* ./ggml/src/ggml-sycl/ -cp -rpv ../ggml/src/ggml-sycl.cpp ./ggml/src/ggml-sycl.cpp -cp -rpv ../ggml/src/ggml-vulkan.cpp ./ggml/src/ggml-vulkan.cpp -cp -rpv ../ggml/src/vulkan-shaders/* ./ggml/src/vulkan-shaders/ +cp -rpv ../ggml/src/ggml*.c ./ggml/src/ +cp -rpv ../ggml/src/ggml*.cpp ./ggml/src/ +cp -rpv ../ggml/src/ggml*.h ./ggml/src/ +cp -rpv ../ggml/src/ggml-amx/* ./ggml/src/ggml-amx/ +cp -rpv ../ggml/src/ggml-blas/* ./ggml/src/ggml-blas/ +cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/ +cp -rpv ../ggml/src/ggml-cpu/* ./ggml/src/ggml-cpu/ +cp -rpv ../ggml/src/ggml-cuda/* ./ggml/src/ggml-cuda/ +cp -rpv ../ggml/src/ggml-hip/* ./ggml/src/ggml-hip/ +cp -rpv ../ggml/src/ggml-kompute/* ./ggml/src/ggml-kompute/ +cp -rpv ../ggml/src/ggml-metal/* ./ggml/src/ggml-metal/ +cp -rpv ../ggml/src/ggml-musa/* ./ggml/src/ggml-musa/ +cp -rpv ../ggml/src/ggml-rpc/* ./ggml/src/ggml-rpc/ +cp -rpv ../ggml/src/ggml-sycl/* ./ggml/src/ggml-sycl/ +cp -rpv ../ggml/src/ggml-vulkan/* ./ggml/src/ggml-vulkan/ -cp -rpv ../ggml/include/ggml.h ./ggml/include/ggml.h -cp -rpv ../ggml/include/ggml-alloc.h ./ggml/include/ggml-alloc.h -cp -rpv ../ggml/include/ggml-backend.h ./ggml/include/ggml-backend.h -cp -rpv ../ggml/include/ggml-blas.h ./ggml/include/ggml-blas.h -cp -rpv ../ggml/include/ggml-cann.h ./ggml/include/ggml-cann.h -cp -rpv ../ggml/include/ggml-cuda.h ./ggml/include/ggml-cuda.h -cp -rpv ../ggml/include/ggml-kompute.h ./ggml/include/ggml-kompute.h -cp -rpv ../ggml/include/ggml-metal.h ./ggml/include/ggml-metal.h -cp -rpv ../ggml/include/ggml-rpc.h ./ggml/include/ggml-rpc.h -cp -rpv ../ggml/include/ggml-sycl.h ./ggml/include/ggml-sycl.h -cp -rpv ../ggml/include/ggml-vulkan.h ./ggml/include/ggml-vulkan.h +cp -rpv ../ggml/include/ggml*.h ./ggml/include/ cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp -cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp cp -rpv ../ggml/tests/test-quantize-fns.cpp ./tests/test-quantize-fns.cpp cp -rpv ../ggml/tests/test-quantize-perf.cpp ./tests/test-quantize-perf.cpp cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp diff --git a/spm-headers/ggml-cpp.h b/spm-headers/ggml-cpp.h new file mode 120000 index 0000000000..8a8604cc21 --- /dev/null +++ b/spm-headers/ggml-cpp.h @@ -0,0 +1 @@ +../ggml/include/ggml-cpp.h \ No newline at end of file diff --git a/spm-headers/ggml-cpu.h b/spm-headers/ggml-cpu.h new file mode 120000 index 0000000000..66e6296076 --- /dev/null +++ b/spm-headers/ggml-cpu.h @@ -0,0 +1 @@ +../ggml/include/ggml-cpu.h \ No newline at end of file diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 46a6ad5620..a866247503 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -29,5 +29,6 @@ target_link_libraries(llama PUBLIC ggml) if (BUILD_SHARED_LIBS) set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD) + target_compile_definitions(llama PRIVATE LLAMA_BUILD) + target_compile_definitions(llama PUBLIC LLAMA_SHARED) endif() diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index e255a8fc4f..fd8ca8a9ed 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -63,6 +63,30 @@ static void llama_log_softmax(float * array, size_t size) { } */ +static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp) { + if (temp <= 0.0f) { + // find the token with the highest logit and set the rest to -inf + size_t max_i = 0; + float max_l = cur_p->data[0].logit; + + for (size_t i = 1; i < cur_p->size; ++i) { + if (cur_p->data[i ].logit > max_l) { + cur_p->data[max_i].logit = -INFINITY; + max_i = i; + max_l = cur_p->data[i].logit; + } else { + cur_p->data[i].logit = -INFINITY; + } + } + + return; + } + + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].logit /= temp; + } +} + static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) { GGML_ASSERT(cur_p->size > 0); @@ -89,7 +113,7 @@ static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) { } static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) { - // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast + // TODO: move bucket sort to separate function so that top_p/typical/softmax first is equally fast // if (k >= (int32_t)cur_p->size) { // return; // } @@ -427,6 +451,9 @@ static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl* static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { auto * ctx = (llama_sampler_dist *) smpl->ctx; + + llama_sampler_softmax_impl(cur_p); + cur_p->selected = llama_sample_dist(cur_p, ctx->rng); } @@ -706,101 +733,6 @@ struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) { }; } -// tail-free - -struct llama_sampler_tail_free { - const float z; - const size_t min_keep; -}; - -static const char * llama_sampler_tail_free_name(const struct llama_sampler * /*smpl*/) { - return "tail-free"; -} - -static void llama_sampler_tail_free_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { - const auto * ctx = (llama_sampler_tail_free *) smpl->ctx; - - if (ctx->z >= 1.0f || cur_p->size <= 2) { - return; - } - - llama_sampler_softmax_impl(cur_p); - - // Compute the first and second derivatives - std::vector first_derivatives(cur_p->size - 1); - std::vector second_derivatives(cur_p->size - 2); - - for (size_t i = 0; i < first_derivatives.size(); ++i) { - first_derivatives[i] = cur_p->data[i].p - cur_p->data[i + 1].p; - } - for (size_t i = 0; i < second_derivatives.size(); ++i) { - second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1]; - } - - // Calculate absolute value of second derivatives - for (size_t i = 0; i < second_derivatives.size(); ++i) { - second_derivatives[i] = std::abs(second_derivatives[i]); - } - - // Normalize the second derivatives - { - const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f); - - if (second_derivatives_sum > 1e-6f) { - for (float & value : second_derivatives) { - value /= second_derivatives_sum; - } - } else { - for (float & value : second_derivatives) { - value = 1.0f / second_derivatives.size(); - } - } - } - - float cum_sum = 0.0f; - size_t last_idx = cur_p->size; - for (size_t i = 0; i < second_derivatives.size(); ++i) { - cum_sum += second_derivatives[i]; - - // Check if the running sum is greater than z or if we have kept at least min_keep tokens - if (cum_sum > ctx->z && i >= ctx->min_keep) { - last_idx = i; - break; - } - } - - // Resize the output vector to keep only the tokens above the tail location - cur_p->size = last_idx; -} - -static struct llama_sampler * llama_sampler_tail_free_clone(const struct llama_sampler * smpl) { - const auto * ctx = (const llama_sampler_tail_free *) smpl->ctx; - return llama_sampler_init_tail_free(ctx->z, ctx->min_keep); -} - -static void llama_sampler_tail_free_free(struct llama_sampler * smpl) { - delete (llama_sampler_tail_free *) smpl->ctx; -} - -static struct llama_sampler_i llama_sampler_tail_free_i = { - /* .name = */ llama_sampler_tail_free_name, - /* .accept = */ nullptr, - /* .apply = */ llama_sampler_tail_free_apply, - /* .reset = */ nullptr, - /* .clone = */ llama_sampler_tail_free_clone, - /* .free = */ llama_sampler_tail_free_free, -}; - -struct llama_sampler * llama_sampler_init_tail_free(float z, size_t min_keep) { - return new llama_sampler { - /* .iface = */ &llama_sampler_tail_free_i, - /* .ctx = */ new llama_sampler_tail_free { - /* .z = */ z, - /*. min_keep = */ min_keep, - }, - }; -} - // typical struct llama_sampler_typical { @@ -912,9 +844,8 @@ static const char * llama_sampler_temp_name(const struct llama_sampler * /*smpl* static void llama_sampler_temp_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { const auto * ctx = (llama_sampler_temp *) smpl->ctx; - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= ctx->temp; - } + + llama_sampler_temp_impl(cur_p, ctx->temp); } static struct llama_sampler * llama_sampler_temp_clone(const struct llama_sampler * smpl) { @@ -961,6 +892,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke if (ctx->delta > 0) { const float min_temp = std::max(0.0f, ctx->temp - ctx->delta); const float max_temp = ctx->temp + ctx->delta; + float exponent_val = ctx->exponent; // no need to do anything if there is only one (or zero) candidates @@ -998,9 +930,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke #endif // Apply the dynamically calculated temperature scaling - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= dyn_temp; - } + llama_sampler_temp_impl(cur_p, dyn_temp); // Re-compute softmax probabilities after scaling logits with dynamic temperature const double max_l_double = cur_p->data[0].logit; @@ -1024,9 +954,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke } #endif } else { - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= ctx->temp; - } + llama_sampler_temp_impl(cur_p, ctx->temp); } } @@ -1059,6 +987,101 @@ struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, floa }; } +// xtc + +struct llama_sampler_xtc { + const float probability; + const float threshold; + const size_t min_keep; + + const uint32_t seed; + uint32_t seed_cur; + + std::mt19937 rng; +}; + +static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) { + return "xtc"; +} + +static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_xtc *) smpl->ctx; + + if (ctx->probability <= 0.0f + || ctx->threshold > 0.5f + || cur_p->size < 2) { + return; + } + + std::uniform_real_distribution distribution(0.0f, 1.0f); + float chance = distribution(ctx->rng); + if (chance > ctx->probability) return; + + // in case it's not sorted/recalculated yet + llama_sampler_softmax_impl(cur_p); + + int pos_last = 0; + + for (size_t i = 0; i < cur_p->size; ++i) { + if (cur_p->data[i].p >= ctx->threshold) { + pos_last = i; + } else break; + } + + if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) { + cur_p->data += pos_last; + cur_p->size -= pos_last; + } +} + +static struct llama_sampler * llama_sampler_xtc_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_xtc *) smpl->ctx; + auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->min_keep, ctx->seed); + + // copy the state + { + auto * result_ctx = (llama_sampler_xtc *) result->ctx; + + result_ctx->rng = ctx->rng; + } + + return result; +} + +static void llama_sampler_xtc_free(struct llama_sampler * smpl) { + delete (llama_sampler_xtc *) smpl->ctx; +} + +static void llama_sampler_xtc_reset(struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_xtc *) smpl->ctx; + ctx->seed_cur = get_rng_seed(ctx->seed); + ctx->rng.seed(ctx->seed_cur); +} + +static struct llama_sampler_i llama_sampler_xtc_i = { + /* .name = */ llama_sampler_xtc_name, + /* .accept = */ nullptr, + /* .apply = */ llama_sample_xtc_apply, + /* .reset = */ llama_sampler_xtc_reset, + /* .clone = */ llama_sampler_xtc_clone, + /* .free = */ llama_sampler_xtc_free, +}; + +struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) { + auto seed_cur = get_rng_seed(seed); + return new llama_sampler { + /* .iface = */ &llama_sampler_xtc_i, + /* .ctx = */ new llama_sampler_xtc { + /* .probability = */ p, + /* .threshold = */ t, + /* .min_keep = */ min_keep, + /* .seed = */ seed, + /* .seed_cur = */ seed_cur, + /* .rng = */ std::mt19937(seed_cur), + }, + }; +} + // mirostat struct llama_sampler_mirostat { @@ -1565,6 +1588,400 @@ struct llama_sampler * llama_sampler_init_penalties( }; } +// DRY + +struct llama_sampler_dry { + int32_t total_context_size; + + const float dry_multiplier; + const float dry_base; + const int32_t dry_allowed_length; + const int32_t dry_penalty_last_n; + + std::unordered_multimap> dry_processed_breakers; + std::vector dry_repeat_count; + std::unordered_map dry_max_token_repeat; + ring_buffer last_tokens; +}; + +// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am) +static void get_overlapping_token_sequences(const llama_vocab & vocab, const std::string& str, std::unordered_multimap>& token_sequences, int max_tail_len = -1) { + for (llama_token token_id = 0; token_id < (llama_token)vocab.n_vocab; token_id++) { + std::string word = llama_detokenize(vocab, {token_id}, true); + if (word.find(str) != std::string::npos) { + token_sequences.emplace(token_id, std::vector()); + } else { + size_t word_len = word.size(), str_len = str.size(); + size_t pos = -1; + while ((pos = word.find(str[0], pos + 1)) != std::string::npos) { + bool match = true; + size_t i; + for (i = 1; i < str_len && i + pos < word_len; ++i) { + if (word[pos + i] != str[i]) { + match = false; + break; + } + } + if (match) { + std::vector tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false); + if (max_tail_len >= 0 && tokenization.size() > (size_t)max_tail_len) { + tokenization.resize(max_tail_len); + } + + // Ensure we don't already have a duplicate matching tokenization + auto its = token_sequences.equal_range(token_id); + bool found = false; + for (auto it = its.first; it != its.second; ++it) { + if (tokenization == it->second) { + found = true; + break; + } + } + if (!found) { + token_sequences.emplace(token_id, tokenization); + } + } + } + } + } +} + +static const char * llama_sampler_dry_name(const struct llama_sampler * /*smpl*/) { + return "dry"; +} + +static void llama_sampler_dry_accept(struct llama_sampler * smpl, llama_token token) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) { + return; + } + + ctx->last_tokens.push_back(token); +} + +// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am) +static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + + if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) { + return; + } + + int32_t effective_dry_penalty_last_n = (ctx->dry_penalty_last_n == -1) ? ctx->total_context_size : std::max(ctx->dry_penalty_last_n, 0); + int last_n_repeat = std::min(std::min((int)ctx->last_tokens.size(), effective_dry_penalty_last_n), ctx->total_context_size); + + if (last_n_repeat <= ctx->dry_allowed_length) { + return; + } + + ctx->dry_repeat_count.assign(last_n_repeat, 0); + ctx->dry_max_token_repeat.clear(); + + // Step 1: Look for restart sequences to limit the maximum repetition length. + // Work backwards through the context looking for any token that begins a restart sequence. + // + // The collection `restart_sequences` is a mapping from a "head" token to all "tail" + // sequences that together comprise a restart sequence. This allows us to quickly check + // whether each token is the head of a complete sequence. Most restart sequences are actually + // a single token, and for these the "tail" is an empty vector. + // + // If the token is a "head", test all restart sequences that begin with this token + // (there will often only be one sequence for each token, but if sequences like 'aaaq1' and + // 'aaa1' are used as restart strings, both could start with 'aaa' when tokenized). The + // longest matching sequence (if any) is used to limit the maximum repetition length. + // + // Note that in the case case of a short sequence contained in a longer one, this might fail to + // find the smallest value for `rep_limit`. For example, if 'amniotic' and 'ni' are both used as + // restart sequences, 'ni' will be found first, and since it's shorter it will fail to suppress + // 'otic'. This is a minor issue since fully contained restart sequences are likely to be rare. + // + // This is theoretically worst-case O(N^2) for arbitrary restart sequences, which is why we + // have already clamped the maximum tail sequence length when generating `restart_sequences`. + // With clamping, this scan is O(N) in the context length. + + int rep_limit = last_n_repeat; + for (int i = 0; i < last_n_repeat; ++i) { + llama_token token = ctx->last_tokens.rat(i); + auto its = ctx->dry_processed_breakers.equal_range(token); + if (its.first == ctx->dry_processed_breakers.end()) { + continue; + } + int longest_match = -1; + for (auto it = its.first; it != its.second; ++it) { + // Note that (*it) does not contain the head character, so seq_len will be + // the restart sequence length minus 1. + // In the common case of a single-token restart sequence, (*it) will be empty + // and we will trivially match. + int seq_len = (int)it->second.size(); + if (seq_len > longest_match && seq_len <= (int)i) { + bool match = true; + for (int offset = 0; offset < seq_len; ++offset) { + // The -1 when indexing `last_tokens` is because we already matched the head. + if (it->second[offset] != ctx->last_tokens.rat(i - offset - 1)) { + match = false; + break; + } + } + if (match) { + longest_match = seq_len; + } + } + } + if (longest_match >= 0) { + // We found a restart sequence starting `i` tokens from the end and continuing for + // `longest_match` tokens. + rep_limit = i - longest_match; + break; + } + } + if (rep_limit < ctx->dry_allowed_length) { + return; + } + + // Step 2: Iterate in reverse over the last N tokens of the context, using the "Z-algorithm" (in + // the reverse direction) to efficiently compute the positions and lengths of suffixes appearing + // elsewhere in the context. We limit the suffix length to `rep_limit` to respect restart sequences. + // + // This algorithm is not currently documented on Wikipedia, but there is a clear description here: + // https://ivanyu.me/blog/2014/10/15/z-algorithm/ + // + // The code below is adapted from the public domain implementation by the same author here: + // https://github.com/ivanyu/string-algorithms/blob/master/z_algorithm.py + // + // Example: + // Last N tokens: a b c c b c y a b c + // Repeat counts: 0 0 3 1 0 2 0 0 0 0 + // ^ + // This `3` means that the last three tokens of the context (a b c) also appear here. + // + // This step is worst case O(N) since the Z-algorithm is linear, despite the appearance of nested + // for/while loops. This can be seen by observing that the `lt` and `rt` bounds are set after each + // repeated suffix is detected (i.e. after each while loop when n > 0). These bound variables + // ensure that the inner while loops only examine each token in the context once as the outer + // for loop iterates over the context. + + { + const int last = last_n_repeat - 1; + int rt = 0, lt = 0; + + for (int k = 1; k < last_n_repeat; ++k) { + if (k > rt) { + // If k is outside the current Z-box, do naive computation. + int n = 0; + while (n + k < last_n_repeat && ctx->last_tokens.rat(n) == ctx->last_tokens.rat(n+k)) { + ++n; + } + ctx->dry_repeat_count[last - k] = std::min(n, rep_limit); + if (n > 0) { + lt = k; + rt = k+n-1; + } + } else { + // If k is inside the current Z-box, consider two cases. + + int p = k - lt; // Pair index. + int right_part_len = rt - k + 1; + + if (ctx->dry_repeat_count[last - p] < right_part_len) { + int n = std::min(ctx->dry_repeat_count[last - p], rep_limit); + ctx->dry_repeat_count[last - k] = n; + } else { + int i = rt + 1; + while (i < last_n_repeat && ctx->last_tokens.rat(i) == ctx->last_tokens.rat(i - k)) { + i += 1; + } + + int n = std::min(i - k, rep_limit); + ctx->dry_repeat_count[last - k] = n; + lt = k; + rt = i - 1; + } + } + } + } + + // Step 3: Iterate over dry_repeat_count and last_tokens, examining the maximum repeat length + // that would be generated by emitting each new token that would extend a sequence. + // + // Following the same example as above: + // Last N tokens: a b c c b c y a b c + // Repeat counts: 0 0 3 1 0 2 0 0 0 0 + // + // For each non-zero, look ahead one token. This token, if emitted, would extend the repetition. + // c: 3 -> 4 (from `a b c` to `a b c c`) + // b: 1 -> 2 (from `c` to `c b`) + // y: 2 -> 3 (from `b c` to `b c y`) + + for (int i = 0; i < last_n_repeat - 1; ++i) { + int repeat_len = ctx->dry_repeat_count[i]; + if (repeat_len >= ctx->dry_allowed_length) { + // This token ends a repeat, so the next token would continue one. + // By convention, the value of `repeat_len` only includes the tokens currently + // in the context, not the new token that would be added. + llama_token token = ctx->last_tokens.rat(last_n_repeat - 2 - i); + // Track the maximum sequence ending in this token. + const auto& it = ctx->dry_max_token_repeat.find(token); + if (it == ctx->dry_max_token_repeat.end() || it->second < repeat_len) { + ctx->dry_max_token_repeat[token] = repeat_len; + } + } + } + + // Step 4: Apply logit penalties based on the maximum repeat length for relevant tokens. + + // Prevent floating point overflow in `pow(penalty_base, exponent)` by clamping to `max_exponent`. + // Compute it from `penalty_base` and the approximate log of `std::numeric_limits::max()` + const float FLOAT_MAX_LOG = 88.7228391f; + int max_exponent = 0; + if (ctx->dry_base > 1.000001f) { + max_exponent = FLOAT_MAX_LOG / std::log(ctx->dry_base); + } + + for (size_t i = 0; i < cur_p->size; ++i) { + const auto& af_kvp = ctx->dry_max_token_repeat.find(cur_p->data[i].id); + if (af_kvp != ctx->dry_max_token_repeat.end()) { + // Check all sequence breakers starting with this token + auto range = ctx->dry_processed_breakers.equal_range(cur_p->data[i].id); + bool is_single_token_breaker = false; + + for (auto it = range.first; it != range.second; ++it) { + if (it->second.empty()) { + is_single_token_breaker = true; + break; + } + } + + // Apply penalty only if it's not a single-token sequence breaker + if (!is_single_token_breaker) { + int repeat_exp = af_kvp->second - ctx->dry_allowed_length; + if (max_exponent > 0 && repeat_exp > max_exponent) { + repeat_exp = max_exponent; + } + float penalty = ctx->dry_multiplier * std::pow(ctx->dry_base, repeat_exp); + cur_p->data[i].logit -= penalty; + } + } + } + + cur_p->sorted = false; +} + +static void llama_sampler_dry_reset(struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + ctx->last_tokens.clear(); + ctx->dry_repeat_count.clear(); + ctx->dry_max_token_repeat.clear(); +} + +static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler * smpl) { + const auto * ctx = (llama_sampler_dry *) smpl->ctx; + + llama_vocab dummy_vocab; + + // dummy vocab is passed because it is only needed for raw sequence breaker processing, which we have already done and will simply be copying + auto * result = llama_sampler_init_dry_impl(dummy_vocab, ctx->total_context_size, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0); + + // Copy the state, including the processed breakers + { + auto * result_ctx = (llama_sampler_dry *) result->ctx; + result_ctx->dry_processed_breakers = ctx->dry_processed_breakers; + result_ctx->dry_repeat_count = ctx->dry_repeat_count; + result_ctx->dry_max_token_repeat = ctx->dry_max_token_repeat; + result_ctx->last_tokens = ctx->last_tokens; + } + + return result; +} + +static void llama_sampler_dry_free(struct llama_sampler * smpl) { + delete (llama_sampler_dry *) smpl->ctx; +} + +static struct llama_sampler_i llama_sampler_dry_i = { + /* .name = */ llama_sampler_dry_name, + /* .accept = */ llama_sampler_dry_accept, + /* .apply = */ llama_sampler_dry_apply, + /* .reset = */ llama_sampler_dry_reset, + /* .clone = */ llama_sampler_dry_clone, + /* .free = */ llama_sampler_dry_free, +}; + +struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) { + int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? context_size : std::max(dry_penalty_last_n, 0); + std::unordered_multimap> processed_breakers; + const int MAX_CHAR_LEN = 40; + const int MAX_SEQ_LEN = 20; + + const bool dry_enabled = (dry_multiplier != 0.0f && dry_base >= 1.0f && dry_penalty_last_n != 0); + + if (dry_enabled && seq_breakers != nullptr && num_breakers > 0) { + // Process sequence breakers + for (size_t i = 0; i < num_breakers; ++i) { + if (seq_breakers[i] == nullptr || std::strlen(seq_breakers[i]) == 0) { + LLAMA_LOG_WARN("skipping null or empty DRY sequence breaker at index %zu\n", i); + continue; + } + + std::string sequence_break(seq_breakers[i]); + if (sequence_break.empty()) { + LLAMA_LOG_WARN("skipping empty DRY sequence breaker\n"); + continue; + } + + if (sequence_break.size() > MAX_CHAR_LEN) { + LLAMA_LOG_WARN("truncating DRY sequence breaker to %d characters\n", MAX_CHAR_LEN); + sequence_break.resize(MAX_CHAR_LEN); + } + + get_overlapping_token_sequences(vocab, sequence_break, processed_breakers, MAX_SEQ_LEN); + } + } + + return new llama_sampler { + /* .iface = */ &llama_sampler_dry_i, + /* .ctx = */ new llama_sampler_dry { + /* .total_context_size = */ context_size, + /* .dry_multiplier = */ dry_multiplier, + /* .dry_base = */ dry_base, + /* .dry_allowed_length = */ dry_allowed_length, + /* .dry_penalty_last_n = */ dry_penalty_last_n, + /* .dry_processed_breakers = */ std::move(processed_breakers), + /* .dry_repeat_count = */ dry_enabled ? std::vector(effective_dry_penalty_last_n, 0) : std::vector{}, + /* .dry_max_token_repeat = */ {}, + /* .last_tokens = */ dry_enabled ? ring_buffer(effective_dry_penalty_last_n) : ring_buffer(0), + }, + }; +} + +// wrapper for test-sampling.cpp +struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector>& seq_breakers) { + llama_vocab dummy_vocab; + auto * result = llama_sampler_init_dry_impl(dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0); + auto * ctx = (llama_sampler_dry *) result->ctx; + + // Process the token-based sequence breakers + ctx->dry_processed_breakers.clear(); + if (seq_breakers.empty()) { + LLAMA_LOG_WARN("empty DRY sequence breakers list in llama_sampler_init_dry_testing\n"); + } else { + for (const auto& breaker : seq_breakers) { + if (breaker.empty()) { + LLAMA_LOG_WARN("skipping DRY empty sequence breaker\n"); + continue; + } + llama_token head_token = breaker[0]; + std::vector tail_tokens(breaker.begin() + 1, breaker.end()); + ctx->dry_processed_breakers.emplace(head_token, std::move(tail_tokens)); + } + + if (ctx->dry_processed_breakers.empty()) { + LLAMA_LOG_WARN("no valid DRY sequence breakers processed in llama_sampler_init_dry_testing\n"); + } + } + + return result; +} + // logit-bias struct llama_sampler_logit_bias { @@ -1644,6 +2061,229 @@ struct llama_sampler * llama_sampler_init_logit_bias( }; } +// infill + +//#define GGML_DEBUG_SAMPLER_INFILL + +struct llama_sampler_infill { + const struct llama_vocab * vocab; + + std::vector buf0; + std::vector buf1; +}; + +static const char * llama_sampler_infill_name(const struct llama_sampler * /*smpl*/) { + return "infill"; +} + +static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_infill *) smpl->ctx; + + llama_sampler_softmax_impl(cur_p); + +#if defined(GGML_DEBUG_SAMPLER_INFILL) +#define LOG_DBG_CUR LLAMA_LOG_DEBUG +#else +#define LOG_DBG_CUR(...) +#endif + + for (size_t i = 0; i < cur_p->size; ++i) { + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + + float p_txt_sum = 0.0f; + float p_eog_sum = 0.0f; + + for (size_t i = 0; i < cur_p->size; ++i) { + if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) { + p_eog_sum += cur_p->data[i].p; + } else { + p_txt_sum += cur_p->data[i].p; + } + } + + const float rat = p_eog_sum == 0.0 ? INFINITY : p_txt_sum / p_eog_sum; GGML_UNUSED(rat); + + LOG_DBG_CUR("%s: p_txt_sum = %.2f, p_eog_sum = %.2f, rat = %.2f, n = %zu\n", __func__, p_txt_sum, p_eog_sum, rat, cur_p->size); + + if (3*p_eog_sum*cur_p->size > p_txt_sum) { + LOG_DBG_CUR("%s: the ratio p_txt/p_eog = %.2f is too low -> sampling EOG\n", __func__, p_txt_sum/p_eog_sum); + + // keep just the EOG tokens + const auto size_org = cur_p->size; + + cur_p->size = 0; + + float p_sum = 0.0f; + + for (size_t i = 0; i < size_org; ++i) { + if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) { + p_sum += cur_p->data[i].p; + + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + } + + return; + } + + size_t n_combined = 0; GGML_UNUSED(n_combined); + + // combine tokens with common prefix + for (size_t i0 = 0; i0 < cur_p->size; ++i0) { + for (size_t i1 = 0; i1 < cur_p->size; ++i1) { + if (cur_p->data[i0].logit == -INFINITY) { + break; + } + + if (i0 == i1 || cur_p->data[i1].logit == -INFINITY) { + continue; + } + + int len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false); + if (len0 < 0) { + ctx->buf0.resize(len0); + len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false); + assert(len0 > 0); + } + + int len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false); + if (len1 < 0) { + ctx->buf1.resize(len1); + len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false); + assert(len1 > 0); + } + + // token i0 is a prefix of token i1 + if (len0 > 0 && len0 <= len1 && memcmp(ctx->buf0.data(), ctx->buf1.data(), len0) == 0) { + int dst = i0; + int src = i1; + + // merge into the token with higher probability + if (cur_p->data[i1].p > cur_p->data[i0].p) { + std::swap(dst, src); + } + + cur_p->data[dst].p += cur_p->data[src].p; + cur_p->data[src].logit = -INFINITY; + cur_p->data[src].p = 0.0f; + + n_combined++; + } + } + } + + size_t n_non_eog = 0; + + size_t size_org = cur_p->size; + + float p_sum = 0.0f; + float thold = 0.2f; + + cur_p->size = 0; + + LOG_DBG_CUR("%s: n_combined = %zu, applying thold = %.3f\n", __func__, n_combined, thold); + + for (size_t i = 0; i < size_org; ++i) { + const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id); + + if (cur_p->data[i].p < thold && !is_eog) { + continue; + } + + if (!is_eog) { + ++n_non_eog; + } + + p_sum += cur_p->data[i].p; + + // keep this token + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + + LOG_DBG_CUR("%s: n_non_eog = %zu\n", __func__, n_non_eog); + + // if no non-EOG tokens are left -> reduce cur_p to single EOT token + if (n_non_eog == 0) { + cur_p->size = 1; + cur_p->data[0].id = llama_token_eot_impl(*ctx->vocab); + cur_p->data[0].logit = 1.0f; + + return; + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + + size_org = cur_p->size; + p_sum = 0.0f; + thold = 1.0/(n_non_eog + 1); + + cur_p->size = 0; + + LOG_DBG_CUR("%s: applying thold = %.3f\n", __func__, thold); + + for (size_t i = 0; i < size_org; ++i) { + const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id); + + if (cur_p->data[i].p < thold && !is_eog) { + continue; + } + + p_sum += cur_p->data[i].p; + + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + +#undef LOG_DBG_CUR +} + +static struct llama_sampler * llama_sampler_infill_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_infill *) smpl->ctx; + return llama_sampler_init_infill_impl(*ctx->vocab); +} + +static void llama_sampler_infill_free(struct llama_sampler * smpl) { + delete (llama_sampler_infill *) smpl->ctx; +} + +static struct llama_sampler_i llama_sampler_infill_i = { + /* .name = */ llama_sampler_infill_name, + /* .accept = */ nullptr, + /* .apply = */ llama_sampler_infill_apply, + /* .reset = */ nullptr, + /* .clone = */ llama_sampler_infill_clone, + /* .free = */ llama_sampler_infill_free, +}; + +struct llama_sampler * llama_sampler_init_infill_impl( + const struct llama_vocab & vocab) { + return new llama_sampler { + /* .iface = */ &llama_sampler_infill_i, + /* .ctx = */ new llama_sampler_infill { + /* .vocab = */ &vocab, + /* .buf0 = */ std::vector(512), + /* .buf1 = */ std::vector(512), + }, + }; +} + // utils uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) { diff --git a/src/llama-sampling.h b/src/llama-sampling.h index d90b147130..919f6fdfce 100644 --- a/src/llama-sampling.h +++ b/src/llama-sampling.h @@ -4,8 +4,6 @@ #include "llama-grammar.h" -#include - struct llama_vocab; struct llama_grammar; @@ -27,3 +25,24 @@ struct llama_sampler * llama_sampler_init_grammar_impl( const struct llama_vocab & vocab, const char * grammar_str, const char * grammar_root); + +struct llama_sampler * llama_sampler_init_infill_impl( + const struct llama_vocab & vocab); + +struct llama_sampler * llama_sampler_init_dry_impl( + const struct llama_vocab & vocab, + int32_t context_size, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const char ** seq_breakers, + size_t num_breakers); + +struct llama_sampler * llama_sampler_init_dry_testing( + int32_t context_size, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const std::vector>& seq_breakers); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index a27394a377..d1dc96276c 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -221,7 +221,7 @@ struct llm_tokenizer_spm_session { } // seed the work queue with all possible 2-character tokens. - for (size_t i = 1; i < symbols.size(); ++i) { + for (int i = 1; i < (int) symbols.size(); ++i) { try_add_bigram(i - 1, i); } @@ -563,7 +563,7 @@ struct llm_tokenizer_bpe_session { index++; symbols.emplace_back(sym); } - for (size_t i = 1; i < symbols.size(); ++i) { + for (int i = 1; i < (int) symbols.size(); ++i) { add_new_bigram(i - 1, i); } @@ -1966,3 +1966,19 @@ int32_t llama_detokenize_impl( return total <= text_len_max ? total : -total; } + +std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector & tokens, bool special) { + std::string text; + text.resize(std::max(text.capacity(), tokens.size())); + int32_t n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + if (n_chars < 0) { + text.resize(-n_chars); + n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization + } + + text.resize(n_chars); + + // NOTE: the original tokenizer decodes bytes after collecting the pieces. + return text; +} diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 17e14488a4..4bb16d2e42 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -48,7 +48,7 @@ struct llama_vocab { id special_cls_id = LLAMA_TOKEN_NULL; id special_mask_id = LLAMA_TOKEN_NULL; - id linefeed_id = 13; + id linefeed_id = 13; // fim tokens id special_fim_pre_id = LLAMA_TOKEN_NULL; @@ -149,6 +149,12 @@ int32_t llama_token_to_piece_impl( int32_t lstrip, bool special); +// check if token0 is contained as a prefix in token1 +bool llama_token_is_prefix_impl( + const struct llama_vocab & vocab, + llama_token token0, + llama_token token1); + int32_t llama_detokenize_impl( const struct llama_vocab & vocab, const llama_token * tokens, @@ -157,3 +163,8 @@ int32_t llama_detokenize_impl( int32_t text_len_max, bool remove_special, bool unparse_special); + +std::string llama_detokenize( + const struct llama_vocab & vocab, + const std::vector & tokens, + bool special); diff --git a/src/llama.cpp b/src/llama.cpp index 0b3b181f70..510b7fe893 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -7,16 +7,7 @@ #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" - -#if defined(GGML_USE_VULKAN) -# include "ggml-vulkan.h" -#elif defined(GGML_USE_SYCL) -# include "ggml-sycl.h" -#elif defined(GGML_USE_KOMPUTE) -# include "ggml-kompute.h" -#elif defined(GGML_USE_CANN) -# include "ggml-cann.h" -#endif +#include "ggml-cpp.h" // TODO: replace with ggml API call #define QK_K 256 @@ -189,6 +180,7 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_OLMO_1124, LLM_ARCH_OLMOE, LLM_ARCH_OPENELM, LLM_ARCH_ARCTIC, @@ -243,6 +235,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_OLMO_1124, "olmo_1124" }, { LLM_ARCH_OLMOE, "olmoe" }, { LLM_ARCH_OPENELM, "openelm" }, { LLM_ARCH_ARCTIC, "arctic" }, @@ -1252,6 +1245,25 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_OLMO_1124, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_OLMOE, { @@ -1592,44 +1604,52 @@ static llm_arch llm_arch_from_string(const std::string & name) { // std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias" // std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight" // +struct LLM_TN_IMPL { + const llm_arch arch; + const llm_tensor tensor; + const char * const suffix; + const int bid; + const int xid; + + std::string str() const { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { + return "__missing__"; + } + + std::string name = ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid); + + if (suffix != nullptr) { + name += "."; + name += suffix; + } + + return name; + } + + operator std::string() const { + return str(); + } + + friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) { + return str == tn.str(); + } + + friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) { + return str != tn.str(); + } +}; + struct LLM_TN { LLM_TN(llm_arch arch) : arch(arch) {} llm_arch arch; - std::string operator()(llm_tensor tensor) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return LLM_TENSOR_NAMES.at(arch).at(tensor); + LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const { + return { arch, tensor, suffix, bid, xid }; } - std::string operator()(llm_tensor tensor, const char * suffix) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return std::string(LLM_TENSOR_NAMES.at(arch).at(tensor)) + "." + suffix; - } - - std::string operator()(llm_tensor tensor, int bid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid); - } - - std::string operator()(llm_tensor tensor, const char * suffix, int bid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid) + "." + suffix; - } - - std::string operator()(llm_tensor tensor, const char * suffix, int bid, int xid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid) + "." + suffix; + LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const { + return { arch, tensor, nullptr, bid, xid }; } }; @@ -2338,6 +2358,7 @@ enum e_model { MODEL_1B, MODEL_1_3B, MODEL_1_4B, + MODEL_1_5B, MODEL_1_6B, MODEL_2B, MODEL_2_8B, @@ -2626,6 +2647,11 @@ struct llama_cparams { // TODO: separate into "llama_layer_enc" and "llama_layer_dec" struct llama_layer { + llama_layer() { + // initialize all pointers to NULL + std::memset(this, 0, sizeof(*this)); + } + // normalization struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm_b; @@ -2709,9 +2735,9 @@ struct llama_layer { struct ggml_tensor * ffn_up_shexp; // ff bias - struct ggml_tensor * ffn_gate_b = nullptr; - struct ggml_tensor * ffn_down_b = nullptr; // b2 - struct ggml_tensor * ffn_up_b = nullptr; // b3 + struct ggml_tensor * ffn_gate_b; + struct ggml_tensor * ffn_down_b; // b2 + struct ggml_tensor * ffn_up_b; // b3 struct ggml_tensor * ffn_act; // mamba proj @@ -3005,32 +3031,23 @@ struct llama_kv_cache { // recurrent state cache for state space models llama_rs_self_cache rs; - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; // NOTE: padding may make this bigger than kv.total_size() + rs.total_size() size_t total_size() const { size_t size = 0; - for (ggml_backend_buffer_t buf : bufs) { - size += ggml_backend_buffer_get_size(buf); + for (auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); } return size; } - - ~llama_kv_cache() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - } }; struct llama_control_vector { std::vector tensors; // per layer - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; int32_t layer_start = -1; int32_t layer_end = -1; @@ -3049,15 +3066,6 @@ struct llama_control_vector { } return cur; } - - ~llama_control_vector() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - } }; struct llama_model { @@ -3070,22 +3078,21 @@ struct llama_model { llama_hparams hparams = {}; llama_vocab vocab; - // TODO: should init all tensors to nullptr - struct ggml_tensor * tok_embd; - struct ggml_tensor * type_embd; - struct ggml_tensor * pos_embd; - struct ggml_tensor * tok_norm; - struct ggml_tensor * tok_norm_b; + struct ggml_tensor * tok_embd = nullptr; + struct ggml_tensor * type_embd = nullptr; + struct ggml_tensor * pos_embd = nullptr; + struct ggml_tensor * tok_norm = nullptr; + struct ggml_tensor * tok_norm_b = nullptr; - struct ggml_tensor * output_norm; - struct ggml_tensor * output_norm_b; - struct ggml_tensor * output; - struct ggml_tensor * output_b; - struct ggml_tensor * output_norm_enc; + struct ggml_tensor * output_norm = nullptr; + struct ggml_tensor * output_norm_b = nullptr; + struct ggml_tensor * output = nullptr; + struct ggml_tensor * output_b = nullptr; + struct ggml_tensor * output_norm_enc = nullptr; // classifier - struct ggml_tensor * cls; - struct ggml_tensor * cls_b; + struct ggml_tensor * cls = nullptr; + struct ggml_tensor * cls_b = nullptr; struct ggml_tensor * cls_out = nullptr; struct ggml_tensor * cls_out_b = nullptr; @@ -3098,30 +3105,30 @@ struct llama_model { int main_gpu; int n_gpu_layers; + std::vector rpc_servers; + // list of devices used in this model std::vector devices; - std::vector rpc_servers; - // layer -> buffer type mapping - struct layer_buft { - layer_buft() : buft_matrix(nullptr), buft(nullptr) {} - layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {} - layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {} + // lists of buffer types used for each layer + using buft_list_t = std::vector>; + buft_list_t cpu_buft_list; + std::map gpu_buft_list; - ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication - ggml_backend_buffer_type_t buft; // everything else + struct layer_dev { + ggml_backend_dev_t dev; + buft_list_t * buft_list; }; - - layer_buft buft_input; - layer_buft buft_output; - std::vector buft_layer; + layer_dev dev_input = {}; + layer_dev dev_output = {}; + std::vector dev_layer; // contexts where the model tensors metadata is stored - std::vector ctxs; + std::vector ctxs; // the model memory buffers for the tensor data - std::vector bufs; + std::vector bufs; // model memory mapped files llama_mmaps mappings; @@ -3133,20 +3140,20 @@ struct llama_model { // for quantize-stats only std::vector> tensors_by_name; - int64_t t_load_us = 0; + int64_t t_load_us = 0; int64_t t_start_us = 0; + // total number of parameters in the model + uint64_t n_elements = 0; + + // total size of all the tensors in the model in bytes + size_t n_bytes = 0; + // keep track of loaded lora adapters std::set lora_adapters; ~llama_model() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - while (!lora_adapters.empty()) { + while (!lora_adapters.empty()) { llama_lora_adapter_free(*lora_adapters.begin()); } } @@ -3157,9 +3164,6 @@ struct llama_sbatch_seq { llama_seq_id * seq_id; size_t offset; size_t length; - - // helper for smoother batch API transition -- can be deprecated in the future - llama_seq_id all_seq_id; // used if seq_id == NULL }; // sequence-length-aware batch splitting @@ -3254,30 +3258,18 @@ struct llama_sbatch { } else { ubatch.embd = nullptr; } - // from here on, the else branches are deprecated; - // they are helpers for smoother batch API transition - if (batch->pos) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; - } - } else { - // simple split - ubatch.pos = batch->pos + seq.offset; + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; } } else { - for (size_t i = 0; i < length; ++i) { - llama_pos bi = ids[seq.offset + i]; - ubatch.pos[ubatch.n_tokens + i] = batch->all_pos_0 + (bi * batch->all_pos_1); - } + // simple split + ubatch.pos = batch->pos + seq.offset; } if (ubatch.equal_seqs) { ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id; if (seq.seq_id) { ubatch.seq_id[ubatch.n_seqs] = seq.seq_id; - } else { - GGML_ASSERT(seq.n_seq_id == 1); - ubatch.seq_id[ubatch.n_seqs] = &seq.all_seq_id; } } else { // simple split @@ -3290,10 +3282,6 @@ struct llama_sbatch { } if (batch->seq_id) { ubatch.seq_id = batch->seq_id + seq.offset; - } else { - for (size_t i = 0; i < length; ++i) { - ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id; - } } } if (logits_all) { @@ -3412,7 +3400,6 @@ struct llama_sbatch { s.seq_id = nullptr; s.offset = 0; s.length = n_tokens; - s.all_seq_id = batch.all_seq_id; return; } std::sort(ids.begin(), ids.end(), @@ -3435,7 +3422,7 @@ struct llama_sbatch { if (batch.pos) { return batch.pos[a] < batch.pos[b]; } - // no pos, sort by id (assuming batch.all_pos_1 is positive) + // no pos, sort by id return a < b; } // shared prompts go first @@ -3445,30 +3432,25 @@ struct llama_sbatch { // init seq llama_sbatch_seq * last_seq = nullptr; - if (batch.n_seq_id != nullptr && batch.seq_id != nullptr) { - for (size_t i = 0; i < n_tokens; ++i) { - const size_t bi = ids[i]; - const int32_t n_seqs = batch.n_seq_id[bi]; - llama_seq_id * seq_ids = batch.seq_id[bi]; - if (last_seq != nullptr) { - bool same = n_seqs == last_seq->n_seq_id; - for (int32_t j = 0; same && j < n_seqs; ++j) { - if (seq_ids[j] != last_seq->seq_id[j]) { - same = false; - } - } - if (same) { - last_seq->length += 1; - continue; + for (size_t i = 0; i < n_tokens; ++i) { + const size_t bi = ids[i]; + const int32_t n_seqs = batch.n_seq_id[bi]; + llama_seq_id * seq_ids = batch.seq_id[bi]; + if (last_seq != nullptr) { + bool same = n_seqs == last_seq->n_seq_id; + for (int32_t j = 0; same && j < n_seqs; ++j) { + if (seq_ids[j] != last_seq->seq_id[j]) { + same = false; } } - llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1, batch.all_seq_id}; - seq.push_back(new_seq); - last_seq = &seq.back(); + if (same) { + last_seq->length += 1; + continue; + } } - } else { - llama_sbatch_seq new_seq = {1, nullptr, 0, n_tokens, batch.all_seq_id}; + llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1}; seq.push_back(new_seq); + last_seq = &seq.back(); } // keep shared prompts first at the end, then sort by length descending. std::sort(seq.begin(), seq.end(), @@ -3488,16 +3470,6 @@ struct llama_context { , t_start_us(model.t_start_us) , t_load_us(model.t_load_us) {} - ~llama_context() { - ggml_backend_sched_free(sched); - - for (ggml_backend_t backend : backends) { - ggml_backend_free(backend); - } - - ggml_backend_buffer_free(buf_output); - } - const struct llama_model & model; struct llama_cparams cparams; @@ -3507,7 +3479,7 @@ struct llama_context { std::unordered_map lora_adapters; - std::vector backends; + std::vector backends; std::vector> set_n_threads_fns; ggml_backend_t backend_cpu = nullptr; @@ -3529,7 +3501,7 @@ struct llama_context { mutable int32_t n_eval = 0; // number of eval calls // host buffer for the model output (logits and embeddings) - ggml_backend_buffer_t buf_output = nullptr; + ggml_backend_buffer_ptr buf_output; // decode output (2-dimensional array: [n_outputs][n_vocab]) size_t logits_size = 0; // capacity (of floats) for logits @@ -3559,7 +3531,7 @@ struct llama_context { // memory buffers used to evaluate the model std::vector buf_compute_meta; - ggml_backend_sched_t sched = nullptr; + ggml_backend_sched_ptr sched; ggml_abort_callback abort_callback = nullptr; void * abort_callback_data = nullptr; @@ -3592,8 +3564,8 @@ struct llama_lora_adapter { struct llama_model * base_model; // map tensor name to lora_a_b std::unordered_map ab_map; - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; float alpha; @@ -3611,12 +3583,6 @@ struct llama_lora_adapter { } ~llama_lora_adapter() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } auto pos = base_model->lora_adapters.find(this); if (pos != base_model->lora_adapters.end()) { base_model->lora_adapters.erase(pos); @@ -3625,142 +3591,44 @@ struct llama_lora_adapter { }; static int llama_get_device_count(const llama_model & model) { - int count = (int) model.devices.size(); - -#if defined(GGML_USE_SYCL) - count += ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - count += ggml_backend_vk_get_device_count(); -#elif defined(GGML_USE_CANN) - count += ggml_backend_cann_get_device_count(); -#endif - - return count; - - GGML_UNUSED(model); + return (int) model.devices.size(); } -static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_model & model, bool host_buffer) { - ggml_backend_buffer_type_t buft = nullptr; +template +static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) { + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead()*8, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context_ptr ctx { ggml_init(params) }; + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } - if (host_buffer) { - for (auto * dev : model.devices) { - buft = ggml_backend_dev_host_buffer_type(dev); - if (buft != nullptr) { - break; - } + ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) }; + ggml_tensor * op_tensor = fn(ctx.get()); + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op_tensor->src[i] != nullptr) { + assert(op_tensor->src[i]->buffer == nullptr); + op_tensor->src[i]->buffer = buf.get(); } } + bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); -#if defined(GGML_USE_SYCL) - if (host_buffer) { - buft = ggml_backend_sycl_host_buffer_type(); - } -#elif defined(GGML_USE_CANN) - if (host_buffer) { - buft = ggml_backend_cann_host_buffer_type(); - } -#elif defined(GGML_USE_CPU_HBM) - buft = ggml_backend_cpu_hbm_buffer_type(); -#elif defined(GGML_USE_VULKAN) - if (host_buffer) { - buft = ggml_backend_vk_host_buffer_type(); - } -#endif - - if (buft == nullptr) { - buft = ggml_backend_cpu_buffer_type(); - } - return buft; - - GGML_UNUSED(host_buffer); + return op_supported; } -static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int device) { - ggml_backend_buffer_type_t buft = nullptr; - - if (device < (int)model.devices.size()) { - return ggml_backend_dev_buffer_type(model.devices[device]); - } - device -= (int)model.devices.size(); - -#if defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(device); -#elif defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_buffer_type(device); -#elif defined(GGML_USE_KOMPUTE) - buft = ggml_backend_kompute_buffer_type(device); -#elif defined(GGML_USE_CANN) - buft = ggml_backend_cann_buffer_type(device); -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_cpu(model, true); - } - return buft; - - GGML_UNUSED(model); -} - -static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) { - ggml_backend_buffer_type_t buft = nullptr; - - // find a backend that supports split buffers - for (size_t i = 0; i < ggml_backend_reg_count(); ++i) { - ggml_backend_reg_t reg = ggml_backend_reg_get(i); - - auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type"); - if (ggml_backend_split_buffer_type_fn) { - buft = ggml_backend_split_buffer_type_fn(tensor_split); - if (buft != nullptr) { - break; - } +template +static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) { + for (const auto & cur : buft_list) { + ggml_backend_dev_t cur_dev = cur.first; + ggml_backend_buffer_type_t cur_buft = cur.second; + if (buft_supported(cur_buft, cur_dev, fn)) { + return cur_buft; } } - -#ifdef GGML_USE_SYCL - if (ggml_backend_sycl_get_device_count() > 1) { - buft = ggml_backend_sycl_split_buffer_type(tensor_split); - } -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_offload(model, fallback_gpu); - } - return buft; - - GGML_UNUSED(tensor_split); -} - -static size_t llama_get_device_memory(const llama_model & model, int device) { - if (device < (int)model.devices.size()) { - ggml_backend_dev_t dev = model.devices[device]; - size_t total; - size_t free; - ggml_backend_dev_memory(dev, &free, &total); - return free; - } - -#if defined(GGML_USE_SYCL) - size_t total; - size_t free; - ggml_backend_sycl_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_VULKAN) - size_t total; - size_t free; - ggml_backend_vk_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_CANN) - size_t total; - size_t free; - ggml_backend_cann_get_device_memory(device, &free, &total); - return free; -#else - return 1; -#endif - GGML_UNUSED(model); - GGML_UNUSED(device); + throw std::runtime_error(format("no suitable buffer type found")); } // @@ -3803,34 +3671,27 @@ static bool llama_kv_cache_init( cache.rs.seq_tails.clear(); cache.rs.seq_tails.resize(rs_size); - // count used buffer types - std::map buft_layer_count; - if (offload) { - for (int64_t i = 0; i < n_layer; ++i) { - buft_layer_count[model.buft_layer[i].buft]++; - } - } else { - buft_layer_count[llama_default_buffer_type_cpu(model, true)] = n_layer; - } - // create a context for each buffer type std::map ctx_map; - for (auto & it : buft_layer_count) { - int n_layers = it.second; - // TODO: for mixed architectures, avoid allocating empty recurrent state or kv cache tensors - struct ggml_init_params params = { - /*.mem_size =*/ 2*(has_kv + has_rs)*n_layers*ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__); - return false; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + // TODO: for mixed architectures, avoid allocating empty recurrent state or kv cache tensors + struct ggml_init_params params = { + /*.mem_size =*/ size_t(2*(has_kv + has_rs)*n_layer*ggml_tensor_overhead()), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + ctx_map[buft] = ctx; + cache.ctxs.emplace_back(ctx); + return ctx; } - ctx_map[it.first] = ctx; - cache.ctxs.push_back(ctx); - } + return it->second; + }; if (has_kv) { cache.kv.k_l.reserve(n_layer); @@ -3842,7 +3703,20 @@ static bool llama_kv_cache_init( } for (int i = 0; i < (int) n_layer; i++) { - struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); + ggml_backend_buffer_type_t buft; + if (offload) { + auto * dev = model.dev_layer.at(i).dev; + buft = ggml_backend_dev_buffer_type(dev); + } else { + buft = ggml_backend_cpu_buffer_type(); + } + ggml_context * ctx = ctx_for_buft(buft); + + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__); + return false; + } + if (has_kv) { ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, hparams.n_embd_k_gqa(i)*kv_size); ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, hparams.n_embd_v_gqa(i)*kv_size); @@ -3863,8 +3737,9 @@ static bool llama_kv_cache_init( // allocate tensors and initialize the buffers to avoid NaNs in the padding for (auto it : ctx_map) { - ggml_backend_buffer_type_t buft = it.first; - ggml_context * ctx = it.second; + auto * buft = it.first; + auto * ctx = it.second; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); if (!buf) { if (!has_kv && !has_rs) { @@ -3876,17 +3751,30 @@ static bool llama_kv_cache_init( } ggml_backend_buffer_clear(buf, 0); LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); - cache.bufs.push_back(buf); + cache.bufs.emplace_back(buf); } return true; } +// a structure holds information about the slot found in llama_kv_cache_find_slot +struct llama_kv_cache_slot_info { + std::pair boundaries; // slot boundaries [begin, end) + bool found = false; // the slot was found + + explicit llama_kv_cache_slot_info(bool found_) : found{found_} {} + llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {} + + operator bool() const { return found; } +}; +static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false}; + // find an empty slot of size "n_tokens" in the cache // updates the cache head +// returns a structure holding information about the slot found // Note: On success, it's important that cache.head points // to the first cell of the slot. -static bool llama_kv_cache_find_slot( +static struct llama_kv_cache_slot_info llama_kv_cache_find_slot( struct llama_kv_cache & cache, const struct llama_ubatch & batch) { struct llama_kv_self_cache & kv_self = cache.kv; @@ -3902,7 +3790,7 @@ static bool llama_kv_cache_find_slot( if (rs_size > 0) { if (!batch.equal_seqs) { LLAMA_LOG_ERROR("%s: can't process batch with unequal new tokens per sequence for recurrent models\n", __func__); - return false; + return llama_kv_cache_slot_info_failed; } // everything should fit if all seq_ids are smaller than the max @@ -3915,7 +3803,7 @@ static bool llama_kv_cache_find_slot( // too big seq_id // TODO: would it be possible to resize the rs cache size instead? LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, rs_size); - return false; + return llama_kv_cache_slot_info_failed; } } } @@ -3925,7 +3813,7 @@ static bool llama_kv_cache_find_slot( // one KV cell per token if (n_tokens > kv_size) { LLAMA_LOG_ERROR("%s: n_tokens=%d > kv_size=%d\n", __func__, n_tokens, kv_size); - return false; + return llama_kv_cache_slot_info_failed; } // if we have enough unused cells before the current head -> @@ -3959,7 +3847,7 @@ static bool llama_kv_cache_find_slot( if (n_tested >= kv_size) { //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens); - return false; + return llama_kv_cache_slot_info_failed; } } } @@ -4093,6 +3981,8 @@ static bool llama_kv_cache_find_slot( // allow getting the range of used cells, from head to head + n rs_self.head = min; rs_self.n = max - min + 1; + rs_self.used = std::count_if(rs_self.cells.begin(), rs_self.cells.end(), + [](const llama_rs_cell& cell){ return !cell.is_empty(); }); } if (kv_size > 0) { @@ -4110,7 +4000,7 @@ static bool llama_kv_cache_find_slot( kv_self.used += n_tokens; } - return true; + return llama_kv_cache_slot_info(cache.kv.head, cache.kv.head + n_tokens); } // find how many KV cells are currently in use @@ -4152,7 +4042,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { cache.rs.seq_tails.resize(cache.rs.size); } for (auto & buf : cache.bufs) { - ggml_backend_buffer_clear(buf, 0); + ggml_backend_buffer_clear(buf.get(), 0); } } @@ -4465,6 +4355,53 @@ static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) return cparams.flash_attn ? 256u : 32u; } +// saves the kv_cache state for future recovery. +// used to rollback llama_kv_cache_find_slot changes. +struct llama_kv_slot_restorer { + struct llama_kv_cache_state { + uint32_t head = 0; + uint32_t n = 0; + } old_state; + + // for non-recurrent models only + // list of slots to restore + std::vector> slot_boundaries; + + bool do_restore = false; + + explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) { + old_state.head = cache.kv.head; + old_state.n = cache.kv.n; + } + + // saves a slot information for future restoration + void save(const struct llama_kv_cache_slot_info & slot) { + if (slot) { + do_restore = true; + if (slot.boundaries.first != slot.boundaries.second) { + slot_boundaries.push_back(slot.boundaries); + } + } + } + + // must be explicitly called to restore the kv_cache state + // and rollback changes from all llama_kv_cache_find_slot calls + void restore(struct llama_kv_cache & cache) { + if (do_restore) { + cache.kv.head = old_state.head; + cache.kv.n = old_state.n; + + if (cache.rs.size > 0) { // recurrent models like Mamba or RWKV can't have a state partially erased + llama_kv_cache_seq_rm(cache, -1, -1, -1); + } else { + for (auto & slot : slot_boundaries) { + llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second); + } + } + } + } +}; + // // model loading and saving // @@ -4680,8 +4617,8 @@ struct llama_model_loader { int n_tensors = 0; int n_created = 0; - int64_t n_elements = 0; - size_t n_bytes = 0; + uint64_t n_elements = 0; + size_t n_bytes = 0; bool use_mmap = false; bool check_tensors; @@ -4699,21 +4636,38 @@ struct llama_model_loader { ggml_tensor * tensor; - llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { - const int tensor_idx = gguf_find_tensor(gguf_ctx, name); - offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); + llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { + const int tensor_idx = gguf_find_tensor(gguf_ctx, ggml_get_name(tensor)); + if (tensor_idx < 0) { + throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor))); + } + offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) { - throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name)); + throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor))); } } }; - std::vector weights; + // custom comparator to sort weights more nicely by layer + struct weight_name_comparer { + bool operator()(const std::string & a, const std::string & b) const { + int a_layer = -1; + int b_layer = -1; + sscanf(a.c_str(), "blk.%d.", &a_layer); + sscanf(b.c_str(), "blk.%d.", &b_layer); + if (a_layer != b_layer) { + return a_layer < b_layer; + } + return a < b; + } + }; + + std::map weights_map; std::unordered_map kv_overrides; - struct gguf_context * meta = NULL; - std::vector contexts; + gguf_context_ptr meta; + std::vector contexts; std::string arch_name; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); @@ -4736,7 +4690,7 @@ struct llama_model_loader { /*.ctx = */ &ctx, }; - meta = gguf_init_from_file(fname.c_str(), params); + meta.reset(gguf_init_from_file(fname.c_str(), params)); if (!meta) { throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str())); } @@ -4751,7 +4705,14 @@ struct llama_model_loader { // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), 0, cur->name, meta, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta.get(), cur)); } uint16_t n_split = 0; get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); @@ -4781,7 +4742,7 @@ struct llama_model_loader { /*.no_alloc = */ true, /*.ctx = */ &ctx, }; - struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params); + gguf_context_ptr ctx_gguf { gguf_init_from_file(split_path, split_params) }; if (!ctx_gguf) { throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path)); } @@ -4791,17 +4752,22 @@ struct llama_model_loader { // Save tensors data offset info of the shard. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur)); } - - gguf_free(ctx_gguf); } get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors); // sanity check { - const int n_tensors_loaded = (int) weights.size(); + const int n_tensors_loaded = (int) weights_map.size(); if (n_tensors != n_tensors_loaded) { throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); } @@ -4810,23 +4776,10 @@ struct llama_model_loader { LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1); } - n_kv = gguf_get_n_kv(meta); - n_tensors = weights.size(); + n_kv = gguf_get_n_kv(meta.get()); + n_tensors = weights_map.size(); - fver = (enum llama_fver) gguf_get_version(meta); - - std::set tensor_names; - for (auto & w : weights) { - n_elements += ggml_nelements(w.tensor); - n_bytes += ggml_nbytes(w.tensor); - // make sure there is no duplicated tensor names - const std::string name(w.tensor->name); - auto found = tensor_names.find(name); - if (found != tensor_names.end()) { - throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name)); - } - tensor_names.insert(name); - } + fver = (enum llama_fver) gguf_get_version(meta.get()); LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver)); @@ -4839,8 +4792,10 @@ struct llama_model_loader { uint32_t n_type_max = 0; enum ggml_type type_max = GGML_TYPE_F32; - for (int i = 0; i < n_tensors; i++) { - const ggml_tensor * tensor = weights.at(i).tensor; + for (const auto & it : weights_map) { + const llama_tensor_weight & w = it.second; + const ggml_tensor * tensor = w.tensor; + enum ggml_type type = tensor->type; n_type[type]++; @@ -4851,8 +4806,8 @@ struct llama_model_loader { } if (trace > 0) { - const uint16_t sid = weights.at(i).idx; - LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); + const uint16_t sid = w.idx; + LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); } } @@ -4895,23 +4850,23 @@ struct llama_model_loader { ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED); { - const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV + const int kid = gguf_find_key(meta.get(), "general.file_type"); // TODO: use LLM_KV if (kid >= 0) { - ftype = (llama_ftype) gguf_get_val_u32(meta, kid); + ftype = (llama_ftype) gguf_get_val_u32(meta.get(), kid); } } LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(meta, i); - const enum gguf_type type = gguf_get_kv_type(meta, i); + const char * name = gguf_get_key(meta.get(), i); + const enum gguf_type type = gguf_get_kv_type(meta.get(), i); const std::string type_name = type == GGUF_TYPE_ARRAY - ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i)) + ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i)) : gguf_type_name(type); - std::string value = gguf_kv_to_str(meta, i); + std::string value = gguf_kv_to_str(meta.get(), i); const size_t MAX_VALUE_LEN = 40; if (value.size() > MAX_VALUE_LEN) { value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); @@ -4940,19 +4895,10 @@ struct llama_model_loader { this->check_tensors = check_tensors; } - ~llama_model_loader() { - if (meta) { - gguf_free(meta); - } - for (auto * ctx : contexts) { - ggml_free(ctx); - } - } - template typename std::enable_if::value, bool>::type get_arr_n(const std::string & key, T & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); if (kid < 0) { if (required) { @@ -4962,7 +4908,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); result = arr_info.length; @@ -4977,9 +4923,9 @@ struct llama_model_loader { template bool get_arr(const std::string & key, std::vector & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); - if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { + if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) { if (required) { throw std::runtime_error(format("array key not found in model: %s", key.c_str())); } @@ -4987,7 +4933,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; @@ -5006,9 +4952,9 @@ struct llama_model_loader { template bool get_arr(const std::string & key, std::array & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); - if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { + if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) { if (required) { throw std::runtime_error(format("array key not found in model: %s", key.c_str())); } @@ -5016,7 +4962,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; @@ -5048,7 +4994,7 @@ struct llama_model_loader { const struct llama_model_kv_override * override = it != kv_overrides.end() ? &it->second : nullptr; - const bool found = GGUFMeta::GKV::set(meta, key, result, override); + const bool found = GGUFMeta::GKV::set(meta.get(), key, result, override); if (required && !found) { throw std::runtime_error(format("key not found in model: %s", key.c_str())); @@ -5065,7 +5011,7 @@ struct llama_model_loader { // get array of n <= N_MAX elements, or a single element repeated n times template bool get_key_or_arr(const std::string & key, std::array & result, uint32_t n, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); if (kid < 0) { if (required) { @@ -5078,9 +5024,9 @@ struct llama_model_loader { throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str())); } - if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) { + if (gguf_get_kv_type(meta.get(), kid) == GGUF_TYPE_ARRAY) { struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); if (n != arr_info.length) { throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length)); @@ -5116,21 +5062,13 @@ struct llama_model_loader { return llm_kv.arch; } - const char * get_tensor_name(int i) const { - return weights.at(i).tensor->name; - } - const llama_tensor_weight * get_weight(const char * name) const { - for (const auto & weight : weights) { - if (strcmp(name, weight.tensor->name) == 0) { - return &weight; - } + auto pos = weights_map.find(name); + if (pos != weights_map.end()) { + return &pos->second; } - return nullptr; - } - const llama_tensor_weight * get_weight(int i) const { - return get_weight(get_tensor_name(i)); + return nullptr; } const llama_tensor_weight & require_weight(const char * name) const { @@ -5149,31 +5087,14 @@ struct llama_model_loader { return weight->tensor; } - struct ggml_tensor * require_tensor_meta(const char * name) const { - struct ggml_tensor * tensor = get_tensor_meta(name); + struct ggml_tensor * require_tensor_meta(const std::string & name) const { + struct ggml_tensor * tensor = get_tensor_meta(name.c_str()); if (!tensor) { - throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name)); + throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } return tensor; } - struct ggml_tensor * get_tensor_meta(int i) const { - return get_tensor_meta(get_tensor_name(i)); - } - - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) { - struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); - ggml_set_name(tensor, ggml_get_name(cur)); - - if (duplicated) { - size_data += ggml_nbytes(cur); - } else { - n_created++; - } - - return tensor; - } - const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const { const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); @@ -5214,7 +5135,19 @@ struct llama_model_loader { return NULL; } - return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED); + bool duplicated = flags & TENSOR_DUPLICATED; + + struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); + ggml_set_name(tensor, ggml_get_name(cur)); + + if (duplicated) { + size_data += ggml_nbytes(cur); + } else { + n_created++; + } + + return tensor; + } struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required = true) { @@ -5268,8 +5201,8 @@ struct llama_model_loader { } // compute the total size of all tensors for progress reporting - for (auto & w : weights) { - size_data += ggml_nbytes(w.tensor); + for (const auto & it : weights_map) { + size_data += ggml_nbytes(it.second.tensor); } } @@ -5281,19 +5214,12 @@ struct llama_model_loader { *last = 0; *addr = mapping->addr; for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { - try { - const auto * weight = get_weight(ggml_get_name(tensor)); - if (!weight) { - continue; - } - if (weight->idx != idx) { - continue; - } - *first = std::min(*first, weight->offs); - *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); - } catch(...) { - // the tensor is not in the model + const auto * weight = get_weight(ggml_get_name(tensor)); + if (!weight || weight->idx != idx) { + continue; } + *first = std::min(*first, weight->offs); + *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); } } @@ -5346,7 +5272,7 @@ struct llama_model_loader { std::vector events; std::vector host_ptrs; size_t buffer_idx = 0; // buffer to use for async loads - ggml_backend_t upload_backend = [&](const char * fn) -> ggml_backend_t { + ggml_backend_t upload_backend = [&](const char * func) -> ggml_backend_t { if (use_mmap || check_tensors) { return nullptr; } @@ -5354,20 +5280,20 @@ struct llama_model_loader { // First determine if the backend supports the necessary features for async uploads. auto * buf = bufs.count(0) ? bufs.at(0) : nullptr; if (!buf) { - LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", fn); + LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", func); return nullptr; } auto * buft = ggml_backend_buffer_get_type(buf); auto * dev = ggml_backend_buft_get_device(buft); if (!dev) { - LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", func, ggml_backend_buft_name(buft)); return nullptr; } if (buft != ggml_backend_dev_buffer_type(dev)) { - LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", func, ggml_backend_buft_name(buft), ggml_backend_dev_name(dev)); return nullptr; } @@ -5375,14 +5301,14 @@ struct llama_model_loader { ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); if (!props.caps.async || !props.caps.host_buffer || !props.caps.events) { - LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", fn, + LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", func, ggml_backend_dev_name(dev)); return nullptr; } auto * host_buft = ggml_backend_dev_host_buffer_type(dev); if (!host_buft) { - LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5391,7 +5317,7 @@ struct llama_model_loader { for (size_t idx = 0; idx < n_buffers; ++idx) { auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size); if (!buf) { - LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5401,7 +5327,7 @@ struct llama_model_loader { auto * event = ggml_backend_event_new(dev); if (!event) { - LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5411,7 +5337,7 @@ struct llama_model_loader { ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); if (!backend) { - LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5470,7 +5396,6 @@ struct llama_model_loader { ggml_backend_tensor_set(cur, data, 0, n_size); } } else { - GGML_ASSERT(weight->idx < files.size()); const auto & file = files.at(weight->idx); if (ggml_backend_buffer_is_host(cur->buffer)) { file->seek(weight->offs, SEEK_SET); @@ -5561,6 +5486,52 @@ struct llama_model_loader { } }; +// temporary allocate memory for the input batch if needed +static const llama_seq_id batch_default_seq_id = 0; +struct llama_batch_allocr { + std::array seq_id_0 = {batch_default_seq_id}; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector logits; + struct llama_batch batch; + // optionally fulfill the batch returned by llama_batch_get_one + llama_batch_allocr(llama_context & ctx, struct llama_batch in_batch) { + batch = in_batch; + GGML_ASSERT(batch.n_tokens > 0); + if (!batch.pos) { + // determine the last position in KV cache + llama_pos last_pos = llama_kv_cache_seq_pos_max(ctx.cache, batch_default_seq_id); + last_pos++; // next position + pos.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + pos[i] = i+last_pos; + } + batch.pos = pos.data(); + } + if (!batch.n_seq_id) { + n_seq_id.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + n_seq_id[i] = seq_id_0.size(); + } + batch.n_seq_id = n_seq_id.data(); + } + if (!batch.seq_id) { + seq_id.resize(batch.n_tokens + 1); + seq_id[batch.n_tokens] = NULL; + for (int32_t i = 0; i < batch.n_tokens; i++) { + seq_id[i] = seq_id_0.data(); + } + batch.seq_id = seq_id.data(); + } + if (!batch.logits) { + logits.resize(batch.n_tokens); + logits[logits.size() - 1] = true; + batch.logits = logits.data(); + } + } +}; + template<> bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { uint32_t tmp; @@ -5656,6 +5627,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_1B: return "1B"; case MODEL_1_3B: return "1.3B"; case MODEL_1_4B: return "1.4B"; + case MODEL_1_5B: return "1.5B"; case MODEL_1_6B: return "1.6B"; case MODEL_2B: return "2B"; case MODEL_2_8B: return "2.8B"; @@ -5709,6 +5681,11 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ } } +static void llm_load_stats(llama_model_loader & ml, llama_model & model) { + model.n_elements = ml.n_elements; + model.n_bytes = ml.n_bytes; +} + static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); if (model.arch == LLM_ARCH_UNKNOWN) { @@ -5720,7 +5697,7 @@ static void llm_load_hparams( llama_model_loader & ml, llama_model & model) { auto & hparams = model.hparams; - const gguf_context * ctx = ml.meta; + const gguf_context * ctx = ml.meta.get(); // get metadata as string for (int i = 0; i < gguf_get_n_kv(ctx); i++) { @@ -6027,6 +6004,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break; + case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break; case 32: model.type = e_model::MODEL_7B; break; case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break; case 80: model.type = e_model::MODEL_70B; break; @@ -6254,6 +6232,17 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_OLMO_1124: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 16: model.type = e_model::MODEL_1B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_13B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_OLMOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -6503,7 +6492,7 @@ static void llm_load_vocab( llama_model & model) { auto & vocab = model.vocab; - struct gguf_context * ctx = ml.meta; + struct gguf_context * ctx = ml.meta.get(); const auto kv = LLM_KV(model.arch); @@ -6935,8 +6924,8 @@ static void llm_load_vocab( ) { vocab.special_eot_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6949,8 +6938,8 @@ static void llm_load_vocab( ) { vocab.special_eom_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6966,8 +6955,8 @@ static void llm_load_vocab( ) { vocab.special_fim_pre_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6983,8 +6972,8 @@ static void llm_load_vocab( ) { vocab.special_fim_suf_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -7000,8 +6989,8 @@ static void llm_load_vocab( ) { vocab.special_fim_mid_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -7016,8 +7005,8 @@ static void llm_load_vocab( ) { vocab.special_fim_pad_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -7033,8 +7022,8 @@ static void llm_load_vocab( ) { vocab.special_fim_rep_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -7047,8 +7036,8 @@ static void llm_load_vocab( ) { vocab.special_fim_sep_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -7059,6 +7048,19 @@ static void llm_load_vocab( // this is currently determined based on the token text, which is obviously not ideal // ref: https://github.com/ggerganov/llama.cpp/issues/9606 vocab.special_eog_ids.clear(); + + if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_pad_id); + } + + if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_rep_id); + } + + if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_sep_id); + } + for (const auto & t : vocab.token_to_id) { if (false || t.first == "<|eot_id|>" @@ -7071,13 +7073,20 @@ static void llm_load_vocab( ) { vocab.special_eog_ids.insert(t.second); if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } + } else { + // token is control, but not marked as EOG -> print a debug log + if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) { + LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n", + __func__, t.second, t.first.c_str()); + } } } + // sanity checks if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) { vocab.special_eog_ids.insert(vocab.special_eos_id); LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__); @@ -7329,6 +7338,360 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { } } +enum llm_tensor_layer { + LLM_TENSOR_LAYER_INPUT, + LLM_TENSOR_LAYER_REPEATING, + LLM_TENSOR_LAYER_OUTPUT, +}; + +struct llm_tensor_info { + llm_tensor_layer layer; + ggml_op op; +}; + +static const std::map llm_tensor_info_mapping = { + {LLM_TENSOR_TOKEN_EMBD, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_POS_EMBD, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_TOKEN_EMBD_NORM, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_TOKEN_TYPES, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_ROPE_FREQS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ROPE_FACTORS_LONG, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ROPE_FACTORS_SHORT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_QKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_A_MQA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_QKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_A_MQA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_INP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_INP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_IN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_DT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_W1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_W2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_DECAY_W1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_DECAY_W2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_KEY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_VALUE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_RECEPTANCE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_OUTPUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_KEY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_VALUE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_ACT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}}, + {LLM_TENSOR_SSM_CONV1D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}}, + {LLM_TENSOR_SSM_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}}, + {LLM_TENSOR_SSM_DT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_SSM_B_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_SSM_C_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_SSM_D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LERP_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_CHANNEL_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_CHANNEL_MIX_LERP_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LERP_W, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_G, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_DECAY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_FIRST, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV6}}, + {LLM_TENSOR_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_NORM_2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_NORM_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_LAYER_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_Q_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_KV_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_SUB_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_SUB_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_CROSS_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_ENC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + // this tensor is loaded for T5, but never used + {LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}}, +}; + +// checks if the weight tensor can be used with the specified buffer type and device +static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) { + GGML_ASSERT(w != nullptr); + + if (op == GGML_OP_NONE) { + return true; + } + + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead()*8, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context_ptr ctx_ptr { ggml_init(params) }; + if (!ctx_ptr) { + throw std::runtime_error(format("failed to create ggml context")); + } + ggml_context * ctx = ctx_ptr.get(); + + ggml_tensor * op_tensor = nullptr; + + switch (op) { + case GGML_OP_GET_ROWS: + { + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_get_rows(ctx, w, b); + } break; + case GGML_OP_MUL_MAT: + { + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); + op_tensor = ggml_mul_mat(ctx, w, b); + } break; + case GGML_OP_MUL_MAT_ID: + { + int n_expert_used = hparams.n_expert_used; + ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); + ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); + op_tensor = ggml_mul_mat_id(ctx, w, b, ids); + } break; + case GGML_OP_ADD: + { + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); + op_tensor = ggml_add(ctx, a, w); + } break; + case GGML_OP_MUL: + { + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); + op_tensor = ggml_mul(ctx, a, w); + } break; + case GGML_OP_DIV: + { + ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]); + op_tensor = ggml_div(ctx, a, w); + } break; + case GGML_OP_ROPE: + { + int n_embd_head = hparams.n_embd_head_v; + int n_head = hparams.n_head(); + ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512); + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_rope_ext( + ctx, a, b, w, + 0, 0, 0, 0, 0, + 0, 0, 0, 0 + ); + + } break; + case GGML_OP_SSM_CONV: + { + // FIXME + ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789); + op_tensor = ggml_ssm_conv(ctx, conv_x, w); + } break; + case GGML_OP_SSM_SCAN: + { + // FIXME + const int64_t d_state = w->ne[0]; + const int64_t d_inner = w->ne[1]; + const int64_t n_seq_tokens = 512; + const int64_t n_seqs = 1; + ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs); + ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C); + } break; + case GGML_OP_RWKV_WKV6: + { + // FIXME + const int64_t S = 123; + const int64_t H = 123; + const int64_t n_tokens = 123; + const int64_t n_seqs = 123; + ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens); + ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * tf = w; + ggml_tensor * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); + op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); + } break; + default: + GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); + } + + // create a temporary dummy buffer for the weight so that supports_op can check the buffer type + GGML_ASSERT(w->buffer == nullptr); + w->buffer = ggml_backend_buft_alloc_buffer(buft, 0); + bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); + ggml_backend_buffer_free(w->buffer); + w->buffer = nullptr; + + return op_supported; +} + +// find the first buffer type in the list that can use the tensor +static ggml_backend_buffer_type_t select_weight_buft(const llama_model & model, ggml_tensor * tensor, ggml_op op, const llama_model::buft_list_t & buft_list) { + GGML_ASSERT(!buft_list.empty()); + for (const auto & cur : buft_list) { + ggml_backend_dev_t cur_dev = cur.first; + ggml_backend_buffer_type_t cur_buft = cur.second; + if (weight_buft_supported(model.hparams, tensor, op, cur_buft, cur_dev)) { + return cur_buft; + } + } + return nullptr; +} + +// CPU: ACCEL -> CPU extra -> GPU host -> CPU +static llama_model::buft_list_t make_cpu_buft_list(llama_model & model) { + llama_model::buft_list_t buft_list; + + // add ACCEL buffer types + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) { + auto * buft = ggml_backend_dev_buffer_type(dev); + // skip + if (buft != ggml_backend_cpu_buffer_type()) { + buft_list.emplace_back(dev, buft); + } + } + } + + // add extra buffer types + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev); + auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t) + ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts"); + if (ggml_backend_dev_get_extra_bufts_fn) { + ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev); + while (extra_bufts && *extra_bufts) { + buft_list.emplace_back(cpu_dev, *extra_bufts); + ++extra_bufts; + } + } + + // add a host buffer type + // storing the tensors in a host buffer is useful when the processing of large batches + // is offloaded to a GPU device, since it reduces the time spent on data transfers + // generally, this will be done using the first device in the list + // a better approach would be to handle this on a weight-by-weight basis using the offload_op + // function of the device to determine if it would benefit from being stored in a host buffer + for (auto * dev : model.devices) { + ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev); + if (buft) { + buft_list.emplace_back(dev, buft); + break; + } + } + + // add the CPU buffer type + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev)); + } + } + + return buft_list; +} + +// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU +static llama_model::buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) { + llama_model::buft_list_t buft_list; + + // add the device split buffer type if requested and available + if (split_mode == LLAMA_SPLIT_MODE_ROW) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); + auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t) + ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type"); + if (ggml_backend_split_buffer_type_fn) { + size_t dev_index = [&]() { + auto * reg = ggml_backend_dev_backend_reg(dev); + for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) { + if (ggml_backend_reg_dev_get(reg, i) == dev) { + return i; + } + } + throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev))); + }(); + auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split); + if (buft != nullptr) { + buft_list.emplace_back(dev, buft); + } + } + } + + // add the device default buffer type + buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev)); + + return buft_list; +} + // Returns false if cancelled by progress_callback static bool llm_load_tensors( llama_model_loader & ml, @@ -7342,128 +7705,98 @@ static bool llm_load_tensors( void * progress_callback_user_data) { auto & hparams = model.hparams; - // check if the value of main_gpu is valid - if (llama_get_device_count(model) > 0 && - split_mode != LLAMA_SPLIT_MODE_LAYER && - (main_gpu < 0 || main_gpu >= llama_get_device_count(model))) { - throw std::runtime_error(format("invalid value for main_gpu: %d (available devices: %d)", main_gpu, llama_get_device_count(model))); - } - model.split_mode = split_mode; model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; const int n_layer = hparams.n_layer; - const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0); bool use_mmap_buffer = true; - // there is very little benefit to offloading the input layer, so always keep it on the CPU - model.buft_input = llama_default_buffer_type_cpu(model, true); - //model.buft_input = llama_default_buffer_type_offload(main_gpu); - - model.buft_layer.resize(n_layer); - - // assign cpu layers - for (int i = 0; i < i_gpu_start; ++i) { - model.buft_layer[i] = llama_default_buffer_type_cpu(model, true); + // build a list of buffer types for the CPU and GPU devices + model.cpu_buft_list = make_cpu_buft_list(model); + for (auto * dev : model.devices) { + llama_model::buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split); + // add CPU buffer types as a fallback + buft_list.insert(buft_list.end(), model.cpu_buft_list.begin(), model.cpu_buft_list.end()); + model.gpu_buft_list.emplace(dev, std::move(buft_list)); } - if (split_mode == LLAMA_SPLIT_MODE_LAYER) { - // calculate the split points - int device_count = llama_get_device_count(model); - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); - std::vector splits(device_count); - if (all_zero) { - // default split, by free memory - for (int i = 0; i < device_count; ++i) { - splits[i] = llama_get_device_memory(model, i); - } - } else { - std::copy(tensor_split, tensor_split + device_count, splits.begin()); - } - - // sum and normalize the splits to get the split points - float split_sum = 0.0f; + // calculate the split points + int device_count = llama_get_device_count(model); + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); + std::vector splits(device_count); + if (all_zero) { + // default split, by free memory for (int i = 0; i < device_count; ++i) { - split_sum += splits[i]; - splits[i] = split_sum; - } - for (int i = 0; i < device_count; ++i) { - splits[i] /= split_sum; - } - - // assign the repeating layers to the devices according to the splits - int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); - for (int i = i_gpu_start; i < n_layer; ++i) { - int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); - model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu); - } - // assign the output layer - if (n_gpu_layers > n_layer) { - int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); - model.buft_output = llama_default_buffer_type_offload(model, layer_gpu); - } else { - model.buft_output = llama_default_buffer_type_cpu(model, true); + ggml_backend_dev_t dev = model.devices[i]; + size_t total; + size_t free; + ggml_backend_dev_memory(dev, &free, &total); + splits[i] = free; } } else { - ggml_backend_buffer_type_t split_buft; - if (split_mode == LLAMA_SPLIT_MODE_ROW) { - split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split); - } else { - // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported - split_buft = llama_default_buffer_type_offload(model, main_gpu); - } - // assign the repeating layers - for (int i = i_gpu_start; i < n_layer; ++i) { - model.buft_layer[i] = { - split_buft, - llama_default_buffer_type_offload(model, main_gpu) - }; - } - // assign the output layer - if (n_gpu_layers > n_layer) { - model.buft_output = { - split_buft, - llama_default_buffer_type_offload(model, main_gpu) - }; - } else { - model.buft_output = llama_default_buffer_type_cpu(model, true); - } + std::copy(tensor_split, tensor_split + device_count, splits.begin()); } - // count used buffer types - std::map buft_layer_count; - buft_layer_count[model.buft_input.buft]++; - buft_layer_count[model.buft_input.buft_matrix]++; - buft_layer_count[model.buft_output.buft]++; - buft_layer_count[model.buft_output.buft_matrix]++; - for (int i = 0; i < n_layer; ++i) { - buft_layer_count[model.buft_layer[i].buft]++; - buft_layer_count[model.buft_layer[i].buft_matrix]++; + // sum and normalize the splits to get the split points + float split_sum = 0.0f; + for (int i = 0; i < device_count; ++i) { + split_sum += splits[i]; + splits[i] = split_sum; + } + for (int i = 0; i < device_count; ++i) { + splits[i] /= split_sum; } - // create one context per buffer type - size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output + ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0); + const int act_gpu_layers = model.devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1); + auto get_layer_buft_list = [&](int il) -> llama_model::layer_dev { + if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) { + return {cpu_dev, &model.cpu_buft_list}; + } + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(il - i_gpu_start)/act_gpu_layers) - splits.begin(); + auto * dev = model.devices.at(layer_gpu); + return {dev, &model.gpu_buft_list.at(dev)}; + }; - // for moe merged tensors - ctx_size += ggml_tensor_overhead()*n_layer*3; + // assign the input layer + // there is very little benefit to offloading the input layer, so always keep it on the CPU + model.dev_input = { cpu_dev, &model.cpu_buft_list }; + + // assign the repeating layers to the devices according to the splits + model.dev_layer.resize(n_layer); + for (int il = 0; il < n_layer; ++il) { + model.dev_layer[il] = get_layer_buft_list(il); + } + // assign the output layer + model.dev_output = get_layer_buft_list(n_layer); + + // one ggml context per buffer type + int max_n_tensors = ml.n_tensors; + max_n_tensors += 1; // duplicated output tensor + max_n_tensors += n_layer*2; // duplicated rope freq tensors + const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors; std::map ctx_map; - for (auto & it : buft_layer_count) { - struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - throw std::runtime_error(format("failed to create context")); + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } + ctx_map[buft] = ctx; + model.ctxs.emplace_back(ctx); + return ctx; } - ctx_map[it.first] = ctx; - model.ctxs.push_back(ctx); - } - - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0); + return it->second; + }; // create tensors for the weights { @@ -7488,15 +7821,107 @@ static bool llm_load_tensors( throw std::runtime_error("model has expert layers but no expert layers are used"); } - ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); - ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); - ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); + int n_moved_tensors = 0; + ggml_tensor * first_moved_tensor = nullptr; + ggml_backend_buffer_type_t first_moved_from_buft = nullptr; + ggml_backend_buffer_type_t first_moved_to_buft = nullptr; - auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; - auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; + auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags) -> ggml_tensor * { + ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str()); + + if (!t_meta) { + if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) { + return nullptr; + } + throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str())); + } + + // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops + // the tensor is duplicated + // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor + llm_tensor tn_tensor = tn.tensor; + if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & llama_model_loader::TENSOR_DUPLICATED) { + tn_tensor = LLM_TENSOR_OUTPUT; + } + + auto it = llm_tensor_info_mapping.find(tn_tensor); + if (it == llm_tensor_info_mapping.end()) { + throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str())); + } + const auto & info = it->second; + + // tensors with "bias" suffix are always used with GGML_OP_ADD + ggml_op op; + bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0; + if (bias) { + op = GGML_OP_ADD; + } else { + op = info.op; + } + + // sanity checks + if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) { + if (tn.bid != -1) { + GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str()); + } + } else { + if (tn.bid == -1) { + GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str()); + } + } + + // select the buffer type for this tensor + llama_model::buft_list_t * buft_list; + switch (info.layer) { + case LLM_TENSOR_LAYER_INPUT: + buft_list = model.dev_input.buft_list; + break; + case LLM_TENSOR_LAYER_OUTPUT: + buft_list = model.dev_output.buft_list; + break; + case LLM_TENSOR_LAYER_REPEATING: + buft_list = model.dev_layer.at(tn.bid).buft_list; + break; + default: + GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str()); + } + + ggml_backend_buffer_type_t buft = select_weight_buft(model, t_meta, op, *buft_list); + if (!buft) { + throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str())); + } + + // avoid using a host buffer when using mmap + auto * buft_dev = ggml_backend_buft_get_device(buft); + if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + buft = ggml_backend_dev_buffer_type(cpu_dev); + } + + if (buft != buft_list->front().second) { + n_moved_tensors++; + if (!first_moved_tensor) { + first_moved_tensor = t_meta; + first_moved_from_buft = buft_list->front().second; + first_moved_to_buft = buft; + } + } + + ggml_context * ctx = ctx_for_buft(buft); + + // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one + if (flags & llama_model_loader::TENSOR_DUPLICATED) { + ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str()); + if (t) { + return t; + } + } + return ml.create_tensor(ctx, tn, ne, flags); + }; model.layers.resize(n_layer); + // TODO: move to a separate function const auto tn = LLM_TN(model.arch); switch (model.arch) { case LLM_ARCH_LLAMA: @@ -7505,82 +7930,51 @@ static bool llm_load_tensors( case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); if (n_expert == 0) { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional MLP bias - layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } else { - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); - if (layer.ffn_gate_exps) { - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } else { - // merge split expert into a single tensor for compatibility with older models - // requires disabling mmap - use_mmap_buffer = false; - - ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type; - ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type; - ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type; - - layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert); - layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert); - layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert); - - ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str()); - - for (uint32_t x = 0; x < n_expert; ++x) { - // the individual experts are loaded into a view of the merged tensor - ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x); - } - } + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } } break; @@ -7591,45 +7985,40 @@ static bool llm_load_tensors( const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); - layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); - layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); - layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0); - layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}); - layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}); + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_GROK: @@ -7638,904 +8027,782 @@ static bool llm_load_tensors( throw std::runtime_error("Grok model cannot have zero experts"); } - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); - if (layer.ffn_gate_exps) { - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } else { - // merge split expert into a single tensor for compatibility with older models - // requires disabling mmap - use_mmap_buffer = false; - - ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type; - ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type; - ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type; - - layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert); - layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert); - layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert); - - ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str()); - - for (uint32_t x = 0; x < n_expert; ++x) { - // the individual experts are loaded into a view of the merged tensor - ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x); - } - } - - layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); } } break; case LLM_ARCH_DBRX: - { - if (n_expert == 0) { - throw std::runtime_error("DBRX model cannot have zero experts"); - } - - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - - // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + if (n_expert == 0) { + throw std::runtime_error("DBRX model cannot have zero experts"); + } - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); - auto & layer = model.layers[i]; + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + for (int i = 0; i < n_layer; ++i) { + auto & layer = model.layers[i]; - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } - } break; + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + } + } break; case LLM_ARCH_BAICHUAN: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_FALCON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_STARCODER: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { // needs to be on GPU - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); if (model.arch == LLM_ARCH_BERT) { - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); - model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_out = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); } - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; if (model.arch == LLM_ARCH_BERT) { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); } else { - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); } - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); - layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); + layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); if (model.arch == LLM_ARCH_BERT) { - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); } else { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); } - layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); - layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); + layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; case LLM_ARCH_JINA_BERT_V2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings + model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); // token_type_embeddings - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); //LayerNorm bias - model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; // JinaBertLayer - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); //output_dens - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm - layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm + layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); - layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); + layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; case LLM_ARCH_BLOOM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_MPT: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + if (!model.output) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_STABLELM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors, present in Stable LM 2 1.6B - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_QWEN: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}, 0); } } break; case LLM_ARCH_QWEN2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_QWEN2MOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0 for QWEN2MOE"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE"); + } // MoE branch const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); // Shared expert branch const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; - layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}); + layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); } } break; case LLM_ARCH_PHI2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.wqkv == nullptr) { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); } - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_PHI3: { const int64_t n_embd_head = n_embd / n_head; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0); - layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_PLAMO: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GPT2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_CODESHELL: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_ORION: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_INTERNLM2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GEMMA: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); } } break; case LLM_ARCH_GEMMA2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); - layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); } } break; case LLM_ARCH_STARCODER2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional bias tensors - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}, 0); } } break; case LLM_ARCH_MAMBA: @@ -8546,46 +8813,43 @@ static bool llm_load_tensors( const int64_t dt_rank = hparams.ssm_dt_rank; // only an expansion factor of 2 is supported for now - GGML_ASSERT(2 * n_embd == d_inner); + if (2 * n_embd != d_inner) { + throw std::runtime_error("only an expansion factor of 2 is supported for now"); + } - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed, duplicated to allow offloading - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed, duplicated to allow offloading + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; // norm - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}); + layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0); - layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}); - layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}); + layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0); + layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0); - layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}); + layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0); - layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}); - layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}); + layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0); + layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0); // no "weight" suffix for these - layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}); - layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner}); + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0); + layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0); // out_proj - layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); + layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0); } } break; case LLM_ARCH_JAMBA: @@ -8598,16 +8862,16 @@ static bool llm_load_tensors( // only an expansion factor of 2 is supported for now GGML_ASSERT(2 * n_embd == d_inner); - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed, duplicated to allow offloading if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -8615,37 +8879,34 @@ static bool llm_load_tensors( const int64_t n_head_kv = hparams.n_head_kv(i); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i); - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; // norm - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); if (n_head_kv == 0) { // Mamba layer - layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}); + layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0); - layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}); - layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}); + layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0); + layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0); - layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}); + layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0); - layer.ssm_dt_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT_NORM, "weight", i), {dt_rank}); + layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, "weight", i), {dt_rank}, 0); - layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}); - layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}); + layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0); + layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0); - layer.ssm_b_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_B_NORM, "weight", i), {d_state}); - layer.ssm_c_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_C_NORM, "weight", i), {d_state}); + layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, "weight", i), {d_state}, 0); + layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, "weight", i), {d_state}, 0); // no "weight" suffix for these - layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}); - layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner}); + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0); + layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0); // out_proj - layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); + layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0); layer.wq = nullptr; layer.wk = nullptr; @@ -8655,10 +8916,10 @@ static bool llm_load_tensors( } else { // Attention layers - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); layer.ssm_in = nullptr; layer.ssm_conv1d = nullptr; @@ -8674,24 +8935,24 @@ static bool llm_load_tensors( layer.ssm_out = nullptr; } - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_inp) { // MoE - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); layer.ffn_gate = nullptr; layer.ffn_down = nullptr; layer.ffn_up = nullptr; } else { // FFN (no MoE) - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); layer.ffn_gate_exps = nullptr; layer.ffn_down_exps = nullptr; @@ -8701,240 +8962,236 @@ static bool llm_load_tensors( } break; case LLM_ARCH_XVERSE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_COMMAND_R: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + // init output from the input tok embed + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); if (n_layer >= 64){ - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0); } - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } + } break; + case LLM_ARCH_OLMO_1124: + { + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = model.layers[i]; + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); } } break; case LLM_ARCH_OLMOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } // MoE branch - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } break; case LLM_ARCH_OPENELM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + // init output from the input tok embed + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); for (int i = 0; i < n_layer; ++i) { const int64_t n_head = hparams.n_head(i); const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head; const int64_t n_ff = hparams.n_ff(i); - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GPTNEOX: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_ARCTIC: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } break; case LLM_ARCH_DEEPSEEK2: @@ -8950,349 +9207,313 @@ static bool llm_load_tensors( const int64_t n_ff_exp = hparams.n_ff_exp; const int64_t n_expert_shared = hparams.n_expert_shared; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); if (!is_lite) { - layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); } - layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); if (!is_lite) { - layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); - layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0); } else { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); } - layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}); - layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}); + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); if (i < (int) hparams.n_layer_dense_lead) { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } else { - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } // MoE branch - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); // Shared expert branch - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); } } } break; case LLM_ARCH_BITNET: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_scale = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_T5: { const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}, 0); // this tensor seems to be unused in HF transformers implementation - layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_T5ENCODER: { const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_JAIS: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); - // Output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_CHATGLM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); } } break; case LLM_ARCH_NEMOTRON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional MLP bias - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_EXAONE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_RWKV6: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // Block 0, LN0 - model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); const int time_mix_extra_dim = hparams.time_mix_extra_dim; const int time_decay_extra_dim = hparams.time_decay_extra_dim; @@ -9301,90 +9522,88 @@ static bool llm_load_tensors( const int ffn_size = hparams.n_ff_arr[0]; for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, 0); - layer.time_mix_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}); - layer.time_mix_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}); + layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0); + layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0); - layer.time_mix_lerp_x = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_w = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_v = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}); + layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, 0); - layer.time_mix_first = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}); - layer.time_mix_decay = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}); - layer.time_mix_decay_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}); - layer.time_mix_decay_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}); - layer.time_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}); + layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0); + layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0); + layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0); + layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0); + layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0); - layer.time_mix_ln = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}); - layer.time_mix_ln_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}); - layer.time_mix_output = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}); + layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0); + layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0); + layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0); - layer.channel_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}); - layer.channel_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}); + layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0); + layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0); - layer.channel_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}); - layer.channel_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}); - layer.channel_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}); + layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0); + layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0); + layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0); } } break; case LLM_ARCH_CHAMELEON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; default: throw std::runtime_error("unknown architecture"); } + + if (n_moved_tensors > 0) { + LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n", + __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1, + ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft)); + } } ml.done_getting_tensors(); @@ -9397,27 +9616,33 @@ static bool llm_load_tensors( ctx_bufs.reserve(ctx_map.size()); // Ensure we have enough capacity for the maximum backend buffer we will potentially create - size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); + const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); model.bufs.reserve(n_max_backend_buffer); for (auto & it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx = it.second; + // skip contexts without tensors + if (ggml_get_first_tensor(ctx) == nullptr) { + continue; + } + llama_buf_map bufs; bufs.reserve(n_max_backend_buffer); - // check if this backend device supports buffer_from_host_ptr - // when using a host buffer as the CPU bakcend buffer, use the CPU device to prioritize using buffer_from_host_ptr over the host buffer - ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft == llama_default_buffer_type_cpu(model, true) ? ggml_backend_cpu_buffer_type() : buft); - bool buffer_from_host_ptr_supported = false; - if (dev) { - ggml_backend_dev_props props; - ggml_backend_dev_get_props(dev, &props); - buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; + // check if it is possible to use buffer_from_host_ptr with this buffer type + ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft); + if (!dev) { + // FIXME: workaround for CPU backend buft having a NULL device + dev = ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0); } + ggml_backend_dev_props props; + ggml_backend_dev_get_props(dev, &props); + bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; + bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev); - if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported) { + if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) { for (uint32_t idx = 0; idx < ml.files.size(); idx++) { // only the mmap region containing the tensors in the model is mapped to the backend buffer // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers @@ -9433,7 +9658,7 @@ static bool llm_load_tensors( if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - model.bufs.push_back(buf); + model.bufs.emplace_back(buf); bufs.emplace(idx, buf); } } @@ -9442,7 +9667,7 @@ static bool llm_load_tensors( if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - model.bufs.push_back(buf); + model.bufs.emplace_back(buf); if (use_mlock && ggml_backend_buffer_is_host(buf)) { model.mlock_bufs.emplace_back(new llama_mlock); auto & mlock_buf = model.mlock_bufs.back(); @@ -9460,7 +9685,7 @@ static bool llm_load_tensors( for (auto & buf : bufs) { // indicate that this buffer contains weights - // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight + // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); } @@ -9472,7 +9697,7 @@ static bool llm_load_tensors( LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); if (n_gpu_layers > (int) hparams.n_layer) { - LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__); + LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__); } const int max_backend_supported_layers = hparams.n_layer + 1; @@ -9481,14 +9706,14 @@ static bool llm_load_tensors( LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); } - // print memory requirements - for (ggml_backend_buffer_t buf : model.bufs) { - LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + // print memory requirements per buffer type + for (auto & buf : model.bufs) { + LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0); } // populate tensors_by_name - for (ggml_context * ctx : model.ctxs) { - for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { + for (auto & ctx : model.ctxs) { + for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) { model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); } } @@ -9536,6 +9761,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam throw std::runtime_error("error loading model vocabulary: " + std::string(e.what())); } + llm_load_stats(ml, model); llm_load_print_meta(ml, model); if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE && @@ -9548,23 +9774,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam return 0; } -#ifdef GGML_USE_KOMPUTE - if (params.n_gpu_layers > 0 && ( - !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) - || !( - model.ftype == LLAMA_FTYPE_ALL_F32 || - model.ftype == LLAMA_FTYPE_MOSTLY_F16 || - model.ftype == LLAMA_FTYPE_MOSTLY_BF16 || - model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || - model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 - ) - )) { - // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file - LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__); - params.n_gpu_layers = 0; - } -#endif - if (!llm_load_tensors( ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data @@ -10051,20 +10260,16 @@ static struct ggml_tensor * llm_build_kqv( cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias, hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_GEMMA2) { - ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); - } + ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); } else { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 || model.arch == LLM_ARCH_NEMOTRON || model.arch == LLM_ARCH_CHATGLM) { - // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs - // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 - ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - } + // note: this op tends to require high floating point range + // while for some models F16 is enough, for others it is not, so we default to F32 here + ggml_mul_mat_set_prec(kq, GGML_PREC_F32); if (model.arch == LLM_ARCH_GROK) { // need to do the following: @@ -10073,9 +10278,6 @@ static struct ggml_tensor * llm_build_kqv( // kq = 30 * tanh(kq / 30) // before the softmax below - //try from phi2 - //ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f)); kq = ggml_scale(ctx, kq, 30); } @@ -10484,7 +10686,7 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix( v = ggml_transpose(ctx, v); r = ggml_transpose(ctx, r); - struct ggml_tensor * wkv_output = ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); + struct ggml_tensor * wkv_output = ggml_rwkv_wkv6(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); cur = ggml_view_1d(ctx, wkv_output, n_embd * n_tokens, 0); *wkv_state = ggml_view_1d(ctx, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float)); @@ -10529,7 +10731,7 @@ struct llm_build_context { llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; - const llama_ubatch & batch; + const llama_ubatch & ubatch; const llama_kv_self_cache & kv_self; const llama_rs_self_cache & rs_self; @@ -10578,14 +10780,14 @@ struct llm_build_context { // TODO: consider making the entire interface noexcept llm_build_context( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, const llm_build_cb & cb, bool worst_case) : model (lctx.model), lctx (lctx), hparams (model.hparams), cparams (lctx.cparams), - batch (batch), + ubatch (ubatch), kv_self (lctx.cache.kv), rs_self (lctx.cache.rs), n_embd (hparams.n_embd), @@ -10606,9 +10808,9 @@ struct llm_build_context { beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), - n_seqs (batch.n_seqs), - n_seq_tokens (batch.n_seq_tokens), - n_tokens (batch.n_tokens), + n_seqs (ubatch.n_seqs), + n_seq_tokens (ubatch.n_seq_tokens), + n_tokens (ubatch.n_tokens), n_kv (worst_case ? kv_self.size : kv_self.n), n_rs (worst_case ? rs_self.size : rs_self.n), n_outputs (worst_case ? n_tokens : lctx.n_outputs), @@ -10650,10 +10852,8 @@ struct llm_build_context { } void free() { - if (ctx0) { - ggml_free(ctx0); - ctx0 = nullptr; - } + ggml_free(ctx0); + ctx0 = nullptr; } struct ggml_cgraph * build_k_shift() { @@ -10681,10 +10881,10 @@ struct llm_build_context { // dequantize to f32 -> RoPE -> quantize back tmp = ggml_cast(ctx0, k, GGML_TYPE_F32); cb(tmp, "K_f32", il); - for (auto * backend : lctx.backends) { + for (auto & backend : lctx.backends) { // Figure out which backend KV cache belongs to - if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft)) { - ggml_backend_sched_set_tensor_backend(lctx.sched, tmp, backend); + if (ggml_backend_supports_buft(backend.get(), ggml_backend_buffer_get_type(kv_self.k_l[il]->buffer))) { + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), tmp, backend.get()); break; } } @@ -10978,7 +11178,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11138,7 +11338,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr; @@ -11253,7 +11453,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11357,7 +11557,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11479,7 +11679,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // multiply by embedding_multiplier_scale of 78.38367176906169 inpL = ggml_scale(ctx0, inpL, 78.38367176906169f); @@ -11637,7 +11837,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11759,7 +11959,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11862,7 +12062,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11964,7 +12164,7 @@ struct llm_build_context { } // construct input embeddings (token, type, position) - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // token types are hardcoded to zero ("Sentence A") struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); @@ -12151,7 +12351,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -12253,7 +12453,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -12391,7 +12591,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12541,7 +12741,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12654,7 +12854,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12769,7 +12969,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12914,7 +13114,7 @@ struct llm_build_context { struct ggml_tensor * ffn_output; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13033,7 +13233,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13161,7 +13361,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13266,7 +13466,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13371,7 +13571,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13481,7 +13681,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13599,7 +13799,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13726,7 +13926,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -13870,7 +14070,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -14071,7 +14271,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -14179,7 +14379,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -14317,7 +14517,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14433,7 +14633,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); @@ -14445,7 +14645,7 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "attn_norm", il); - cur = llm_build_mamba(ctx0, lctx, batch, gf, cur, + cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur, state_copy, state_mask, NULL, NULL, NULL, rs_head, n_rs, cb, il); @@ -14489,7 +14689,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); @@ -14507,7 +14707,7 @@ struct llm_build_context { if (n_head_kv == 0) { // Mamba - cur = llm_build_mamba(ctx0, lctx, batch, gf, cur, + cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur, state_copy, state_mask, model.layers[il].ssm_dt_norm, model.layers[il].ssm_b_norm, model.layers[il].ssm_c_norm, rs_head, n_rs, cb, il); @@ -14610,7 +14810,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14767,7 +14967,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14878,6 +15078,130 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_olmo_1124() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = inpL; + + // self_attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Qcur, "Qcur_normed", il); + + Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Kcur, "Kcur_normed", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur_rope", il); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur_rope", il); + + cur = llm_build_kv(ctx0, lctx, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_post_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_post_norm", il); + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_ffn(ctx0, lctx, ffn_inp, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].ffn_post_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "ffn_post_norm", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + // based on the build_qwen2moe() function, changes: // * removed shared experts // * removed bias @@ -14895,7 +15219,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15014,7 +15338,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15141,7 +15465,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15286,7 +15610,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15427,7 +15751,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15642,7 +15966,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15776,6 +16100,7 @@ struct llm_build_context { cb(cur, "result_norm", -1); // lm_head + // FIXME: do not use model.tok_embd directly, duplicate as model.output cur = llm_build_lora_mm(lctx, ctx0, model.tok_embd, cur); cb(cur, "result_output", -1); @@ -15796,7 +16121,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(lctx.is_encoding); struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); @@ -15928,7 +16253,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(!lctx.is_encoding); GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first"); @@ -16130,7 +16455,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -16222,7 +16547,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16336,7 +16661,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16460,7 +16785,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16580,11 +16905,11 @@ struct llm_build_context { // Token shift state dimensions should be 2 * n_emb GGML_ASSERT(n_embd == hparams.n_embd_r(0) / 2); - const int64_t n_seqs = batch.n_seqs; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_seqs = ubatch.n_seqs; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(n_seqs != 0); - GGML_ASSERT(batch.equal_seqs); + GGML_ASSERT(ubatch.equal_seqs); GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs); struct ggml_tensor * cur; @@ -16592,7 +16917,7 @@ struct llm_build_context { struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1); for (int il = 0; il < n_layer; ++il) { @@ -16677,9 +17002,11 @@ struct llm_build_context { cur = ggml_get_rows(ctx0, cur, inp_out_ids); cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1); - cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_norm", -1); + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); cb(cur, "result_output", -1); + ggml_build_forward_expand(gf, cur); return gf; @@ -16704,7 +17031,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16900,7 +17227,7 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { static struct ggml_cgraph * llama_build_graph( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, bool worst_case) { const auto & model = lctx.model; @@ -16915,20 +17242,21 @@ static struct ggml_cgraph * llama_build_graph( if (!lctx.cparams.offload_kqv) { if (strcmp(name, "kqv_merged_cont") == 0) { // all nodes between the KV store and the attention output are run on the CPU - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu); + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, lctx.backend_cpu); } } // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends // FIXME: fix in ggml_backend_sched const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer; - if (batch.n_tokens < 32 || full_offload) { + if (ubatch.n_tokens < 32 || full_offload) { if (il != -1 && strcmp(name, "norm") == 0) { - for (auto * backend : lctx.backends) { - if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) && - (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) { - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); - break; + const auto & dev_layer = lctx.model.dev_layer.at(il); + for (auto & backend : lctx.backends) { + if (ggml_backend_get_device(backend.get()) == dev_layer.dev) { + if (ggml_backend_supports_op(backend.get(), cur)) { + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, backend.get()); + } } } } @@ -16937,7 +17265,7 @@ static struct ggml_cgraph * llama_build_graph( struct ggml_cgraph * result = NULL; - struct llm_build_context llm(lctx, batch, cb, worst_case); + struct llm_build_context llm(lctx, ubatch, cb, worst_case); llm.init(); @@ -17070,6 +17398,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_OLMO_1124: + { + result = llm.build_olmo_1124(); + } break; case LLM_ARCH_OLMOE: { result = llm.build_olmoe(); @@ -17180,7 +17512,7 @@ static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t return relative_bucket; } -static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { +static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) { // // set input data // @@ -17190,28 +17522,28 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const auto & kv_self = lctx.cache.kv; const auto & rs_self = lctx.cache.rs; - if (batch.token) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.token) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); + ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); } - if (batch.embd) { + if (ubatch.embd) { const int64_t n_embd = hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); + ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); } - if (batch.pos && lctx.inp_pos) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.pos && lctx.inp_pos) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer)); int32_t * data = (int32_t *) lctx.inp_out_ids->data; @@ -17220,10 +17552,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int i = 0; i < n_tokens; ++i) { data[i] = i; } - } else if (batch.output) { + } else if (ubatch.output) { int32_t n_outputs = 0; for (int i = 0; i < n_tokens; ++i) { - if (batch.output[i]) { + if (ubatch.output[i]) { data[n_outputs++] = i; } } @@ -17248,9 +17580,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. if (cparams.causal_attn && !lctx.is_encoding) { const int64_t n_kv = kv_self.n; - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; float * data = nullptr; @@ -17267,14 +17599,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } // For causal attention, use only the previous KV cells - // of the correct sequence for each token of the batch. + // of the correct sequence for each token of the ubatch. // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int j = 0; j < n_seq_tokens; ++j) { - const llama_pos pos = batch.pos[s*n_seq_tokens + j]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + j]; for (int i = 0; i < n_kv; ++i) { float f; @@ -17320,9 +17652,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } } } else { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; // when using kv cache, the mask needs to match the kv cache size const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens; @@ -17332,7 +17664,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int s1 = 0; s1 < n_seqs; ++s1) { - const llama_seq_id seq_id = batch.seq_id[s1][0]; + const llama_seq_id seq_id = ubatch.seq_id[s1][0]; for (int j = 0; j < n_seq_tokens; ++j) { const int32_t tj = s1*n_seq_tokens + j; @@ -17342,10 +17674,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const int32_t ti = s0*n_seq_tokens + i; float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[s0]; ++s) { - if (batch.seq_id[s0][s] == seq_id) { + for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) { + if (ubatch.seq_id[s0][s] == seq_id) { if (hparams.use_alibi) { - f = -std::abs(batch.pos[ti] - batch.pos[tj]); + f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]); } else { f = 0.0f; } @@ -17367,9 +17699,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_mean); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -17380,12 +17712,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector sum(n_tokens, 0); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); - sum[seq_id] += batch.n_seq_tokens; + sum[seq_id] += ubatch.n_seq_tokens; } std::vector div(n_tokens, 0.0f); @@ -17397,7 +17729,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int i = 0; i < n_seq_tokens; ++i) { data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id]; @@ -17408,9 +17740,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (cparams.embeddings && ( cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -17419,13 +17751,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos == 0) { data[seq_id] = s*n_seq_tokens + i; @@ -17435,9 +17767,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -17449,13 +17781,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector last_row(n_tokens, -1); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos >= last_pos[seq_id]) { last_pos[seq_id] = pos; @@ -17518,10 +17850,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (lctx.inp_pos_bucket) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing int32_t * data = (int32_t *) lctx.inp_pos_bucket->data; @@ -17531,7 +17863,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_kv; ++i) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.cache.kv.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.cache.kv.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -17539,7 +17871,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_tokens; ++i) { - data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -17555,10 +17887,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) { const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing float * data = (float *) lctx.inp_KQ_mask_cross->data; @@ -17567,8 +17899,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_output_enc; ++i) { float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[j]; ++s) { - const llama_seq_id seq_id = batch.seq_id[j][s]; + for (int s = 0; s < ubatch.n_seq_id[j]; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[j][s]; if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) { f = 0.0f; } @@ -17610,7 +17942,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { lctx.output_ids.resize(n_batch); } - const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0; + const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0; const size_t new_size = (logits_size + embd_size) * sizeof(float); // alloc only when more than the current capacity is required @@ -17621,20 +17953,26 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark) LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif - ggml_backend_buffer_free(lctx.buf_output); lctx.buf_output = nullptr; lctx.logits = nullptr; lctx.embd = nullptr; } - lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(lctx.model, true), new_size); + auto * buft = ggml_backend_cpu_buffer_type(); + // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory + auto * output_dev = lctx.model.dev_output.dev; + auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr; + if (output_dev_host_buft) { + buft = output_dev_host_buft; + } + lctx.buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size)); if (lctx.buf_output == nullptr) { LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0)); return 0; } } - float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output); + float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output.get()); lctx.logits = has_logits ? output_base : nullptr; lctx.embd = has_embd ? output_base + logits_size : nullptr; @@ -17646,7 +17984,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // set all ids as invalid (negative) std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1); - ggml_backend_buffer_clear(lctx.buf_output, 0); + ggml_backend_buffer_clear(lctx.buf_output.get(), 0); lctx.n_outputs = 0; @@ -17691,7 +18029,8 @@ static void llama_output_reorder(struct llama_context * ctx) { } } -static void llama_graph_compute( +// returns the result of ggml_backend_sched_graph_compute_async execution +static enum ggml_status llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads, @@ -17706,15 +18045,20 @@ static void llama_graph_compute( set_n_threads_fn.second(set_n_threads_fn.first, n_threads); } - auto err = ggml_backend_sched_graph_compute_async(lctx.sched, gf); - if (err != GGML_STATUS_SUCCESS) { - LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err); + auto status = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf); + if (status != GGML_STATUS_SUCCESS) { + LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status); } // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); + + return status; } // decode a batch of tokens by evaluating the transformer +// in case of unsuccessful decoding (error or warning), +// the kv_cache state will be returned to its original state +// (for non-recurrent models) or cleaned (for recurrent models) // // - lctx: llama context // - batch: batch to evaluate @@ -17725,26 +18069,30 @@ static void llama_graph_compute( // static int llama_decode_internal( llama_context & lctx, - llama_batch batch_all) { // TODO: rename back to batch + llama_batch inp_batch) { lctx.is_encoding = false; - const uint32_t n_tokens_all = batch_all.n_tokens; - if (n_tokens_all == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens_all = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; - GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT + GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT - if (batch_all.token) { + if (batch.token) { for (uint32_t i = 0; i < n_tokens_all; ++i) { - if (batch_all.token[i] < 0 || (uint32_t)batch_all.token[i] >= model.vocab.n_vocab) { - LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch_all.token[i]); + if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) { + LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]); return -1; } } @@ -17761,6 +18109,7 @@ static int llama_decode_internal( auto & kv_self = lctx.cache.kv; auto & rs_self = lctx.cache.rs; + llama_kv_slot_restorer kv_slot_restorer(lctx.cache); const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; @@ -17776,9 +18125,9 @@ static int llama_decode_internal( lctx.embd_seq.clear(); // count outputs - if (batch_all.logits && !embd_pooled) { + if (batch.logits && !embd_pooled) { for (uint32_t i = 0; i < n_tokens_all; ++i) { - n_outputs += batch_all.logits[i] != 0; + n_outputs += batch.logits[i] != 0; } } else if (lctx.logits_all || embd_pooled) { n_outputs = n_tokens_all; @@ -17787,7 +18136,7 @@ static int llama_decode_internal( n_outputs = 1; } - lctx.sbatch.from_batch(batch_all, n_embd, + lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ rs_self.size == 0, /* logits_all */ n_outputs == n_tokens_all); @@ -17839,9 +18188,11 @@ static int llama_decode_internal( if (hparams.causal_attn) { llama_kv_cache_update(&lctx); - if (!llama_kv_cache_find_slot(lctx.cache, ubatch)) { + const auto slot = llama_kv_cache_find_slot(lctx.cache, ubatch); + if (!slot) { return 1; } + kv_slot_restorer.save(slot); // TODO: move into llama_kv_cache_find_slot if (kv_self.size > 0) { @@ -17856,8 +18207,8 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - ggml_backend_sched_reset(lctx.sched); - ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + ggml_backend_sched_reset(lctx.sched.get()); + ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); @@ -17885,11 +18236,23 @@ static int llama_decode_internal( } // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_inputs(lctx, ubatch); - llama_graph_compute(lctx, gf, n_threads, threadpool); + const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool); + if (compute_status != GGML_STATUS_SUCCESS) { + kv_slot_restorer.restore(lctx.cache); + switch (compute_status) { + case GGML_STATUS_ABORTED: + return 2; + case GGML_STATUS_ALLOC_FAILED: + return -2; + case GGML_STATUS_FAILED: + default: + return -3; + } + } // update the kv ring buffer { @@ -17912,7 +18275,7 @@ static int llama_decode_internal( // extract logits if (res) { - ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res); + ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), res); GGML_ASSERT(backend_res != nullptr); GGML_ASSERT(lctx.logits != nullptr); @@ -17928,7 +18291,7 @@ static int llama_decode_internal( // extract embeddings if (embd) { - ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd); GGML_ASSERT(backend_embd != nullptr); switch (cparams.pooling_type) { @@ -18027,7 +18390,7 @@ static int llama_decode_internal( // Reset state for the next token before backend sync, to allow the CPU activities in the reset to // overlap with device computation. - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); return 0; } @@ -18043,17 +18406,20 @@ static int llama_decode_internal( // static int llama_encode_internal( llama_context & lctx, - llama_batch batch) { + llama_batch inp_batch) { lctx.is_encoding = true; - const uint32_t n_tokens = batch.n_tokens; - - if (n_tokens == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -18102,8 +18468,8 @@ static int llama_encode_internal( GGML_ASSERT(n_threads > 0); - ggml_backend_sched_reset(lctx.sched); - ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + ggml_backend_sched_reset(lctx.sched.get()); + ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); @@ -18127,15 +18493,26 @@ static int llama_encode_internal( } } - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_inputs(lctx, ubatch); - llama_graph_compute(lctx, gf, n_threads, threadpool); + const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool); + switch (compute_status) { + case GGML_STATUS_SUCCESS: + break; + case GGML_STATUS_ABORTED: + return 2; + case GGML_STATUS_ALLOC_FAILED: + return -2; + case GGML_STATUS_FAILED: + default: + return -3; + } // extract embeddings if (embd) { - ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd); GGML_ASSERT(backend_embd != nullptr); if (llama_model_has_decoder(&lctx.model)) { @@ -18202,7 +18579,7 @@ static int llama_encode_internal( // Reset state for the next token before backend sync, to allow the CPU activities in the reset to // overlap with device computation. - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); return 0; } @@ -18416,7 +18793,7 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { #else // ggml_graph defrag - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids); @@ -18431,18 +18808,18 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { static void llama_kv_cache_update_internal(struct llama_context & lctx) { bool need_reserve = false; - // apply K-shift if needed - if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.cache.kv.has_shift) { - if (lctx.model.arch == LLM_ARCH_DEEPSEEK2) { // not supported due to MLA - GGML_ABORT("Deepseek2 does not support K-shift"); + if (lctx.cache.kv.has_shift) { + if (!llama_kv_cache_can_shift(&lctx)) { + GGML_ABORT("The current context does not support K-shift"); } - { - ggml_backend_sched_reset(lctx.sched); + // apply K-shift if needed + if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { + ggml_backend_sched_reset(lctx.sched.get()); ggml_cgraph * gf = llama_build_graph_k_shift(lctx); - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_k_shift(lctx); @@ -18482,8 +18859,8 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true); // initialize scheduler with the worst-case graph - ggml_backend_sched_reset(lctx.sched); - if (!ggml_backend_sched_reserve(lctx.sched, gf)) { + ggml_backend_sched_reset(lctx.sched.get()); + if (!ggml_backend_sched_reserve(lctx.sched.get(), gf)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); } } @@ -19010,6 +19387,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s llama_model model; llm_load_arch(ml, model); llm_load_hparams(ml, model); + llm_load_stats(ml, model); struct quantize_state_internal qs(model, params); @@ -19034,40 +19412,57 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } const size_t align = GGUF_DEFAULT_ALIGNMENT; - struct gguf_context * ctx_out = gguf_init_empty(); + gguf_context_ptr ctx_out { gguf_init_empty() }; // copy the KV pairs from the input file - gguf_set_kv (ctx_out, ml.meta); - gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV - gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV + gguf_set_kv (ctx_out.get(), ml.meta.get()); + gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV + gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV // Remove split metadata - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); if (params->kv_overrides) { const std::vector & overrides = *(const std::vector *)params->kv_overrides; - for (auto & o : overrides) { + for (const auto & o : overrides) { if (o.key[0] == 0) break; if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) { - gguf_set_val_f32(ctx_out, o.key, o.val_f64); + gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) { - gguf_set_val_i32(ctx_out, o.key, o.val_i64); + gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) { - gguf_set_val_bool(ctx_out, o.key, o.val_bool); + gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) { - gguf_set_val_str(ctx_out, o.key, o.val_str); + gguf_set_val_str(ctx_out.get(), o.key, o.val_str); } else { LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key); } } } - for (int i = 0; i < ml.n_tensors; ++i) { - const struct ggml_tensor * meta = ml.get_tensor_meta(i); + // make a list of weights + std::vector tensors; + tensors.reserve(ml.weights_map.size()); + for (const auto & it : ml.weights_map) { + tensors.push_back(&it.second); + } - const std::string name = ggml_get_name(meta); + // keep_split requires that the weights are sorted by split index + if (params->keep_split) { + std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) { + if (a->idx == b->idx) { + return a->offs < b->offs; + } + return a->idx < b->idx; + }); + } + + for (const auto * it : tensors) { + const struct ggml_tensor * tensor = it->tensor; + + const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants if (name.find("attn_v.weight") != std::string::npos || @@ -19105,32 +19500,32 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector> f32_conv_buf; uint16_t n_split = 1; + // Assume split index is continuous if (params->keep_split) { - for (int i = 0; i < ml.n_tensors; ++i) { - n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split); + for (const auto * it : tensors) { + n_split = std::max(uint16_t(it->idx + 1), n_split); } } - std::vector ctx_outs(n_split, NULL); - ctx_outs[0] = ctx_out; + std::vector ctx_outs(n_split); + ctx_outs[0] = std::move(ctx_out); // populate the original tensors so we get an initial meta data - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - uint16_t i_split = params->keep_split ? weight->idx : 0; - struct ggml_tensor * tensor = weight->tensor; - if (ctx_outs[i_split] == NULL) { - ctx_outs[i_split] = gguf_init_empty(); + for (const auto * it : tensors) { + uint16_t i_split = params->keep_split ? it->idx : 0; + struct ggml_tensor * tensor = it->tensor; + if (!ctx_outs[i_split]) { + ctx_outs[i_split].reset(gguf_init_empty()); } - gguf_add_tensor(ctx_outs[i_split], tensor); + gguf_add_tensor(ctx_outs[i_split].get(), tensor); } // Set split info if needed if (n_split > 1) { for (size_t i = 0; i < ctx_outs.size(); ++i) { - gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); - gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); - gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); + gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); + gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); + gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); } } @@ -19140,8 +19535,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // Write metadata and close file handler if (fout.is_open()) { fout.seekp(0); - std::vector data(gguf_get_meta_size(ctx_outs[cur_split])); - gguf_get_meta_data(ctx_outs[cur_split], data.data()); + std::vector data(gguf_get_meta_size(ctx_outs[cur_split].get())); + gguf_get_meta_data(ctx_outs[cur_split].get(), data.data()); fout.write((const char *) data.data(), data.size()); fout.close(); } @@ -19158,19 +19553,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s fout = std::ofstream(fname, std::ios::binary); fout.exceptions(std::ofstream::failbit); // fail fast on write errors - const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]); + const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get()); // placeholder for the meta data ::zeros(fout, meta_size); }; const auto tn = LLM_TN(model.arch); new_ofstream(0); - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - struct ggml_tensor * tensor = weight->tensor; - if (weight->idx != cur_split && params->keep_split) { + for (const auto * it : tensors) { + const auto & weight = *it; + struct ggml_tensor * tensor = weight.tensor; + if (weight.idx != cur_split && params->keep_split) { close_ofstream(); - new_ofstream(weight->idx); + new_ofstream(weight.idx); } const std::string name = ggml_get_name(tensor); @@ -19343,17 +19738,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s total_size_new += new_size; // update the gguf meta data as we go - gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type); - gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size); + gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type); + gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data, new_size); // write tensor data + padding fout.write((const char *) new_data, new_size); zeros(fout, GGML_PAD(new_size, align) - new_size); } close_ofstream(); - for (auto & c:ctx_outs) { - gguf_free(c); - } LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); @@ -19367,55 +19759,55 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) { LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora); - ggml_context * ctx = nullptr; + ggml_context * ctx_init; struct gguf_init_params meta_gguf_params = { /* .no_alloc = */ true, - /* .ctx = */ &ctx, + /* .ctx = */ &ctx_init, }; - struct gguf_context * ctx_gguf = gguf_init_from_file(path_lora, meta_gguf_params); + + gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) }; if (!ctx_gguf) { throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora)); } + ggml_context_ptr ctx { ctx_init }; + // check metadata { auto get_kv_str = [&](const std::string & key) -> std::string { - int id = gguf_find_key(ctx_gguf, key.c_str()); - return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id)); + int id = gguf_find_key(ctx_gguf.get(), key.c_str()); + return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id)); }; auto get_kv_f32 = [&](const std::string & key) -> float { - int id = gguf_find_key(ctx_gguf, key.c_str()); - return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id); + int id = gguf_find_key(ctx_gguf.get(), key.c_str()); + return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id); }; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE)); if (general_type != "adapter") { - gguf_free(ctx_gguf); throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type); } auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE)); auto general_arch = llm_arch_from_string(general_arch_str); if (general_arch != model->arch) { - gguf_free(ctx_gguf); throw std::runtime_error("model arch and LoRA arch mismatch"); } auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE)); if (adapter_type != "lora") { - gguf_free(ctx_gguf); throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type); } adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA)); } - int n_tensors = gguf_get_n_tensors(ctx_gguf); + int n_tensors = gguf_get_n_tensors(ctx_gguf.get()); // contexts for each buffer type std::map ctx_map; - auto get_ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { auto it = ctx_map.find(buft); if (it == ctx_map.end()) { // add a new context @@ -19425,7 +19817,11 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c /*.no_alloc =*/ true, }; ggml_context * buft_ctx = ggml_init(params); + if (!buft_ctx) { + return nullptr; + } ctx_map[buft] = buft_ctx; + adapter.ctxs.emplace_back(buft_ctx); return buft_ctx; }; return it->second; @@ -19436,7 +19832,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c auto str_endswith = [](const std::string & str, const std::string & suffix) { return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0; }; - for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) { std::string name(cur->name); if (str_endswith(name, ".lora_a")) { replace_all(name, ".lora_a", ""); @@ -19453,8 +19849,6 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c ab_map[name].b = cur; } } else { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix"); } } @@ -19465,28 +19859,20 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c llama_lora_weight & w = it.second; if (!w.a || !w.b) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component"); } // device buft and device ctx auto * model_tensor = llama_get_model_tensor(model, name.c_str()); if (!model_tensor) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model"); } - struct ggml_context * dev_ctx = get_ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer)); + struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer)); // validate tensor shape if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("tensor '" + name + "' has incorrect shape"); } if (w.a->ne[1] != w.b->ne[0]) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)"); } // save tensor to adapter @@ -19501,18 +19887,15 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c { adapter.ctxs.reserve(ctx_map.size()); adapter.bufs.reserve(ctx_map.size()); - for (auto it : ctx_map) { + for (auto & it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx_dev = it.second; - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft); + ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) }; if (!buf) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("failed to allocate buffer for lora adapter\n"); } - LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); - adapter.ctxs.push_back(ctx_dev); - adapter.bufs.push_back(buf); + LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0); + adapter.bufs.emplace_back(std::move(buf)); } } @@ -19521,7 +19904,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c llama_file gguf_file(path_lora, "rb"); std::vector read_buf; auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) { - size_t offs = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, gguf_find_tensor(ctx_gguf, orig->name)); + size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name)); size_t size = ggml_nbytes(orig); read_buf.resize(size); gguf_file.seek(offs, SEEK_SET); @@ -19536,11 +19919,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c } } - LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2); - - // free ctx for reading gguf - gguf_free(ctx_gguf); - ggml_free(ctx); + LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2); } int32_t llama_lora_adapter_set( @@ -19675,15 +20054,8 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) - // Defined when llama.cpp is compiled with support for offloading model layers to GPU. - return true; -#else return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr || - ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL) != nullptr || llama_supports_rpc(); -#endif } bool llama_supports_rpc(void) { @@ -19773,8 +20145,7 @@ struct llama_model * llama_load_model_from_file( return nullptr; } - // ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); - using ggml_backend_rpc_add_device_t = ggml_backend_dev_t (*)(const char *); + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); if (!ggml_backend_rpc_add_device_fn) { LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__); @@ -19801,17 +20172,34 @@ struct llama_model * llama_load_model_from_file( ggml_backend_dev_t dev = ggml_backend_dev_get(i); switch (ggml_backend_dev_type(dev)) { case GGML_BACKEND_DEVICE_TYPE_CPU: - case GGML_BACKEND_DEVICE_TYPE_CPU_FULL: - // skip CPU backends since they are `handled separately + case GGML_BACKEND_DEVICE_TYPE_ACCEL: + // skip CPU backends since they are handled separately break; case GGML_BACKEND_DEVICE_TYPE_GPU: - case GGML_BACKEND_DEVICE_TYPE_GPU_FULL: model->devices.push_back(dev); break; } } + // if using single GPU mode, remove all except the main GPU + if (params.split_mode == LLAMA_SPLIT_MODE_NONE) { + if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) { + LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size()); + llama_free_model(model); + return nullptr; + } + ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; + model->devices.clear(); + model->devices.push_back(main_gpu); + } + + for (auto * dev : model->devices) { + size_t free, total; // NOLINT + ggml_backend_dev_memory(dev, &free, &total); + LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024); + } + int status = llama_model_load(path_model, *model, params); GGML_ASSERT(status <= 0); if (status < 0) { @@ -19860,7 +20248,7 @@ struct llama_context * llama_new_context_with_model( params.flash_attn = false; } - if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) { + if (ggml_is_quantized(params.type_v) && !params.flash_attn) { LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__); return nullptr; } @@ -19940,12 +20328,26 @@ struct llama_context * llama_new_context_with_model( cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL; } - LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); - LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); - LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); - LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn); - LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); - LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); + const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max; + + LLAMA_LOG_INFO("%s: n_seq_max = %u\n", __func__, cparams.n_seq_max); + LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); + LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n", __func__, n_ctx_per_seq); + LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); + LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); + LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn); + LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); + LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); + + if (n_ctx_per_seq < hparams.n_ctx_train) { + LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n", + __func__, n_ctx_per_seq, hparams.n_ctx_train); + } + + if (n_ctx_per_seq > hparams.n_ctx_train) { + LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n", + __func__, n_ctx_per_seq, hparams.n_ctx_train); + } ctx->abort_callback = params.abort_callback; ctx->abort_callback_data = params.abort_callback_data; @@ -19962,150 +20364,48 @@ struct llama_context * llama_new_context_with_model( GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); if (!hparams.vocab_only) { - // initialize backends - int main_gpu = model->main_gpu; - - // with registry - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - if (main_gpu >= 0 && main_gpu < (int)model->devices.size()) { - ggml_backend_dev_t main_dev = model->devices[main_gpu]; - ggml_backend_t backend = ggml_backend_dev_init(main_dev, nullptr); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(main_dev)); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); + // GPU backends + for (auto * dev : model->devices) { + ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); + llama_free(ctx); + return nullptr; } - } else { - // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU - for (auto * dev : model->devices) { - ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } - if (main_gpu >= (int)model->devices.size()) { - main_gpu -= (int)model->devices.size(); + ctx->backends.emplace_back(backend); } -#if defined(GGML_USE_VULKAN) - if (model->split_mode == LLAMA_SPLIT_MODE_ROW) { - LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; - } - if (model->split_mode == LLAMA_SPLIT_MODE_NONE) { - ggml_backend_t backend = ggml_backend_vk_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) { - ggml_backend_t backend = ggml_backend_vk_init(device); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } -#elif defined(GGML_USE_SYCL) - // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_sycl_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, main_gpu); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_LAYER requires a backend for each GPU - for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - ggml_backend_t backend = ggml_backend_sycl_init(i); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d for No.%d backend\n", __func__, i, i); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } -#elif defined(GGML_USE_KOMPUTE) - if (model->n_gpu_layers > 0) { - auto * backend = ggml_backend_kompute_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } -#elif defined(GGML_USE_CANN) - // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - // TODO: ggml_backend_cann is not support split tensor now, just leave code here. - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_cann_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, main_gpu); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU - // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version. - for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) { - ggml_backend_t backend = ggml_backend_cann_init(device); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } -#endif - - // add other backends (such as BLAS) + // add ACCEL backends (such as BLAS) for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) { ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); if (backend == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); llama_free(ctx); return nullptr; } - ctx->backends.push_back(backend); + ctx->backends.emplace_back(backend); } } + // add CPU backend ctx->backend_cpu = ggml_backend_cpu_init(); if (ctx->backend_cpu == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); llama_free(ctx); return nullptr; } - ctx->backends.push_back(ctx->backend_cpu); + ctx->backends.emplace_back(ctx->backend_cpu); // create a list of the set_n_threads functions in the backends - for (auto * backend : ctx->backends) { - ggml_backend_dev_t dev = ggml_backend_get_device(backend); + for (auto & backend : ctx->backends) { + ggml_backend_dev_t dev = ggml_backend_get_device(backend.get()); ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr; if (reg) { auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads"); if (ggml_backend_set_n_threads_fn) { - ctx->set_n_threads_fns.emplace_back(backend, ggml_backend_set_n_threads_fn); + ctx->set_n_threads_fns.emplace_back(backend.get(), ggml_backend_set_n_threads_fn); } } } @@ -20161,21 +20461,27 @@ struct llama_context * llama_new_context_with_model( } LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__, - ggml_backend_buffer_name(ctx->buf_output), - ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0); + ggml_backend_buffer_name(ctx->buf_output.get()), + ggml_backend_buffer_get_size(ctx->buf_output.get()) / 1024.0 / 1024.0); } // scheduler and compute buffers { // buffer types used for the compute buffer of each backend std::vector backend_buft; - for (auto * backend : ctx->backends) { - if (ggml_backend_is_cpu(backend)) { - // use host buffers for the CPU backend compute buffer - backend_buft.push_back(llama_default_buffer_type_cpu(*model, true)); - } else { - backend_buft.push_back(ggml_backend_get_default_buffer_type(backend)); + std::vector backend_ptrs; + for (auto & backend : ctx->backends) { + auto * buft = ggml_backend_get_default_buffer_type(backend.get()); + if (ggml_backend_is_cpu(backend.get()) && !model->devices.empty()) { + // use the host buffer of the first device CPU for faster transfer of the intermediate state + auto * dev = model->devices[0]; + auto * host_buft = ggml_backend_dev_host_buffer_type(dev); + if (host_buft) { + buft = host_buft; + } } + backend_buft.push_back(buft); + backend_ptrs.push_back(backend.get()); } const size_t max_nodes = llama_model_max_nodes(*model); @@ -20193,17 +20499,12 @@ struct llama_context * llama_new_context_with_model( // pipeline parallelism requires support for async compute and events in all devices if (pipeline_parallel) { - for (auto * backend : ctx->backends) { - if (ggml_backend_is_cpu(backend)) { + for (auto & backend : ctx->backends) { + if (ggml_backend_is_cpu(backend.get())) { // ignore CPU backend continue; } - auto * dev = ggml_backend_get_device(backend); - if (!dev) { - // backend is using old interface, not supported - pipeline_parallel = false; - break; - } + auto * dev = ggml_backend_get_device(backend.get()); ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); if (!props.caps.async || !props.caps.events) { @@ -20214,30 +20515,44 @@ struct llama_context * llama_new_context_with_model( } } - ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), max_nodes, pipeline_parallel); + ctx->sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel)); if (pipeline_parallel) { - LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched)); + LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched.get())); } - // build worst-case graph + // initialize scheduler with the worst-case graph uint32_t n_seqs = 1; // TODO: worst-case number of sequences uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; - ggml_cgraph * gf = llama_build_graph(*ctx, ubatch, true); - // initialize scheduler with the worst-case graph - if (!ggml_backend_sched_reserve(ctx->sched, gf)) { + llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf_pp = llama_build_graph(*ctx, ubatch_pp, true); + + // reserve pp graph first so that buffers are only allocated once + ggml_backend_sched_reserve(ctx->sched.get(), gf_pp); + int n_splits_pp = ggml_backend_sched_get_n_splits(ctx->sched.get()); + int n_nodes_pp = ggml_graph_n_nodes(gf_pp); + + // reserve with tg graph to get the number of splits and nodes + llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf_tg = llama_build_graph(*ctx, ubatch_tg, true); + ggml_backend_sched_reserve(ctx->sched.get(), gf_tg); + int n_splits_tg = ggml_backend_sched_get_n_splits(ctx->sched.get()); + int n_nodes_tg = ggml_graph_n_nodes(gf_tg); + + // reserve again with pp graph to avoid ggml-alloc reallocations during inference + gf_pp = llama_build_graph(*ctx, ubatch_pp, true); + if (!ggml_backend_sched_reserve(ctx->sched.get(), gf_pp)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); llama_free(ctx); return nullptr; } - for (size_t i = 0; i < ctx->backends.size(); i++) { - ggml_backend_t backend = ctx->backends[i]; + for (size_t i = 0; i < backend_ptrs.size(); ++i) { + ggml_backend_t backend = backend_ptrs[i]; ggml_backend_buffer_type_t buft = backend_buft[i]; - size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend); + size_t size = ggml_backend_sched_get_buffer_size(ctx->sched.get(), backend); if (size > 1) { LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, ggml_backend_buft_name(buft), @@ -20245,10 +20560,16 @@ struct llama_context * llama_new_context_with_model( } } - // note: the number of splits during measure is higher than during inference due to the kv shift - int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); - LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, ggml_graph_n_nodes(gf)); - LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits); + if (n_nodes_pp == n_nodes_tg) { + LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, n_nodes_pp); + } else { + LLAMA_LOG_INFO("%s: graph nodes = %d (with bs=%d), %d (with bs=1)\n", __func__, n_nodes_pp, n_tokens, n_nodes_tg); + } + if (n_splits_pp == n_splits_tg) { + LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits_pp); + } else { + LLAMA_LOG_INFO("%s: graph splits = %d (with bs=%d), %d (with bs=1)\n", __func__, n_splits_pp, n_tokens, n_splits_tg); + } } } @@ -20358,6 +20679,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN: case LLM_ARCH_QWEN2: case LLM_ARCH_QWEN2MOE: + case LLM_ARCH_OLMO_1124: case LLM_ARCH_OLMOE: case LLM_ARCH_PHI2: case LLM_ARCH_PHI3: @@ -20431,19 +20753,11 @@ int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t bu } uint64_t llama_model_size(const struct llama_model * model) { - uint64_t size = 0; - for (const auto & it : model->tensors_by_name) { - size += ggml_nbytes(it.second); - } - return size; + return model->n_bytes; } uint64_t llama_model_n_params(const struct llama_model * model) { - uint64_t nparams = 0; - for (const auto & it : model->tensors_by_name) { - nparams += ggml_nelements(it.second); - } - return nparams; + return model->n_elements; } struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) { @@ -20516,40 +20830,47 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const GGML_ASSERT(cvec.ctxs.empty()); GGML_ASSERT(cvec.bufs.empty()); - // count layer buffer types - std::map buft_layer_count; - for (int64_t i = 0; i < model.hparams.n_layer; i++) { - buft_layer_count[model.buft_layer[i].buft]++; - } - - // allocate contexts + // create a context for each buffer type std::map ctx_map; - for (auto & it : buft_layer_count) { - int n_layers = it.second; - struct ggml_init_params params = { - /*.mem_size =*/ n_layers * ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__); - return 1; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + struct ggml_init_params params = { + /*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + ctx_map[buft] = ctx; + cvec.ctxs.emplace_back(ctx); + return ctx; } - ctx_map[it.first] = ctx; - } + return it->second; + }; // make tensors cvec.tensors.reserve(model.hparams.n_layer); cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 for (size_t il = 1; il < model.hparams.n_layer; il++) { - struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft); + ggml_backend_buffer_type_t buft = select_buft(*model.dev_layer.at(il).buft_list, + [&](ggml_context * ctx) { + ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); + ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); + return ggml_add(ctx, cur, layer_dir); + }); + ggml_context * ctx = ctx_for_buft(buft); + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__); + return false; + } ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); cvec.tensors.push_back(tensor); } // allocate tensors / buffers and zero - cvec.ctxs.reserve(ctx_map.size()); cvec.bufs.reserve(ctx_map.size()); for (auto it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; @@ -20560,8 +20881,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const return false; } ggml_backend_buffer_clear(buf, 0); - cvec.ctxs.push_back(ctx); - cvec.bufs.push_back(buf); + cvec.bufs.emplace_back(buf); } return true; @@ -20769,6 +21089,10 @@ void llama_kv_cache_update(struct llama_context * ctx) { llama_kv_cache_update_internal(*ctx); } +bool llama_kv_cache_can_shift(struct llama_context * ctx) { + return ctx->model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA +} + // deprecated size_t llama_get_state_size(struct llama_context * ctx) { return llama_state_get_size(ctx); @@ -22021,9 +22345,7 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) { struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id) { + int32_t n_tokens) { return { /*n_tokens =*/ n_tokens, /*tokens =*/ tokens, @@ -22032,9 +22354,6 @@ struct llama_batch llama_batch_get_one( /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ pos_0, - /*all_pos_1 =*/ 1, - /*all_seq_id =*/ seq_id, }; } @@ -22047,9 +22366,6 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_ /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ 0, - /*all_pos_1 =*/ 0, - /*all_seq_id =*/ 0, }; if (embd) { @@ -22089,7 +22405,7 @@ int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch) { const int ret = llama_encode_internal(*ctx, batch); - if (ret < 0) { + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); } @@ -22104,7 +22420,7 @@ int32_t llama_decode( #endif const int ret = llama_decode_internal(*ctx, batch); - if (ret < 0) { + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); } @@ -22116,7 +22432,7 @@ int32_t llama_decode( } void llama_synchronize(struct llama_context * ctx) { - ggml_backend_sched_synchronize(ctx->sched); + ggml_backend_sched_synchronize(ctx->sched.get()); // FIXME: if multiple single tokens are evaluated without a synchronization, // the stats will be added to the prompt evaluation stats @@ -22170,7 +22486,7 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs)); } } else if ((size_t) i >= ctx->output_ids.size()) { - throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size())); + throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size())); } else { j = ctx->output_ids[i]; } @@ -22648,6 +22964,29 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "[|assistant|]"; } + } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world")) { + // this template requires the model to have "\n\n" as EOT token + for (auto message : chat) { + std::string role(message->role); + if (role == "user") { + ss << "User: " << message->content << "\n\nAssistant:"; + } else { + ss << message->content << "\n\n"; + } + } + } else if (tmpl == "granite" || tmpl_contains("<|start_of_role|>")) { + // IBM Granite template + for (const auto & message : chat) { + std::string role(message->role); + ss << "<|start_of_role|>" << role << "<|end_of_role|>"; + if (role == "assistant_tool_call") { + ss << "<|tool_call|>"; + } + ss << message->content << "<|end_of_text|>\n"; + } + if (add_ass) { + ss << "<|start_of_role|>assistant<|end_of_role|>\n"; + } } else { // template not supported return -1; @@ -22706,6 +23045,14 @@ struct llama_sampler * llama_sampler_init_grammar(const struct llama_model * mod return llama_sampler_init_grammar_impl(model->vocab, grammar_str, grammar_root); } +struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model) { + return llama_sampler_init_infill_impl(model->vocab); +} + +struct llama_sampler * llama_sampler_init_dry(const struct llama_model * model, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) { + return llama_sampler_init_dry_impl(model->vocab, llama_n_ctx_train(model), dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers, num_breakers); +} + // // model split // @@ -22735,6 +23082,8 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int } const char * llama_print_system_info(void) { + ggml_cpu_init(); // some ARM features are detected at runtime + static std::string s; s = ""; @@ -22745,6 +23094,7 @@ const char * llama_print_system_info(void) { s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; + s += "AMX_INT8 = " + std::to_string(ggml_cpu_has_amx_int8()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | "; @@ -22753,7 +23103,6 @@ const char * llama_print_system_info(void) { s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | "; s += "RISCV_VECT = " + std::to_string(ggml_cpu_has_riscv_v()) + " | "; s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | "; - s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | "; s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | "; s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | "; s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; @@ -22799,28 +23148,6 @@ void llama_perf_context_reset(struct llama_context * ctx) { ctx->t_p_eval_us = ctx->n_p_eval = 0; } -void llama_perf_dump_yaml(FILE * stream, const llama_context * ctx) { - fprintf(stream, "\n"); - fprintf(stream, "###########\n"); - fprintf(stream, "# Timings #\n"); - fprintf(stream, "###########\n"); - fprintf(stream, "\n"); - - fprintf(stream, "mst_eval: %.2f # ms / token during generation\n", - 1.0e-3 * ctx->t_eval_us / ctx->n_eval); - fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n", - 1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval); - fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval); - fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval); - fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us); - fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us); - fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us); - fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n", - 1.0e6 * ctx->n_eval / ctx->t_eval_us); - fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n", - 1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us); -} - // For internal test use const std::vector> & llama_internal_get_tensor_map( struct llama_context * ctx diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 08ad66b49f..b06f122e89 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -116,9 +116,8 @@ llama_target_and_test(test-sampling.cpp) llama_target_and_test(test-chat-template.cpp) llama_target_and_test(test-grammar-parser.cpp) -llama_target_and_test(test-llama-grammar.cpp) llama_target_and_test(test-grammar-integration.cpp) -llama_target_and_test(test-grad0.cpp) +llama_target_and_test(test-llama-grammar.cpp) llama_target_and_test(test-barrier.cpp) # llama_target_and_test(test-opt.cpp) # SLOW llama_target_and_test(test-backend-ops.cpp) diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs index 71bf62ed34..b20ac1d6b5 100644 --- a/tests/run-json-schema-to-grammar.mjs +++ b/tests/run-json-schema-to-grammar.mjs @@ -1,5 +1,5 @@ import { readFileSync } from "fs" -import { SchemaConverter } from "../examples/server/public/json-schema-to-grammar.mjs" +import { SchemaConverter } from "../examples/server/public_legacy/json-schema-to-grammar.mjs" const [, , file] = process.argv const url = `file://${file}` diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ee1a8877e1..b2b5705243 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -16,6 +16,7 @@ #include +#include #include #include @@ -680,6 +681,7 @@ struct test_case { // run int64_t total_time_us = 0; + int64_t total_mem = 0; int total_runs = 0; do { int64_t start_time = ggml_time_us(); @@ -687,6 +689,7 @@ struct test_case { int64_t end_time = ggml_time_us(); total_time_us += end_time - start_time; + total_mem += mem; total_runs += n_runs; } while (total_time_us < 1000*1000); // run for at least 1 second @@ -716,7 +719,7 @@ struct test_case { } else { printf("%8zu kB/run - \033[1;34m%7.2f GB/s\033[0m", op_size(out) / 1024, - mem / (total_time_us / 1e6) / 1024.0 / 1024.0 / 1024.0); + total_mem / (total_time_us / 1e6) / 1024.0 / 1024.0 / 1024.0); } printf("\n"); @@ -808,15 +811,14 @@ struct test_case { ggml_build_forward_expand(gf, out); ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx, gf, gb, false); + ggml_build_backward_expand(ctx, ctx, gb, false); if (expect.size() != 1 || expect[0] != 0.0f) { GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf)); for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || t->grad->op != GGML_OP_NONE); + GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || ggml_graph_get_grad(gb, t)->op != GGML_OP_NONE); } } - // TODO: refactor so that this check is only needed once for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { if (!ggml_backend_supports_op(backend, t)) { printf("not supported [%s] ", ggml_backend_name(backend)); @@ -859,7 +861,13 @@ struct test_case { const char * bn = ggml_backend_name(backend); const int64_t ne = ggml_nelements(t); - std::vector ga = tensor_to_float(t->grad); + std::vector ga; + struct ggml_tensor * grad = ggml_graph_get_grad(gb, t); + if (grad) { + ga = tensor_to_float(grad); + } else { + ga.resize(ne); // default value is 0.0f + } for (int64_t i = 0; i < ne; ++i) { // gradient algebraic // check for nans @@ -1146,6 +1154,26 @@ struct test_argmax : public test_case { return out; } + void initialize_tensors(ggml_context * ctx) override { + std::random_device rd; + std::default_random_engine rng(rd()); + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + if (t->type == GGML_TYPE_F32) { + // initialize with unique values to avoid ties + for (int64_t r = 0; r < ggml_nrows(t); r++) { + std::vector data(t->ne[0]); + for (int i = 0; i < t->ne[0]; i++) { + data[i] = i; + } + std::shuffle(data.begin(), data.end(), rng); + ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float)); + } + } else { + init_tensor_uniform(t); + } + } + } + double max_nmse_err() override { return 0.0; } @@ -1613,8 +1641,8 @@ struct test_ssm_scan : public test_case { } }; -// GGML_OP_RWKV_WKV -struct test_rwkv_wkv : public test_case { +// GGML_OP_RWKV_WKV6 +struct test_rwkv_wkv6 : public test_case { const ggml_type type; const int64_t head_count; @@ -1626,7 +1654,7 @@ struct test_rwkv_wkv : public test_case { return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs); } - test_rwkv_wkv(ggml_type type = GGML_TYPE_F32, + test_rwkv_wkv6(ggml_type type = GGML_TYPE_F32, int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32) : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {} @@ -1638,7 +1666,7 @@ struct test_rwkv_wkv : public test_case { ggml_tensor * tf = ggml_new_tensor(ctx, type, 2, std::vector{ head_size, head_count }.data()); ggml_tensor * td = ggml_new_tensor(ctx, type, 4, std::vector{ 1, head_size, head_count, n_tokens }.data()); ggml_tensor * s = ggml_new_tensor(ctx, type, 2, std::vector{ head_size * head_size * head_count, n_seqs }.data()); - ggml_tensor * out = ggml_rwkv_wkv(ctx, k, v, r, tf, td, s); + ggml_tensor * out = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, s); return out; } }; @@ -1650,11 +1678,12 @@ struct test_mul_mat : public test_case { const int64_t m; const int64_t n; const int64_t k; - const std::array bs; // dims 3 and 4 - const std::array nr; // repeat in dims 3 and 4 + const std::array bs; // dims 3 and 4 + const std::array nr; // repeat in dims 3 and 4 + const std::array per; // permutation of dimensions std::string vars() override { - return VARS_TO_STR7(type_a, type_b, m, n, k, bs, nr); + return VARS_TO_STR8(type_a, type_b, m, n, k, bs, nr, per); } double max_nmse_err() override { @@ -1669,17 +1698,44 @@ struct test_mul_mat : public test_case { test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32, int64_t m = 32, int64_t n = 32, int64_t k = 32, std::array bs = {10, 10}, - std::array nr = {2, 2}) - : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr) {} + std::array nr = {2, 2}, + std::array per = {0, 1, 2, 3}) + : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per) {} ggml_tensor * build_graph(ggml_context * ctx) override { // C^T = A * B^T: (k, m) * (k, n) => (m, n) - ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0] , bs[1]); - ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); - ggml_set_param(ctx, a); - ggml_set_param(ctx, b); - ggml_set_name(a, "a"); - ggml_set_name(b, "b"); + ggml_tensor * a; + ggml_tensor * b; + + const int npermuted = (per[0] != 0) + (per[1] != 1) + (per[2] != 2) + (per[3] != 3); + if (npermuted > 0) { + GGML_ASSERT(npermuted == 2); + GGML_ASSERT(!ggml_is_quantized(type_a) || per[0] == 0); + GGML_ASSERT(!ggml_is_quantized(type_b) || per[0] == 0); + + // Create tensors with the permuted dimensions, then permute them back to the dimensions given by m,n,k. + const int64_t ne_a[4] = {k, m, bs[0], bs[1]}; + const int64_t ne_b[4] = {k, n, bs[0]*nr[0], bs[1]*nr[1]}; + + a = ggml_new_tensor_4d(ctx, type_a, ne_a[per[0]], ne_a[per[1]], ne_a[per[2]], ne_a[per[3]]); + b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]); + ggml_set_param(ctx, a); + ggml_set_param(ctx, b); + ggml_set_name(a, "a"); + ggml_set_name(b, "b"); + + a = ggml_permute(ctx, a, per[0], per[1], per[2], per[3]); + b = ggml_permute(ctx, b, per[0], per[1], per[2], per[3]); + ggml_set_name(a, "a_permuted"); + ggml_set_name(b, "b_permuted"); + } else { + a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]); + b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); + ggml_set_param(ctx, a); + ggml_set_param(ctx, b); + ggml_set_name(a, "a"); + ggml_set_name(b, "b"); + } ggml_tensor * out = ggml_mul_mat(ctx, a, b); ggml_set_name(out, "out"); @@ -2469,6 +2525,35 @@ struct test_sum_rows : public test_case { } }; +// GGML_OP_MEAN +struct test_mean : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_mean(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 5, 4, 3}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(ctx, a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_mean(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + float grad_eps() override { + return 0.1f * ne[0]*ne[1]*ne[2]*ne[3]; + } +}; + // GGML_OP_UPSCALE struct test_upscale : public test_case { const ggml_type type; @@ -2711,6 +2796,13 @@ struct test_flash_attn_ext : public test_case { return 5e-4; } + uint64_t op_flops(ggml_tensor * t) override { + GGML_UNUSED(t); + // Just counting matmul costs: + // Q*K^T is nb x hs x kv, P*V is nb x kv x hs, per head + return 2 * 2 * nh * nb * hs * kv; + } + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16) : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV) {} @@ -2796,24 +2888,14 @@ struct test_cross_entropy_loss : public test_case { struct test_opt_step_adamw : public test_case { const ggml_type type; const std::array ne; - const float alpha; - const float beta1; - const float beta2; - const float eps; - const float wd; std::string vars() override { - return VARS_TO_STR7(type, ne, alpha, beta1, beta2, eps, wd); + return VARS_TO_STR2(type, ne); } test_opt_step_adamw(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 5, 4, 3}, - float alpha = 1e-3f, - float beta1 = 0.9f, - float beta2 = 0.999f, - float eps = 1e-8f, - float wd = 0.0f) - : type(type), ne(ne), alpha(alpha), beta1(beta1), beta2(beta2), eps(eps), wd(wd) {} + std::array ne = {10, 5, 4, 3}) + : type(type), ne(ne) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); @@ -2823,7 +2905,16 @@ struct test_opt_step_adamw : public test_case { ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); ggml_set_name(grad, "grad"); - ggml_tensor * out = ggml_opt_step_adamw(ctx, a, grad, alpha, beta1, beta2, eps, wd); + ggml_tensor * grad_m = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + ggml_set_name(grad_m, "grad_m"); + + ggml_tensor * grad_v = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + ggml_set_name(grad_v, "grad_v"); + + ggml_tensor * adamw_params = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 7); + ggml_set_name(adamw_params, "adamw_params"); + + ggml_tensor * out = ggml_opt_step_adamw(ctx, a, grad, grad_m, grad_v, adamw_params); ggml_set_name(out, "out"); return out; @@ -2831,7 +2922,7 @@ struct test_opt_step_adamw : public test_case { void initialize_tensors(ggml_context * ctx) override { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - init_tensor_uniform(t, 0.0f, 1.0f); // grad_v needs non-negative values. + init_tensor_uniform(t, 0.0f, 1.0f); // grad_v and adamw_params need non-negative values. } } @@ -3308,13 +3399,49 @@ static std::vector> make_test_cases_eval() { } } - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32)); - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); - // test cases for 1D im2col + // im2col 1D test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); + for (int s0 : {1, 3}) { + for (int p0 : {0, 3}) { + for (int d0 : {1, 3}) { + test_cases.emplace_back(new test_im2col( + GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 2, 2, 1}, {3, 2, 2, 1}, + s0, 0, p0, 0, d0, 0, false)); + } + } + } + + // im2col 2D + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + for (int s0 : {1, 3}) { + for (int s1 : {1, 3}) { + for (int p0 : {0, 3}) { + for (int p1 : {0, 3}) { + for (int d0 : {1, 3}) { + for (int d1 : {1, 3}) { + test_cases.emplace_back(new test_im2col( + GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 20, 2, 2}, {3, 3, 2, 2}, + s0, s1, p0, p1, d0, d1, true)); + } + } + } + } + } + } + + // extra tests for im2col 2D + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 32}, {3, 3, 1, 32}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 32}, {3, 3, 2, 32}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 1024}, {3, 3, 1, 1024}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 1024}, {3, 3, 2, 1024}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2048}, {3, 3, 1, 2048}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2048}, {3, 3, 2, 2048}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2560}, {3, 3, 1, 2560}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2560}, {3, 3, 2, 2560}, 1, 1, 1, 1, 1, 1, true)); // sycl backend will limit task global_range < MAX_INT // test cases for 2D im2col with large input W and H (occurs in stable-diffusion) @@ -3333,6 +3460,11 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1)); test_cases.emplace_back(new test_argmax()); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 1, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {100, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {2000, 10, 1, 1})); + test_cases.emplace_back(new test_count_equal()); for (int ne3 : {1, 3}) { // CUDA backward pass only supports ne3 == 1 @@ -3434,21 +3566,22 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1024, 32, 4)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 1, 1)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 1)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 4)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 128, 4)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 1, 1)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 1)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 4)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 128, 4)); #if 1 for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2})); + // test cases without permutation + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {1, 1})); @@ -3457,6 +3590,19 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2})); + + // test cases with permutation + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); + + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); + + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); } } for (ggml_type type_a : other_types) { @@ -3520,7 +3666,7 @@ static std::vector> make_test_cases_eval() { for (int n_mats : {4}) { for (int n_used : {2}) { for (bool b : {false}) { - for (int n : {1}) { + for (int n : {1, 32}) { int m = 512; int k = 256; test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k)); @@ -3647,6 +3793,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_sum()); test_cases.emplace_back(new test_sum_rows()); + test_cases.emplace_back(new test_mean()); test_cases.emplace_back(new test_upscale()); test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true)); test_cases.emplace_back(new test_upscale_ext()); @@ -3666,7 +3813,7 @@ static std::vector> make_test_cases_eval() { for (int nh : { 32, }) { for (int kv : { 512, 1024, }) { for (int nb : { 1, 3, 32, 35, }) { - for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { + for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV)); } } @@ -3678,9 +3825,7 @@ static std::vector> make_test_cases_eval() { } test_cases.emplace_back(new test_cross_entropy_loss()); - for (float wd : {0.0f, 1e-2f}) { - test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}, 1.0f, 1e-3f, 0.9f, 0.999f, wd)); - } + test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3})); // these tests are disabled to save execution time, but they can be handy for debugging #if 0 @@ -3700,6 +3845,20 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 1, 1, 1})); test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 512, 1, 1})); + test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F16, {512, 3072, 1, 1})); + + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {1024, 1024, 10, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 1024, 10, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {256, 256, 20, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, 1.0f, 0.0f)); + + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1})); + for (int bs : {1, 512}) { for (ggml_type type_a : all_types) { for (ggml_type type_b : {GGML_TYPE_F32}) { @@ -3848,6 +4007,8 @@ int main(int argc, char ** argv) { ggml_backend_free(backend); } + ggml_quantize_free(); + printf("%zu/%zu backends passed\n", n_ok, ggml_backend_dev_count()); if (n_ok != ggml_backend_dev_count()) { @@ -3855,8 +4016,6 @@ int main(int argc, char ** argv) { return 1; } - ggml_quantize_free(); - printf("\033[1;32mOK\033[0m\n"); return 0; } diff --git a/tests/test-barrier.cpp b/tests/test-barrier.cpp index cf54237db8..d85bf912b2 100644 --- a/tests/test-barrier.cpp +++ b/tests/test-barrier.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-backend.h" #include diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 6f046249fa..03e897e66d 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -65,6 +65,8 @@ int main(void) { u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + // ibm-granite/granite-3.0-8b-instruct + "{%- if tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n {%- for tool in tools %}\n {{- tool | tojson(indent=4) }}\n {%- if not loop.last %}\n {{- '\n\n' }}\n {%- endif %}\n {%- endfor %}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'user' %}\n {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant' %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant_tool_call' %}\n {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'tool_response' %}\n {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n {%- endif %}\n{%- endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -109,6 +111,8 @@ int main(void) { u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", + // ibm-granite/granite-3.0-8b-instruct + "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|> I am an assistant <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n", }; std::vector formatted_chat(1024); int32_t res; diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp deleted file mode 100644 index 2200ad93db..0000000000 --- a/tests/test-grad0.cpp +++ /dev/null @@ -1,1683 +0,0 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows -#include "ggml.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wdouble-promotion" -#endif - -#define MAX_NARGS 3 - -#undef MIN -#undef MAX -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - -#define GGML_SILU_FP16 - -// -// logging -// - -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - -#define GGML_PRINT(...) printf(__VA_ARGS__) - -static float frand(void) { - return (float)rand()/(float)RAND_MAX; -} - -static int irand(int n) { - if (n == 0) return 0; - return rand()%n; -} - -static void get_random_dims(int64_t * dims, int ndims) { - dims[0] = dims[1] = dims[2] = dims[3] = 1; - - for (int i = 0; i < ndims; i++) { - dims[i] = 1 + irand(4); - } -} - -static struct ggml_tensor * get_random_tensor_f32( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - float fmin, - float fmax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static struct ggml_tensor * get_random_tensor_f16( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - float fmin, - float fmax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F16, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static struct ggml_tensor * get_random_tensor_i32( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - int32_t imin, - int32_t imax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_I32, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i0] = irand(imax - imin) + imin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static bool check_gradient( - const char * op_name, - struct ggml_context * ctx0, - struct ggml_tensor * x[], - struct ggml_tensor * f, - int ndims, - int nargs, - float eps, - float max_error_abs, - float max_error_rel, - std::vector expected_vals) { - - static int n_threads = -1; - if (n_threads < 0) { - n_threads = GGML_DEFAULT_N_THREADS; - - const char *env = getenv("GGML_N_THREADS"); - if (env) { - n_threads = atoi(env); - } - - printf("GGML_N_THREADS = %d\n", n_threads); - } - - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); - struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); - ggml_build_forward_expand(gf, f); - ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx0, gf, gb, false); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - ggml_graph_reset(gb); - if (f->grad) { - ggml_set_f32(f->grad, 1.0f); - } - - ggml_graph_compute_with_ctx(ctx0, gb, n_threads); - - // ggml_graph_dump_dot(gf, NULL, "test-grad0-forward.dot"); - // ggml_graph_dump_dot(gb, gf, "test-grad0-backward.dot"); - - for (int i = 0; i < nargs; ++i) { - bool all_g0_bad = true; - const int nelements = ggml_nelements(x[i]); - for (int k = 0; k < nelements; ++k) { - // Calculate gradient numerically: - const float x0 = ggml_get_f32_1d(x[i], k); - const float xm = x0 - eps; - const float xp = x0 + eps; - ggml_set_f32_1d(x[i], k, xp); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - const double f0 = ggml_get_f32_1d(f, 0); - - ggml_set_f32_1d(x[i], k, xm); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - const double f1 = ggml_get_f32_1d(f, 0); - const double g0 = (f0 - f1)/(2.0*(double) eps); - - // The numerical calculation of the gradient fails around noncontinuities (e.g. 0 for ReLU). - // In such cases, provide a vector of expected values and skip the comparison for failed calculations. - if (!expected_vals.empty()) { - bool matches_any = false; - for (const double & ev : expected_vals) { - const double error_abs = std::fabs(g0 - ev); - if (error_abs > max_error_abs) { - continue; - } - const double error_rel = g0 != 0.0 ? fabs(g0 - ev)/fabs(g0) : 0.0; - if (error_rel > max_error_rel) { - continue; - } - matches_any = true; - break; - } - if (!matches_any) { - continue; - } - } - all_g0_bad = false; - - ggml_set_f32_1d(x[i], k, x0); - - // compute gradient using backward graph - ggml_graph_reset(gb); - if (f->grad) { - ggml_set_f32(f->grad, 1.0f); - } - - ggml_graph_compute_with_ctx(ctx0, gb, n_threads); - - const double g1 = ggml_get_f32_1d(x[i]->grad, k); - - const double error_abs = fabs(g0 - g1); - const double error_rel = g0 != 0.0 ? fabs(g0 - g1)/fabs(g0) : 0.0; - - if (error_abs > max_error_abs || error_rel > max_error_rel) { - printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n", - op_name, ndims, i, k, x0, xm, xp, f0, f1, g0, g1, eps, error_abs, error_rel); - //assert(false); - return false; - } - } - if (all_g0_bad) { - printf("%s: numerical calculation of the gradient failed for all values\n", op_name); - return false; - } - } - - return true; -} - -// TODO: clean-up this .. -static bool check_mat_mul( - const struct ggml_tensor * y, - const struct ggml_tensor * x0, - const struct ggml_tensor * x1) { - float * dst = (float *) y->data; - float * src0 = (float *) x0->data; - float * src1 = (float *) x1->data; - - const int nc = x0->ne[1]; - const int nr = x1->ne[1]; - const int nk = x0->ne[0]; - - GGML_PRINT_DEBUG("check_mat_mul: nc=%d, nr=%d, nk=%d\n", nc, nr, nk); - - GGML_PRINT_DEBUG("x0:\n"); - for (int j = 0; j < x0->ne[1]; ++j) { - for (int i = 0; i < x0->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", src0[j*nk + i]); - } - GGML_PRINT_DEBUG("\n"); - } - GGML_PRINT_DEBUG("\n"); - - GGML_PRINT_DEBUG("x1:\n"); - for (int j = 0; j < x1->ne[1]; ++j) { - for (int i = 0; i < x1->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", src1[j*nk + i]); - } - GGML_PRINT_DEBUG("\n"); - } - GGML_PRINT_DEBUG("\n"); - - GGML_PRINT_DEBUG("y: n_dims = %d, (%lld, %lld)\n", y->n_dims, y->ne[0], y->ne[1]); - for (int j = 0; j < y->ne[1]; ++j) { - for (int i = 0; i < y->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", dst[j*nr + i]); - } - GGML_PRINT_DEBUG("\n"); - } - - for (int i = 0; i < nr; ++i) { - for (int j = 0; j < nc; ++j) { - float sum = 0.0f; - - for (int k = 0; k < nk; ++k) { - sum += src0[j*nk + k]*src1[i*nk + k]; - } - - if (fabsf(dst[i*nc + j] - sum) > 1e-5f) { - fprintf(stderr, "check_mat_mul: dst[%d] = %f, sum = %f\n", i*nc + j, dst[i*nc + j], sum); - assert(false); - return false; - } - } - } - - return true; -} - -#define NUM_PERMUTATIONS (4*3*2*1) - -int main(int argc, const char ** argv) { - struct ggml_init_params params = { - /* .mem_size = */ 256*1024*1024, - /* .mem_buffer = */ NULL, - /* .no_alloc = */ false, - }; - - int64_t ne[4]; - - int all_permutations[4 * NUM_PERMUTATIONS]; - { - int count = 0; - for (int ax0=0; ax0<4; ++ax0) { - for (int ax1=0; ax1<4; ++ax1) { - if (ax1 == ax0) continue; - for (int ax2=0; ax2<4; ++ax2) { - if (ax2 == ax0) continue; - if (ax2 == ax1) continue; - for (int ax3=0; ax3<4; ++ax3) { - if (ax3 == ax0) continue; - if (ax3 == ax1) continue; - if (ax3 == ax2) continue; - assert(count < NUM_PERMUTATIONS); - all_permutations[count*4+0] = ax0; - all_permutations[count*4+1] = ax1; - all_permutations[count*4+2] = ax2; - all_permutations[count*4+3] = ax3; - ++count; - } - } - } - } - } - - unsigned seed_iter = 1; - - // original loop: 1000 - int niter = 4; - const char *env = getenv("GGML_NLOOP"); - if (env != NULL) { - niter = atoi(env); - } - if (argc > 1) { - niter = atoi(argv[1]); - } - for (int iter = 0; iter < niter; ++iter) { - srand(seed_iter); - seed_iter = rand(); - unsigned seed = rand(); - - printf("test-grad0: iter:%d/%d\n", (iter+1), niter); - struct ggml_context * ctx0 = ggml_init(params); - - get_random_dims(ne, 4); - - struct ggml_tensor * x[MAX_NARGS]; - - // add f32 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1])); - - check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f, {}); - } - } - - // add f16 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1])); - - check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f, {}); - } - } - - // sub - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1])); - - check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // mul - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1])); - - check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // div - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 0.5f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1])); - - check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f, {}); - } - } - - // sqr - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0])); - - check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // sqrt - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0])); - - check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f, {}); - } - } - - // log - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_log(ctx0, x[0])); - - check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f, {}); - } - } - - // sum - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, x[0]); - - check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - - // sum_rows - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sum_rows(ctx0, x[0]))); - - check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // mean, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_mean(ctx0, x[0])); - - check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // argmax - if (0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_argmax(ctx0, x[0])); - - check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // repeat - { - srand(seed); - int64_t ne2[4]; - get_random_dims(ne2, 4); - - ne2[0] = ne[0] * ne2[0]; - ne2[1] = ne[1] * ne2[1]; - ne2[2] = 1; - ne2[3] = 1; - - const int nargs = 1; - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1])))); - - check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // repeat back - { - srand(seed); - int64_t ne2[4]; - get_random_dims(ne2, 4); - - ne2[0] = ne[0] * ne2[0]; - ne2[1] = ne[1] * ne2[1]; - ne2[2] = 1; - ne2[3] = 1; - - const int nargs = 1; - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[0], ggml_repeat_back(ctx0, x[1], x[0])))); - - check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // abs - { - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0])); - - check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f, {-1.0, 1.0}); - } - } - - // sgn - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_sgn(ctx0, x[0])); - - check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0}); - } - } - - // neg - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_neg(ctx0, x[0])); - - check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // step - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_step(ctx0, x[0])); - - check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0}); - } - } - - // tanh, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_tanh(ctx0, x[0])); - - check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // mul_mat - { - srand(seed); - const int nargs = 2; - - for (int ndims = 2; ndims <= 4; ++ndims) { - int max_nrep = (ndims >= 3) ? 2 : 1; - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - for (int nrep2 = 1; nrep2 < max_nrep; ++nrep2) { - for (int nrep3 = 1; nrep3 < max_nrep; ++nrep3) { - { - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] = ne[0]; - ne2[2] = nrep2 * ne[2]; - ne2[3] = nrep3 * ne[3]; - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - } - - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - - struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]); - struct ggml_tensor * f = ggml_sum(ctx0, m); - - GGML_PRINT_DEBUG("testing: mul_mat, [%lld, %lld] (%d) * [%lld, %lld] (%d)\n", x[1]->ne[0], x[1]->ne[1], x[1]->n_dims, x[0]->ne[0], x[0]->ne[1], x[0]->n_dims); - - check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - if (ndims == 2) { - // check_mat_mul does not support ndims > 2 - check_mat_mul(m, x[1], x[0]); - } - } - } - } - } - - // elu, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_elu(ctx0, x[0])); - - check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // relu - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_relu(ctx0, x[0])); - - check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {0.0, 1.0}); - } - } - - // gelu, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_gelu(ctx0, x[0])); - - check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // silu - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_silu(ctx0, x[0])); - -#ifdef GGML_SILU_FP16 - // due to GGML_SILU_FP16 the finite difference method will be slightly wrong -> increase error bounds. - check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY, {}); -#else - check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); -#endif - } - } - - // rms_norm - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rms_norm(ctx0, x[0], 1e-6f)); - - check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY, {}); - } - } - - // scale - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - const float s = -1.0f + 2.0f*frand(); - - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], s)); - - check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // cpy f32 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - // x[1] is overwritten by x[0], so the gradients don't propagate to x[1] - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1])); - - check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // cpy f16 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - // x[1] is overwritten by x[0], so the gradients don't propagate to x[1] - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1])); - - check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {}); - } - } - - // reshape (1d->nd) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - int64_t ne2[4]; - ne2[0] = 1; - ne2[1] = 1; - ne2[2] = 1; - ne2[3] = 1; - for (int i = 0; i < ndims; ++i) { - ne2[0] *= ne[i]; - } - x[0] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1])); - check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // reshape (nd->1d) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - int64_t ne2[4]; - ne2[0] = 1; - ne2[1] = 1; - ne2[2] = 1; - ne2[3] = 1; - for (int i = 0; i < ndims; ++i) { - ne2[0] *= ne[i]; - } - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1])); - check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 1d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - - const int nargs = 2; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 1); - while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 1); - } - - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1])); - const int offset = irand(max_offset) * ggml_element_size(x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 2d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 2; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 2); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 2); - } - - x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - const int offset = offsets[0] + offsets[1]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 3d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 3; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 3); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 3); - } - - x[1] = get_random_tensor_f32(ctx0, 3, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - offsets[2] = irand(max_offsets[2]) * x[0]->nb[2]; - const int offset = offsets[0] + offsets[1] + offsets[2]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 4d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 4; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 4); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[3] > ne[3]) || (ne2[0]*ne2[1]*ne2[2]*ne2[3] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 4); - } - - x[1] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]); - max_offsets[3] = MAX(0, x[0]->ne[3] - x[1]->ne[3]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - offsets[2] = irand(max_offsets[2]) * x[0]->nb[2]; - offsets[3] = irand(max_offsets[3]) * x[0]->nb[3]; - const int offset = offsets[0] + offsets[1] + offsets[2] + offsets[3]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // set_1d - { - srand(seed); - int64_t ne2[4]; - - const int nargs = 2; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 1); - while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 1); - } - - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1])); - const int offset = irand(max_offset) * ggml_element_size(x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_1d(ctx0, x[0], x[1], offset)); - - check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // set_2d - { - srand(seed); - int64_t ne2[4]; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 1; - for (int ndims = 2; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 2); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 2); - } - - x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - const int offset = offsets[0] + offsets[1]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_2d(ctx0, x[0], x[1], x[1]->nb[1], offset)); - - check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_1d - { - srand(seed); - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int k0 = irand(ggml_nelements(x[0])); - const int k1 = irand(ggml_nelements(x[0])); - const int i0 = MIN(k0, k1); - const int i1 = MAX(k0, k1); - - const int offset = i0 * sizeof(float); - const int nelem = i1 - i0; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_1d(ctx0, x[0], nelem, offset)); - - check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_2d - { - srand(seed); - int64_t ne2[4]; - int64_t nb2[4]; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - get_random_dims(ne2, 2); - while (ne2[0]*ne2[1] > ggml_nelements(x[0])) { - get_random_dims(ne2, 2); - } - const int count = ne2[0]*ne2[1]; - - nb2[0] = sizeof(float); - nb2[1] = nb2[0]*ne2[0]; - - ggml_set_param(ctx0, x[0]); - - const int max_offset = ggml_nelements(x[0]) - count; - const int offset = irand(max_offset+1) * sizeof(float); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_2d(ctx0, x[0], ne2[0], ne2[1], nb2[1], offset)); - - check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_3d - { - srand(seed); - int64_t ne2[4] = {1,1,1,1}; - int64_t nb2[4] = {0,0,0,0}; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - get_random_dims(ne2, 3); - while (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0])) { - get_random_dims(ne2, 3); - } - const int count = ne2[0]*ne2[1]*ne2[2]; - - nb2[0] = sizeof(float); - nb2[1] = nb2[0]*ne2[0]; - nb2[2] = nb2[1]*ne2[1]; - - ggml_set_param(ctx0, x[0]); - - const int max_offset = ggml_nelements(x[0]) - count; - const int offset = irand(max_offset+1) * sizeof(float); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_3d(ctx0, x[0], ne2[0], ne2[1], ne2[2], nb2[1], nb2[2], offset)); - - check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // permute - { - srand(seed); - int64_t ne2[4]; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) - { - // ggml_permute will set axes of dimensions below n_dims to 1. - // to make ggml_permute work correctly on all axes, - // the input tensor needs maximal n_dim of 4. - for (int i=0; i finite differences should not work - // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0) - struct ggml_tensor * f = ggml_sum(ctx0, - ggml_log(ctx0, - ggml_add1(ctx0, - ggml_scale(ctx0, - ggml_soft_max(ctx0, x[0]), - 1.0f - eps), - ggml_new_f32(ctx0, eps)))); - - check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY, {}); - // NOTE: softmax forward is computed using f16 table lookup instead of using actual expf, but backward assumes actual expf. - // this may result in different gradients too finite differences. - // when this test reports errors, first try to replace the table lookup with actual expf and test again to see if just that was the cause. - // if only the table lookup causes gradients to differ this is acceptable. - } - } - - // cross_entropy_loss - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - - for (int ndims = 1; ndims <= 4; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f); - // the second argument to cross_entropy_loss must sum up to 1 for each row - int nr = ggml_nrows(x[1]); - int nc = ggml_nelements(x[1]) / nr; - for (int ir = 0; ir < nr; ++ir) { - float sum = 0; - for (int ic = 0; ic < nc; ++ic) { - sum += ((float *) x[1]->data)[ic + ir*nc]; - } - for (int ic = 0; ic < nc; ++ic) { - ((float *) x[1]->data)[ic + ir*nc] /= sum; - } - } - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]); - - check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // rope f32 - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] += ne2[0] % 2; - int n_rot = ne2[0]; - - for (int ndims = 3; ndims <= 4; ++ndims) { - for (int mode = 0; mode < 4; ++mode) { - for (int n_past = 1; n_past < ne2[2]; ++n_past) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - - struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); - for (int i = 0; i < ne2[2]; ++i) { - ((int32_t *) p->data)[i] = n_past + i; - } - - ggml_set_param(ctx0, x[0]); - - const bool skip_past = (mode & 1); - if (skip_past) { - // we have no past, so this would have to work on uninitialized memory. - // we only test the gradients here; - // skip_past should have no influence on gradient computation. - // so when other modes work, we assume that this does as well. - continue; - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode)); - - GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); - check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {}); - } - } - } - } - - // rope f16 - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] += ne2[0] % 2; - int n_rot = ne2[0]; - - for (int ndims = 3; ndims <= 4; ++ndims) { - for (int mode = 0; mode < 4; ++mode) { - for (int n_past = 1; n_past < ne2[2]; ++n_past) { - x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f); - - struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); - for (int i = 0; i < ne2[2]; ++i) { - ((int32_t *) p->data)[i] = n_past + i; - } - - ggml_set_param(ctx0, x[0]); - - const bool skip_past = (mode & 1); - if (skip_past) { - // we have no past, so this would have to work on uninitialized memory. - // we only test the gradients here; - // skip_past should have no influence on gradient computation. - // so when other modes work, we assume that this does as well. - continue; - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode)); - - GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); - check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {}); - } - } - } - } - - // im2col f32 - { - srand(seed); - const int nargs = 1; - const int ndims = 4; - - for (const bool is_2D : {false, true}) { - int64_t ne0[ndims]; - int64_t ne1[ndims]; - get_random_dims(ne0, ndims); - get_random_dims(ne1, ndims); - - // // Ensure that the output is not zero-sized: - ne1[0] += 8; - ne1[1] += 8; - - if (is_2D) { - ne1[2] = ne0[2]; - } else { - ne1[1] = ne0[1]; - ne0[3] = 1; - ne1[3] = 1; - } - - // The order of arguments is swapped because the first tensor is only used for its shape. - x[1] = get_random_tensor_f16(ctx0, ndims, ne0, -1.0f, 1.0f); - x[0] = get_random_tensor_f32(ctx0, ndims, ne1, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int s0 = 1 + irand(2); - const int s1 = is_2D ? 1 + irand(2) : 0; - const int p0 = 0 + irand(2); - const int p1 = is_2D ? 0 + irand(2) : 0; - const int d0 = 1 + irand(2); - const int d1 = is_2D ? 1 + irand(2) : 0; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_im2col(ctx0, x[1], x[0], s0, s1, p0, p1, d0, d1, is_2D, GGML_TYPE_F32)); - - GGML_PRINT_DEBUG("im2col f32: is_2D=%s, s0=%d, s1=%d, p0=%d, p1=%d, d0=%d, d1=%d\n", is_2D ? "yes" : "no", s0, s1, p0, p1, d0, d1); - check_gradient("im2col f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {}); - } - } - - // pool_2d f32 - { - srand(seed); - const int nargs = 1; - const int ndims = 4; - - for (const enum ggml_op_pool op : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { - int64_t ne0[ndims]; - get_random_dims(ne0, ndims); - - ne0[0] += 8; - ne0[1] += 8; - - x[0] = get_random_tensor_f32(ctx0, ndims, ne0, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int k0 = 2 + irand(2); - const int k1 = 2 + irand(2); - const int s0 = 2 + irand(2); - const int s1 = 2 + irand(2); - const int p0 = 0 + irand(2); - const int p1 = 0 + irand(2); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_pool_2d(ctx0, x[0], op, k0, k1, s0, s1, p0, p1)); - - GGML_PRINT_DEBUG("ggml_pool_2d f32: op=%s k0=%d, k1=%d, s0=%d, s1=%d, p0=%d, p1=%d\n", - op == GGML_OP_POOL_MAX ? "max" : "avg", k0, k1, s0, s1, p0, p1); - std::vector expected_vals; - if (op == GGML_OP_POOL_MAX) { - expected_vals.push_back(0.0); - expected_vals.push_back(1.0); - } - check_gradient("ggml_pool_2d f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, expected_vals); - } - } - - // flash_attn f32 - // TODO: adapt to ggml_flash_attn_ext() changes - //{ - // srand(seed); - // const int nargs = 3; - - // int64_t ne2[4]; - - // get_random_dims(ne2, 4); - // int64_t D = ne2[0]; - // int64_t N = ne2[1]; - // int64_t M = ne2[2] + N; - // int64_t B = ne2[3]; - - // for (int masked = 0; masked <= 1; ++masked) { - // for (int ndims = 2; ndims <= 4; ++ndims) { - // int max_nrep = (ndims >= 3) ? 2 : 1; - // for (int nrep = 1; nrep < max_nrep; ++nrep) { - // int64_t neq[4] = { D, N, B*nrep, ne[3] }; - // int64_t nek[4] = { D, M, B, ne[3] }; - // int64_t nev[4] = { M, D, B, ne[3] }; - // if (ndims == 2) { - // neq[2] = 1; neq[3] = 1; - // nek[2] = 1; nek[3] = 1; - // nev[2] = 1; nev[3] = 1; - // } else if (ndims == 3) { - // neq[3] = 1; - // nek[3] = 1; - // nev[3] = 1; - // } - // x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); - // x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); - // x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); - // ggml_set_param(ctx0, x[0]); - // ggml_set_param(ctx0, x[1]); - // ggml_set_param(ctx0, x[2]); - - // struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - - // check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY, {}); - // } - // } - // } - //} - - ggml_free(ctx0); - } - - return 0; -} diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 3a89598a82..9d2db91f52 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -696,7 +696,7 @@ static void test_all(const std::string & lang, std::function -#include -#include -#include +#include +#include +#include +#include +#include -#define MAX_NARGS 2 - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wdouble-promotion" -#endif - -// -// logging -// -#define GGML_DEBUG 0 -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - -#define GGML_PRINT(...) printf(__VA_ARGS__) - - -static float frand(void) { - return (float)rand()/(float)RAND_MAX; +static bool almost_equal(const double a, const double b, const double atol) { + return fabs(a - b) < atol; } -static struct ggml_tensor * get_random_tensor( - struct ggml_context * ctx0, int ndims, int64_t ne[], float fmin, float fmax -) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne); +constexpr int64_t ne_datapoint = 2; +constexpr int64_t ne_label = 1; +constexpr int64_t ndata = 6; - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin; +struct helper_ctx_data { + std::vector datasets_supervised; + std::vector data_batch; + std::vector labels_batch; + + ggml_opt_dataset_t dataset_unsupervised; + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + struct ggml_opt_params opt_params; + ggml_opt_context_t opt_ctx; + struct ggml_tensor * inputs; + struct ggml_tensor * weights; + struct ggml_tensor * outputs; + ggml_backend_buffer_t buf; + ggml_opt_result_t result; + ggml_opt_result_t result2; +}; + +// These default values make it easier to check optimization results vs. expected values. +static ggml_opt_optimizer_params helper_get_test_opt_pars(void * userdata) { + ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata); + result.adamw.alpha = 1.0f; + result.adamw.beta1 = 0.0f; + result.adamw.beta2 = 0.0f; + result.adamw.eps = 0.0f; + return result; +} + +static helper_ctx_data helper_get_ctx_data( + ggml_backend_sched_t backend_sched, + ggml_backend_t backend, + const bool init_opt_ctx = true, + const bool optimizer_defaults = true, + int64_t nbatch_logical = 1, + int64_t nbatch_physical = 1, + enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) { + std::vector datasets(ndata); + for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) { + ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard); + + float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); + float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); + + for (int64_t idata = 0; idata < ndata; ++idata) { + for (int64_t id = 0; id < ne_datapoint; ++id) { + data[ idata*ne_datapoint + id] = 16*idata + id; } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } + for (int64_t il = 0; il < ne_label; ++il) { + labels[idata*ne_label + il] = 16*(16*idata + il); } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; + } + + datasets[ndata_shard-1] = dataset; + } + + ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1); + + float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised)); + + for (int64_t idata = 0; idata < ndata; ++idata) { + data[idata] = idata; + } + + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + { + struct ggml_init_params params = { + /*.mem_size =*/ (2*ndata + 2)*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_static = ggml_init(params); + } + { + struct ggml_init_params params = { + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_compute = ggml_init(params); + } + + std::vector data_batch(ndata); + std::vector labels_batch(ndata); + for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) { + data_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_datapoint); + labels_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_label); + } + + struct ggml_tensor * inputs = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, nbatch_physical); + ggml_set_name(inputs, "inputs"); + + struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(weights, "weights"); + ggml_set_param(ctx_static, weights); + + struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights); + + struct ggml_tensor * outputs = ggml_scale(ctx_compute, intermediary, 1.0f); + ggml_set_name(outputs, "outputs"); + + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend); + const float w0 = float(ndata)/2; + ggml_backend_tensor_set(weights, &w0, 0, sizeof(float)); + + GGML_ASSERT(nbatch_logical % nbatch_physical == 0); + const int32_t opt_period = nbatch_logical / nbatch_physical; + + struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); + opt_params.opt_period = opt_period; + if (!optimizer_defaults) { + opt_params.get_opt_pars = helper_get_test_opt_pars; + } + ggml_opt_context_t opt_ctx = init_opt_ctx ? ggml_opt_init(opt_params) : nullptr; + + ggml_opt_result_t result = ggml_opt_result_init(); + ggml_opt_result_t result2 = ggml_opt_result_init(); + + return {datasets, data_batch, labels_batch, dataset_unsupervised, ctx_static, ctx_compute, opt_params, opt_ctx, inputs, weights, outputs, buf, result, result2}; +} + +static void helper_free_ctx_data(struct helper_ctx_data ctx_data) { + ggml_opt_result_free(ctx_data.result); + ggml_opt_result_free(ctx_data.result2); + ggml_opt_free(ctx_data.opt_ctx); + ggml_backend_buffer_free(ctx_data.buf); + ggml_free(ctx_data.ctx_static); + ggml_free(ctx_data.ctx_compute); + for (ggml_opt_dataset_t dataset : ctx_data.datasets_supervised) { + ggml_opt_dataset_free(dataset); + } + ggml_opt_dataset_free(ctx_data.dataset_unsupervised); +} + +static void helper_after_test( + const char * func, const bool high_level, const std::string options, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + printf(" %s(high_level=%s%s, subtest=%s): ", + func, high_level ? "yes" : "no", options.c_str(), subtest.c_str()); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; +} + +static std::pair test_dataset(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool shuffle) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend); + + for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) { + ggml_opt_dataset_t dataset = cd.datasets_supervised[ndata_shard-1]; + + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + + for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) { + if (ndata_batch % ndata_shard != 0) { + continue; + } + bool subtest_ok = true; + + struct ggml_tensor * data_batch = cd.data_batch[ndata_batch-1]; + struct ggml_tensor * labels_batch = cd.labels_batch[ndata_batch-1]; + + std::vector data(ggml_nelements( data_batch)); + std::vector labels(ggml_nelements(labels_batch)); + + std::vector idata_shuffled; + const int64_t nbatches = ndata / ndata_batch; + for (int64_t ibatch = 0; ibatch < nbatches; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, data_batch, labels_batch, ibatch); + + ggml_backend_tensor_get( data_batch, data.data(), 0, ggml_nbytes( data_batch)); + ggml_backend_tensor_get(labels_batch, labels.data(), 0, ggml_nbytes(labels_batch)); + + for (int64_t idata_batch = 0; idata_batch < ndata_batch; ++idata_batch) { + const int64_t idata = ibatch*ndata_batch + idata_batch; + const int64_t idata_found = data[idata_batch*ne_datapoint] / 16; + subtest_ok = subtest_ok && (shuffle || idata_found == idata); + idata_shuffled.push_back(idata_found); + + for (int64_t id = 0; id < ne_datapoint; ++id) { + if (data[ idata_batch*ne_datapoint + id] != 16*idata_found + id) { + subtest_ok = false; + } } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; + for (int64_t il = 0; il < ne_label; ++il) { + if (labels[idata_batch*ne_label + il] != 16*(16*idata_found + il)) { + subtest_ok = false; } } } } - break; - default: - assert(false); + + if (!shuffle || ndata % ndata_batch == 0) { + const int ndata_max = (ndata / ndata_batch) * ndata_batch; + + for (int64_t idata = 0; subtest_ok && idata < ndata_max; ++idata) { + int ninstances = 0; + for (int64_t id : idata_shuffled) { + ninstances += id == idata; + } + if (ninstances != 1) { + subtest_ok = false; + } + } + } + + printf(" %s(shuffle=%s, ndata_shard=%" PRId64 ", ndata_batch=%" PRId64 "): ", + __func__, shuffle ? "yes" : "no", ndata_shard, ndata_batch); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } } + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static std::pair test_grad(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, + /*nbatch_logical =*/ 999999, /*nbatch_physical =*/ 1); + + std::vector grad_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + grad_history[idata] = NAN; + } + + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float)); + } + + { + bool subtest_ok = true; + for (int idata = 0; idata < ndata; ++idata) { + if (grad_history[idata] != idata + 1) { + subtest_ok = false; + } + } + printf(" %s(): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_forward_backward( + const char * func, const bool high_level, const bool shuffle, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", shuffle="; + options += shuffle ? "yes" : "no"; + helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_forward_backward( + ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level, const bool shuffle) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + + std::vector loss_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + const bool subtest_ok = ndata == 0 && loss == 0.0 && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc); + helper_after_test_forward_backward(__func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass); + } + + if (high_level) { + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + ggml_opt_epoch(cd.opt_ctx, dataset, nullptr, cd.result, 0, nullptr, nullptr); + } else { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == ndata/2; + helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass); + } + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + bool subtest_ok = ndata == 6; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 33.0 && almost_equal(loss_unc, sqrt(3.5), 1e-10); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_forward_backward(__func__, high_level, shuffle, "results_after_forward", subtest_ok, ntest, npass); + } + + float w0; + ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float)); + for (int i = 0; i < 10; ++i) { + ggml_opt_forward_backward(cd.opt_ctx, nullptr); + } + ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float)); + + ggml_opt_reset(cd.opt_ctx, /*optimizer =*/ false); + ggml_opt_result_reset(cd.result); + + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + if (high_level) { + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr); + } else { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == -ndata/2; + helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass); + } + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + bool subtest_ok = ndata == 6; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 18.0 && (shuffle || loss_unc == 0.0); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_forward_backward(__func__, high_level, shuffle, "result_after_forward_backward", subtest_ok, ntest, npass); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static std::pair test_epoch_vs_fit(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; + + float weights_epoch; + float weights_fit; + + { + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true); + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr); + + ggml_backend_tensor_get(cd.weights, &weights_epoch, 0, ggml_nbytes(cd.weights)); + helper_free_ctx_data(cd); + } + { + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ false); + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + + ggml_opt_fit(backend_sched, cd.ctx_compute, cd.inputs, cd.outputs, dataset, + GGML_OPT_LOSS_TYPE_SUM, ggml_opt_get_default_optimizer_params, 1, 1, 0.0f, true); + + ggml_backend_tensor_get(cd.weights, &weights_fit, 0, ggml_nbytes(cd.weights)); + helper_free_ctx_data(cd); + } + + const bool subtest_ok = weights_epoch == weights_fit; + + printf(" %s(): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_idata_split( + const char * func, const bool high_level, const int epoch, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", epoch="; + options += std::to_string(epoch); + helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_idata_split(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + const int idata_split = ndata * 2/3; + + std::vector loss_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + for (int epoch = 1; epoch <= 4; ++epoch) { + if (high_level) { + ggml_opt_epoch(cd.opt_ctx, cd.dataset_unsupervised, cd.result, cd.result2, idata_split, nullptr, nullptr); + } else { + int idata = 0; + for (; idata < idata_split; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + for (; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward(cd.opt_ctx, cd.result2); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == ndata/2 - epoch*idata_split; + helper_after_test_idata_split(__func__, high_level, epoch, "weights", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result, &ndata_result); + bool subtest_ok = ndata_result == idata_split; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 28.0 - epoch*16.0 && loss_unc == 0.0; + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_idata_split(__func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result2, &ndata_result); + bool subtest_ok = ndata_result == ndata - idata_split; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result2, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 15.0 - epoch*8 && almost_equal(loss_unc, sqrt(0.5), 1e-10); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result2, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_idata_split(__func__, high_level, epoch, "results_forward", subtest_ok, ntest, npass); + } + + ggml_opt_result_reset(cd.result); + ggml_opt_result_reset(cd.result2); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_gradient_accumulation( + const char * func, const int nbatch_physical, const enum ggml_opt_loss_type loss_type, const int epoch, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", nbatch_physical="; + options += std::to_string(nbatch_physical); + options += ", loss_type="; + options += loss_type == GGML_OPT_LOSS_TYPE_MEAN ? "mean" : "sum"; + options += ", epoch="; + options += std::to_string(epoch); + helper_after_test(func, false, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_gradient_accumulation( + ggml_backend_sched_t backend_sched, ggml_backend_t backend, const int32_t nbatch_physical, const enum ggml_opt_loss_type loss_type) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data( + backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + + std::vector grad_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + grad_history[idata] = NAN; + } + + for (int epoch = 1; epoch <= 4; ++epoch) { + if (nbatch_physical == 1) { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float)); + } + } else if (nbatch_physical == 2) { + for (int idata = 0; idata < ndata; idata += 2) { + const float idataf[2] = {float(idata + 0), float(idata + 1)}; + ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + + grad_history[idata + 0] = 0.0f; + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float)); + } + } else { + GGML_ASSERT(false); + } + + { + GGML_ASSERT(ndata == 6); + constexpr double atol = 1e-6; + bool subtest_ok = true; + if (loss_type == GGML_OPT_LOSS_TYPE_SUM) { + if (nbatch_physical == 1) { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0, atol); + } else { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0, atol); + } + subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol); + } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) { + if (nbatch_physical == 1) { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0/ndata, atol); + } else { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0/ndata, atol); + } + subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol); + } else { + GGML_ASSERT(false); + } + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "grads", subtest_ok, ntest, npass); + } + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == (ndata/2) - epoch; + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result, &ndata_result); + bool subtest_ok = ndata_result == ndata/nbatch_physical; + + double loss; + ggml_opt_result_loss(cd.result, &loss, /*loss_unc =*/ nullptr); + if (loss_type == GGML_OPT_LOSS_TYPE_SUM) { + subtest_ok = subtest_ok && loss == (39.0 - epoch*6.0); + } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) { + subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, 1e-6); + } else { + GGML_ASSERT(false); + } + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "results", subtest_ok, ntest, npass); + } + + ggml_opt_result_reset(cd.result); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static ggml_opt_optimizer_params helper_get_regression_opt_pars(void * userdata) { + ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata); + result.adamw.alpha = 0.1f; return result; } -int main(void) { - struct ggml_init_params params = { - /* .mem_size = */ 1024*1024*1024, - /* .mem_buffer = */ NULL, - /* .no_alloc = */ false, - }; +static std::pair test_regression(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; - struct ggml_context * ctx = ggml_init(params); + // Test for simple regression with f(x) = a*x + b - int64_t ne1[4] = {4, 128, 1, 1}; - int64_t ne2[4] = {4, 256, 1, 1}; - int64_t ne3[4] = {128, 256, 1, 1}; + constexpr int64_t ndata_regression = 201; + constexpr float a_true = 1.2f; + constexpr float b_true = 3.4f; - struct ggml_tensor * a = get_random_tensor(ctx, 2, ne1, -1, +1); - struct ggml_tensor * b = get_random_tensor(ctx, 2, ne2, -1, +1); - ggml_set_param(ctx, a); - ggml_set_param(ctx, b); + std::mt19937 gen(12345); + std::normal_distribution nd{0.0f, 0.1f}; - struct ggml_tensor * c = get_random_tensor(ctx, 2, ne3, -1, +1); + ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression); - struct ggml_tensor * ab = ggml_mul_mat(ctx, a, b); - struct ggml_tensor * d = ggml_sub(ctx, c, ab); - struct ggml_tensor * e = ggml_sum(ctx, ggml_sqr(ctx, d)); + float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); + float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); - struct ggml_cgraph * ge = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true); - ggml_build_forward_expand(ge, e); - ggml_graph_reset(ge); + constexpr float x_min = -100.0f; + constexpr float x_max = 100.0f; - ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); + for (int64_t idata = 0; idata < ndata_regression; ++idata) { + const float x = x_min + (x_max - x_min) * idata/(ndata_regression-1); + const float y = a_true*x + b_true + nd(gen); - const float fe = ggml_get_f32_1d(e, 0); - printf("%s: e = %.4f\n", __func__, fe); + data[idata] = x; + labels[idata] = y; + } - struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + { + struct ggml_init_params params = { + /*.mem_size =*/ 3*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_static = ggml_init(params); + } + { + struct ggml_init_params params = { + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_compute = ggml_init(params); + } - ggml_opt(ctx, opt_params, e); + // The first dimension is the dimension of the datapoints, the second dimension is the number of datapoints. + struct ggml_tensor * x = ggml_new_tensor_2d(ctx_static, GGML_TYPE_F32, 1, ndata_regression); + ggml_set_name(x, "x"); - ggml_graph_reset(ge); + struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(a, "a"); + ggml_set_param(ctx_static, a); - ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(b, "b"); + ggml_set_param(ctx_static, b); - const float fe_opt = ggml_get_f32_1d(e, 0); - printf("%s: original e = %.4f\n", __func__, fe); - printf("%s: optimized e = %.4f\n", __func__, fe_opt); + struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b); + ggml_set_name(f, "f"); + ggml_set_param(ctx_static, f); - const bool success = (fe_opt <= fe); - assert(success); + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend); + const float a0 = 1.0f; + const float b0 = 3.0f; + ggml_backend_tensor_set(a, &a0, 0, sizeof(float)); + ggml_backend_tensor_set(b, &b0, 0, sizeof(float)); - ggml_free(ctx); - return success ? 0 : -1; + ggml_opt_fit(backend_sched, ctx_compute, x, f, dataset, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, + helper_get_regression_opt_pars, 100, ndata_regression, 0.0f, true); + + { + float a_fit; + ggml_backend_tensor_get(a, &a_fit, 0, sizeof(float)); + float b_fit; + ggml_backend_tensor_get(b, &b_fit, 0, sizeof(float)); + const bool subtest_ok = almost_equal(a_fit, a_true, 1e-2) && almost_equal(b_fit, b_true, 1e-2); + printf(" %s(subtest=weights): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + ggml_backend_buffer_free(buf); + ggml_free(ctx_static); + ggml_opt_dataset_free(dataset); + + return std::make_pair(npass, ntest); } -// int64_t ne1[4] = {4, 128, 1, 1}; -// int64_t ne2[4] = {4, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 25890.9375 -// main: optimized e = 10094.7031 -// int64_t ne1[4] = {8, 128, 1, 1}; -// int64_t ne2[4] = {8, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 39429.5078 -// main: optimized e = 9275.8936 +static std::pair test_backend(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int npass = 0; + int ntest = 0; -// int64_t ne1[4] = {16, 128, 1, 1}; -// int64_t ne2[4] = {16, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 68371.1328 -// main: optimized e = 7854.4502 + for (bool shuffle : {false, true}) { + std::pair partial = test_dataset(backend_sched, backend, shuffle); + npass += partial.first; + ntest += partial.second; + } + { + std::pair partial = test_grad(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } + for (bool high_level : {false, true}){ + for (bool shuffle : {false, true}) { + if (!high_level && shuffle) { + continue; + } + std::pair partial = test_forward_backward(backend_sched, backend, high_level, shuffle); + npass += partial.first; + ntest += partial.second; + } + } + { + std::pair partial = test_epoch_vs_fit(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } + for (bool high_level : {false, true}){ + std::pair partial = test_idata_split(backend_sched, backend, high_level); + npass += partial.first; + ntest += partial.second; + } + for (int32_t nbatch_physical : {2, 1}) { + for (enum ggml_opt_loss_type loss_type : {GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_MEAN}) { + std::pair partial = test_gradient_accumulation(backend_sched, backend, nbatch_physical, loss_type); + npass += partial.first; + ntest += partial.second; + } + } + { + std::pair partial = test_regression(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } -// int64_t ne1[4] = {32, 128, 1, 1}; -// int64_t ne2[4] = {32, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 126061.1953 -// main: optimized e = 5451.0166 + return std::make_pair(npass, ntest); +} -// int64_t ne1[4] = {4, 1024, 1, 1}; -// int64_t ne2[4] = {4, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 1620817.8750 -// main: optimized e = 698387.6875 +int main(void) { + const size_t dev_count = ggml_backend_dev_count(); + printf("Testing %zu devices\n\n", dev_count); + size_t n_ok = 0; -// another run on M1 -// int64_t ne1[4] = {4, 1024, 1, 1}; -// int64_t ne2[4] = {4, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 1629595.6250 -// main: optimized e = 698169.1250 + std::vector devs; + std::vector backends; -// int64_t ne1[4] = {32, 1024, 1, 1}; -// int64_t ne2[4] = {32, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 8146770.5000 -// main: optimized e = 651119.1250 + for (size_t i = 0; i < dev_count; ++i) { + devs.push_back(ggml_backend_dev_get(i)); + + ggml_backend_t backend = ggml_backend_dev_init(devs[i], NULL); + GGML_ASSERT(backend != NULL); + + if (ggml_backend_is_cpu(backend)) { + ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2); + } + + backends.push_back(backend); + } + + for (size_t i = 0; i < dev_count; ++i) { + // Put the backend to be tested in front so that it's prioritized: + std::vector backends_modded = {backends[i]}; + backends_modded.insert(backends_modded.end(), backends.begin(), backends.end()); + + ggml_backend_sched_t backend_sched = ggml_backend_sched_new( + backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false); + + printf("Backend %zu/%zu: %s\n", i + 1, dev_count, ggml_backend_dev_name(devs[i])); + printf(" Device description: %s\n", ggml_backend_dev_description(devs[i])); + size_t free, total; // NOLINT + ggml_backend_dev_memory(devs[i], &free, &total); + printf(" Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024); + printf("\n"); + + std::pair result = test_backend(backend_sched, backends[i]); + + printf(" %d/%d tests passed\n", result.first, result.second); + printf(" Backend %s: ", ggml_backend_name(backends[i])); + if (result.first == result.second) { + printf("\033[1;32mOK\033[0m\n"); + n_ok++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + + printf("\n"); + + ggml_backend_sched_free(backend_sched); + } + + for (ggml_backend_t backend : backends) { + ggml_backend_free(backend); + } + + printf("%zu/%zu backends passed\n", n_ok, dev_count); + if (n_ok != dev_count) { + printf("\033[1;31mFAIL\033[0m\n"); + return 1; + } + printf("\033[1;32mOK\033[0m\n"); + return 0; +} diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index d50417ba01..8d0bf0470f 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -1,6 +1,7 @@ // Unit tests for quantization specific functions - quantize, dequantize and dot product #include "ggml.h" +#include "ggml-cpu.h" #undef NDEBUG #include @@ -44,22 +45,23 @@ static float array_rmse(const float * a1, const float * a2, size_t n) { } // Total quantization error on test data -static float total_quantization_error(const ggml_type_traits * qfns, size_t test_size, const float * test_data) { +static float total_quantization_error(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); - qfns->from_float(test_data, tmp_q.data(), test_size); + qfns_cpu->from_float(test_data, tmp_q.data(), test_size); qfns->to_float(tmp_q.data(), tmp_out.data(), test_size); return array_rmse(test_data, tmp_out.data(), test_size); } // Total quantization error on test data -static float reference_quantization_error(const ggml_type_traits * qfns, size_t test_size, const float * test_data) { +static float reference_quantization_error(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); std::vector tmp_out_ref(test_size); - qfns->from_float(test_data, tmp_q.data(), test_size); + // FIXME: why is done twice? + qfns_cpu->from_float(test_data, tmp_q.data(), test_size); qfns->to_float(tmp_q.data(), tmp_out.data(), test_size); qfns->from_float_ref(test_data, tmp_q.data(), test_size); @@ -78,18 +80,18 @@ static float dot_product(const float * a1, const float * a2, size_t test_size) { // Total dot product error static float dot_product_error( - const ggml_type_traits * qfns, size_t test_size, const float * test_data1, const float *test_data2 + const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data1, const float *test_data2 ) { std::vector tmp_q1(2*test_size); std::vector tmp_q2(2*test_size); - const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type); - qfns->from_float(test_data1, tmp_q1.data(), test_size); + qfns_cpu->from_float(test_data1, tmp_q1.data(), test_size); vdot->from_float(test_data2, tmp_q2.data(), test_size); float result = INFINITY; - qfns->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1); + qfns_cpu->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1); const float dot_ref = dot_product(test_data1, test_data2, test_size); @@ -132,6 +134,7 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); // deprecated - skip if (qfns->blck_size == 0) { @@ -143,8 +146,8 @@ int main(int argc, char * argv[]) { printf("Testing %s\n", ggml_type_name((ggml_type) i)); ggml_quantize_init(ei); - if (qfns->from_float && qfns->to_float) { - const float total_error = total_quantization_error(qfns, test_size, test_data.data()); + if (qfns_cpu->from_float && qfns->to_float) { + const float total_error = total_quantization_error(qfns, qfns_cpu, test_size, test_data.data()); const float max_quantization_error = type == GGML_TYPE_TQ1_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY : type == GGML_TYPE_TQ2_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY : @@ -159,14 +162,14 @@ int main(int argc, char * argv[]) { printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error); } - const float reference_error = reference_quantization_error(qfns, test_size, test_data.data()); + const float reference_error = reference_quantization_error(qfns, qfns_cpu, test_size, test_data.data()); failed = !(reference_error < MAX_QUANTIZATION_REFERENCE_ERROR); num_failed += failed; if (failed || verbose) { printf("%5s reference implementation error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], reference_error); } - const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data()); + const float vec_dot_error = dot_product_error(qfns, qfns_cpu, test_size, test_data.data(), test_data2.data()); const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S ? MAX_DOT_PRODUCT_ERROR_LOWBIT diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index bdbdd90a8d..2882884938 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -1,12 +1,12 @@ // Benchmark quantization specific functions on synthetic data #include "ggml.h" +#include "ggml-cpu.h" #undef NDEBUG #include #include #include -#include #include #include #include @@ -122,9 +122,10 @@ static void usage(char * argv[]) { printf(" --type TYPE set test type as"); for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - const auto * qfns = ggml_get_type_traits(type); + const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); if (ggml_type_name(type) != NULL) { - if (qfns->from_float && qfns->to_float) { + if (qfns_cpu->from_float && qfns->to_float) { printf(" %s", ggml_type_name(type)); } } @@ -271,11 +272,12 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { continue; } - if (qfns->from_float && qfns->to_float) { + if (qfns_cpu->from_float && qfns->to_float) { printf("%s\n", ggml_type_name(type)); ggml_quantize_init(type); @@ -299,7 +301,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { - qfns->from_float(test_data1, test_q1, size); + qfns_cpu->from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = ggml_row_size(type, size); @@ -310,7 +312,7 @@ int main(int argc, char * argv[]) { if (params.op_dequantize_row_q) { printf(" dequantize_row_q\n"); - qfns->from_float(test_data1, test_q1, largest); + qfns_cpu->from_float(test_data1, test_q1, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { @@ -328,7 +330,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { - const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type); vdot->from_float(test_data1, test_q1, size); return test_q1[0]; }; @@ -340,13 +342,13 @@ int main(int argc, char * argv[]) { if (params.op_vec_dot_q) { printf(" vec_dot_q\n"); - qfns->from_float(test_data1, test_q1, largest); - qfns->from_float(test_data2, test_q2, largest); + qfns_cpu->from_float(test_data1, test_q1, largest); + qfns_cpu->from_float(test_data2, test_q2, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { float result; - qfns->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1); + qfns_cpu->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1); return result; }; size_t quantized_size = ggml_row_size(type, size); diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp index 246bb227d1..4656b30f09 100644 --- a/tests/test-rope.cpp +++ b/tests/test-rope.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "ggml-cpu.h" #include #include diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 6e021c4c70..be370044d6 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -10,6 +10,8 @@ #include #include +extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector>& seq_breakers); + static void dump(const llama_token_data_array * cur_p) { for (size_t i = 0; i < cur_p->size; i++) { printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); @@ -18,181 +20,188 @@ static void dump(const llama_token_data_array * cur_p) { #define DUMP(__cur_p) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__cur_p)); printf("-\n"); } while(0) -#define APPLY(__cnstr, __cur_p) do { \ - auto * cnstr = (__cnstr); \ - llama_sampler_apply(cnstr, (__cur_p)); \ - llama_sampler_free(cnstr); \ -} while(0) +struct sampler_tester { + sampler_tester(size_t n_vocab) { + cur.reserve(n_vocab); + for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { + const float logit = logf(token_id); + cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); + } -static void test_top_k(const std::vector & probs, const std::vector & expected_probs, int k) { - const size_t n_vocab = probs.size(); + cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false }; + } + + sampler_tester(const std::vector & probs, const std::vector & probs_expected) : probs_expected(probs_expected) { + cur.reserve(probs.size()); + for (llama_token token_id = 0; token_id < (llama_token)probs.size(); token_id++) { + const float logit = logf(probs[token_id]); + cur.emplace_back(llama_token_data{token_id, logit, probs[token_id]}); + } + + cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false }; + } + + void apply(llama_sampler * sampler) { + llama_sampler_apply(sampler, &cur_p); + llama_sampler_free(sampler); + } + + void check() { + GGML_ASSERT(cur_p.size == probs_expected.size()); + for (size_t i = 0; i < cur_p.size; i++) { + GGML_ASSERT(fabs(cur_p.data[i].p - probs_expected[i]) < 1e-5); + } + } + + llama_token_data_array cur_p; + +private: + const std::vector probs_expected; std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } +}; - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_top_k(k), &cur_p); - DUMP(&cur_p); +static void test_temp(const std::vector & probs, const std::vector & probs_expected, float temp) { + sampler_tester tester(probs, probs_expected); - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_temp(temp)); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); + + tester.check(); } -static void test_top_p(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_temp_ext(const std::vector & probs, const std::vector & probs_expected, float temp, float delta, float exponent) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_temp_ext(temp, delta, exponent)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_top_p(p, 1), &cur_p); - DUMP(&cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_tfs(const std::vector & probs, const std::vector & expected_probs, float z) { - const size_t n_vocab = probs.size(); +static void test_top_k(const std::vector & probs, const std::vector & probs_expected, int k) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_top_k(k)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_tail_free(z, 1), &cur_p); - DUMP(&cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_min_p(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_top_p(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_top_p(p, 1)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_min_p(p, 1), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_softmax(), &cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_min_p(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_min_p(p, 1)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_typical(p, 1), &cur_p); - DUMP(&cur_p); + tester.check(); +} - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } +static void test_xtc(const std::vector & probs, const std::vector & probs_expected, float p, float t) { + sampler_tester tester(probs, probs_expected); + + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_xtc(p, t, 0, 0)); + DUMP(&tester.cur_p); + + tester.check(); +} + +static void test_typical(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); + + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_typical(p, 1)); + DUMP(&tester.cur_p); + + tester.check(); } static void test_penalties( const std::vector & probs, const std::vector & last_tokens, - const std::vector & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence + const std::vector & probs_expected, float repeat_penalty, float alpha_frequency, float alpha_presence ) { - GGML_ASSERT(probs.size() == expected_probs.size()); + GGML_ASSERT(probs.size() == probs_expected.size()); + + sampler_tester tester(probs, probs_expected); const size_t n_vocab = probs.size(); - - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } - - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false); for (size_t i = 0; i < last_tokens.size(); i++) { llama_sampler_accept(sampler, last_tokens[i]); } - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(sampler, &cur_p); - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); + DUMP(&tester.cur_p); + tester.apply(sampler); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); + tester.check(); +} + +static void test_dry( + const std::vector & probs, const std::vector & last_tokens, + const std::vector & expected_probs, float dry_multiplier, float dry_base, + int dry_allowed_length, int dry_penalty_last_n, + const std::vector> & seq_breakers +) { + GGML_ASSERT(probs.size() == expected_probs.size()); + + sampler_tester tester(probs, expected_probs); + + auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers); + + for (size_t i = 0; i < last_tokens.size(); i++) { + llama_sampler_accept(sampler, last_tokens[i]); } + + DUMP(&tester.cur_p); + tester.apply(sampler); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); + tester.check(); } static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p ) { - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(token_id); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } - - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; + sampler_tester tester(n_vocab); llama_token min_token_id = 0; const llama_token max_token_id = n_vocab-1; for (auto s : samplers_sequence) { switch (s){ - case 'k': APPLY(llama_sampler_init_top_k(top_k), &cur_p); break; - case 'f': GGML_ABORT("tail_free test not implemented"); + case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break; case 'y': GGML_ABORT("typical test not implemented"); - case 'p': APPLY(llama_sampler_init_top_p(top_p, 1), &cur_p); break; - case 'm': APPLY(llama_sampler_init_min_p(min_p, 1), &cur_p); break; + case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break; + case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break; case 't': GGML_ABORT("temperature test not implemented"); default : GGML_ABORT("Unknown sampler"); } - APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests + tester.apply(llama_sampler_init_dist(0)); + + auto & cur_p = tester.cur_p; const int size = cur_p.size; @@ -263,7 +272,7 @@ static void bench(llama_sampler * cnstr, const char * cnstr_name, const std::vec } const int64_t t_end = ggml_time_us(); llama_sampler_free(cnstr); - printf("%-42s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter); + printf("%-43s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter); } #define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter)) @@ -279,26 +288,31 @@ static void test_perf() { data.emplace_back(llama_token_data{i, logit, 0.0f}); } - BENCH(llama_sampler_init_top_k (40), data, 32); - BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); - BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); - BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32); - BENCH(llama_sampler_init_typical (0.5f, 1), data, 32); - BENCH(llama_sampler_init_softmax (), data, 32); + BENCH(llama_sampler_init_top_k (40), data, 32); + BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); + BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); + BENCH(llama_sampler_init_typical(0.5f, 1), data, 32); + BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32); } int main(void) { ggml_time_init(); - test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1); - test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3); + test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f); + test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f); + + test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f, 0.0f, 1.0f); + test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f, 0.0f, 1.0f); + + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 0); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f}, 0.7f); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 0.8f); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f); @@ -309,9 +323,13 @@ int main(void) { test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f); + printf("XTC should:\n"); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.1f}, 0.99f, 0.09f); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.2f, 0.1f}, 0.99f, 0.19f); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.3f, 0.2f, 0.1f}, 0.99f, 0.29f); + + printf("XTC should not:\n"); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f); test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f); test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f); @@ -324,6 +342,13 @@ int main(void) { test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f); test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f); + + test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {}); + test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.296923f, 0.109232f}, 1.0f, 1.1f, 2, 5, {}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.241818f, 0.241818f, 0.032727f}, 2.0f, 1.1f, 2, 5, {}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {}); + test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f); test_sampler_queue(10000, "k", 1, 1.0f, 1.0f); test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);