* convert : use direct copies when possible
Using os.copy_file_range where available,
and falling back to shutil.copyfileobj otherwise.
* gguf : handle misaligned offset more cleanly
Applies to both local and remote safetensors custom parsing.
This matches the behavior of the official safetensors implementation.
* convert : rename from_safetensors_meta to from_local_tensor
For consistency with from_remote_tensor
* CUDA: Add mul_mat_id support the mmf
Add support for mul_mat_id for bs < 16
* Review: use warp_size, fix should_use_mmf condition
* Launch one block per expert, stride along n_expert_used
* templatize mul_mat_id
* Pad shmem to 16 bytes, add helper function mul_mat_f_switch_ids
* Reduce compile times by dividing mmf into f16, bf16 and f32 variants
* Divide mmf by ncols_dst
* Add missing files
* Fix MUSA/HIP builds
* requirements : update transformers/torch for Embedding Gemma
This commit updates the requirements to support converting
Embedding Gemma 300m models.
The motivation for this change is that during development I had a local
copy of the transformers package which is what I used for converting
the models. This was a mistake on my part and I should have also updated
my transformers version to the official release.
I had checked the requirements/requirements-convert_legacy_llama.txt
file and noted that the version was >=4.45.1,<5.0.0 and came to the
conculusion that no updated would be needed, this assumed that
Embedding Gemma would be in a transformers release at the time
Commit fb15d649ed ("llama : add support
for EmbeddingGemma 300m (#15798)) was merged. So anyone wanting to
convert themselves would be able to do so. However, Embedding Gemma is
a preview release and this commit updates the requirements to use this
preview release.
* resolve additional python dependencies
* fix pyright errors in tokenizer test and remove unused import
* feat: Extra debugging support for model conversion - added BF16 support for llama-callback-eval and support for dumping intermediate steps in run-org-model.py
* vulkan: sort graph to allow more parallel execution
Add a backend proc to allow the backend to modify the graph. The
vulkan implementation looks at which nodes depend on each other
and greedily reorders them to group together nodes that don't
depend on each other. It only reorders the nodes, doesn't change
the contents of any of them.
With #15489, this reduces the number of synchronizations needed.
* call optimize_graph per-split
* Add DeepSeek V3.1 thinking mode support
- Added COMMON_CHAT_FORMAT_DEEPSEEK_V3_1 enum value
- Created common_chat_params_init_deepseek_v3_1() function (currently uses R1 implementation)
- Created common_chat_parse_deepseek_v3_1() function that handles V3.1 thinking format:
- Extracts reasoning content before '</think>' tag into reasoning_content
- Extracts regular content after '</think>' tag into content
- No opening '<think>' tag in V3.1 format
- Added detection logic for V3.1 templates based on pattern: 'message['prefix'] is defined and message['prefix'] and thinking'
- Added V3.1 case to parsing switch statement
This addresses the issue where V3.1 outputs reasoning content followed by '</think>' and then regular content without the opening '<think>' tag.
* Another attempt by V3.1 non-thinking
* Fix test, but it's not asserting anything.
* Ignore vim swap files in tests dir
* Update the test
* Try using try_find_literal instead of regex
* passing test
* Revert "Try using try_find_literal instead of regex"
This reverts commit c50d887ec2.
* Remove unnecessary change
* Remove comment
* Add code to handle non-thinking mode.
* Try to set message['prefix'] when thinking is enabled.
* This fixes reasoning, but breaks normal content. We need state in the
chat parser.
* DeepSeek V3.1 thinking is now the default. Disable with `--reasoning-budget 0`.
* Simplify (DeepSeek V3.1 reasoning)
* Fix sign inversion bug
* Add some tool calling code (not working).
* Tool calls working in non-reasoning mode.
* Attempt a unit test for tool call parsing.
* Passing test
* Add tests for both happy path and broken fenced DeepSeek V3.1 tool call variants.
* Passing DeepSeek V3.1 tool call tests, but model is not working.
* Revert assistance response prefill change. Not my monkeys.
* Add fenced_thinking unit test variant. Passes, but thinking tool calling
still isn't working for some reason.
* Tests pass in reasoning mode. Also e2e tool test passes.
* Make a copy of the parse_json_tool_calls function for deepseek-v3.1 so
as to not accidentally introduce regressions.
* Fix thinking_forced_open logic. tool calling broken. Need to add another
test case.
* That's what I get for cargo culting a newline.
* Add multi tool call test for deepseek v3.1 non-reasoning
* Move test, remove .gitignore change
* Place deepseek-v3.1 reasoning test directly into existing reasoning
function per CISC's request.
* Address whitespace CI failure.
* Merge two assert_equals per CISC's request.
* Add DeepSeek-V3.1 tests to tests/test-chat.cpp per CISC's request.
* Merge deepseek V3.1 and regular parse_json_tool_calls() function
behaviors by adding optional update_cursor argument.
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* DeepSeek V3.1 fix reasoning_format none
* Strip grammar down to strictly what we expect based on model card. Throw
out parts we cargo culted from R1 that don't make sense.
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* DeepSeek V3.1 - Add edge case where thinking is forced open, there is
tool calling in the reasoning content, but then the model just stops the
output without closing the </think> tag, so it's not a partial. In this
case, use the tool call in the reasoning content.
* DeepSeek V3.1 - simplify update_cursor
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix indent
---------
Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* cuda : fix supports_op condition for get_rows when src1->ne2 > 1
ggml-ci
* ggml : add comment about ggml_get_rows
ggml-ci
* cuda : add FIXME [no ci]
* cuda : update support condition
ggml-ci
* ggml: allow casting between f32 and i32
* fix cuda
* add vulkan
* fix CPU non-cont
* add non-cont test case
* add note
* extend test number range
* correct note
* add cont version for vulkan
* convert : force setting sliding_window from original config
This commit modifies the set_gguf_parameters method for EmbeddingGemma
so that it reads the sliding_window parameter from the original model
config.json and uses that value.
The motivation for this change is that the Gemma3TextConfig
constructor adjusts the sliding_window value, which can lead to
inconsistencies when converting models as we expects this value to
match the original model's configuration.
Refs: bb45d3631e/src/transformers/models/gemma3/configuration_gemma3.py (L230)
* fix flake8 error
* add link to huggingface PR
I think glslang will translate an access like x[i][1].z to
OpAccessChain ... x, i, 1, 2
OpLoad float16_t ...
rather than loading all of x[i] in a single OpLoad. Change the
code to explicitly load the vector/matrix.
* ggml WebGPU: remove userdata from request adapter callback
This commit removes the `userdata` parameter from the WebGPU request
adapter callback in `ggml-webgpu.cpp`. Instead, the lambda function
captures the `webgpu_context` directly.
The motivation for this change is to simplify the code and improve
readability.
* inline the callback lambda into the RequestAdapter call
This commit removes the callback lambda variable and inlines it directly
into the RequestAdapter call.
* server : implement `return_progress`
* add timings.cache_n
* add progress.time_ms
* add test
* fix test for chat/completions
* readme: add docs on timings
* use ggml_time_us
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
