* CANN: Add ROPE sin/cos cache for reuse
Introduce sin/cos caching mechanism in ROPE to avoid redundant
computation across layers. The cache is built on the first layer
per device and reused by subsequent layers if parameters match.
- Added sin_cache / cos_cache pointers and position_length tracking
- Introduced cache validity flags and properties:
(ext_factor, theta_scale, freq_scale, attn_factor, is_neox)
- Accelerates ROPE by eliminating repeated sin/cos generation
This change reduces overhead in multi-layer scenarios while
preserving correctness by verifying parameter consistency.
Co-authored-by: hipudding <huafengchun@gmail.com>
* fix typo
Signed-off-by: noemotiovon <757486878@qq.com>
---------
Signed-off-by: noemotiovon <757486878@qq.com>
Co-authored-by: hipudding <huafengchun@gmail.com>
* CANN: implement LRU cache for ACL graphs in CANN backend
- Introduce ggml_cann_graph_lru_cache to store multiple ggml_cann_graph objects.
- Graphs are loaded on demand and evicted using LRU policy when capacity is exceeded.
- Updated push, move_to_front, and clear methods to manage cached graphs efficiently.
- Ensures reuse of graphs, reducing graph reconstruction overhead in CANN backend.
* fix typo
* The LRU cache capacity can be configured via an env variable
Signed-off-by: noemotiovon <757486878@qq.com>
* refactory acl graph
* refactory && fix review comments
Signed-off-by: noemotiovon <757486878@qq.com>
---------
Signed-off-by: noemotiovon <757486878@qq.com>
This commit adds check for two function pointers returned from
ggml_backend_reg_get_proc_address.
The motivation for this is that the function pointer could be nullptr if
the get proc address function changes in the future. This is also
consistent with all the other calls to ggml_backend_reg_get_proc_address
in the code base.
This commit adds caching of the ROCm installation for the windows-latest-cmake-hip job.
The motivation for this is that the installation can sometimes hang and/or not complete properly leaving an invalid installation which later fails the build. By caching the installation hopefully we can keep a good installation available in the cache and avoid the installation step.
Refs: https://github.com/ggml-org/llama.cpp/pull/15365
* 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>
* feat: Add python-side constants and conversion for adapter.lora.invocation_string
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add c++ side constants for adapter.lora.invocation_string
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Parse invocation string for adapters from GGUF
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(python): Update conversion to alora_invocation_tokens
This is the preferred method in PEFT which is the source of ground truth
https://github.com/huggingface/peft/pull/2609/files#diff-13380145401d203d5935c5189dd09879f990b81aa63e8e3aaff8ce9110333f0e
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(cpp): Update to alora_invocation_tokens on c++ side
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add C APIs to get alora invocation token array from lora
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Initial implementation of alora cache logic in server
This does not yet do the part to identify the invocation tokens and only
apply the lora adapter afterwards, but it does seem to produce correct
results if the invocation tokens are the beginning of the uncached input.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Identify alora invocation sequences
This currently limits to a single enabled alora per slot. Multiple aloras
with different invocation sequences would be possible, but it would require
a more complex integration of the adapter toggling and is not really a well
studied case for alora since it's unclear if one alora can reuse cache from
previous prefill computed with a different alora.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Only reuse cache for tokens before the alora invocation start
This is a bit of an edge case, but theoretically a user could try the same
query with the alora disabled (just using the base model), then retry with
the alora. The cached tokens from the first pass should be invalid.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Handle un-cached tokens that come before the alora activation
The solution is to only fill up to the token before the invocation start in
the batch if there are any tokens to be prefilled between those pulled from
cache and the invocation start. When this is detected, the alora is
temporarily disabled with a scale of 0.0, then immediately re-enabled after
it has been initialized for the internal graph. Since the batch does not
complete the prompt tokens, the remaining prompt tokens are handled in the
next task, pulling all of the non-alora tokens from cache and proceeding
with prefill for the alora tokens.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use || instead of 'or'
Too much python 🤦
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix off-by-one for limiting cached tokens to before alora start
This was the cause of the inconsistent results from the dummy test script
with and without the turn that runs the prompt without the adapter before
running it with the adapter.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Support backwards-compatibility for "invocation_string" in adapter_config.json
While this has been replaced in the PEFT PR in favor of
alora_invocation_tokens, the existing adapters in the ibm-granite org on HF
use "invocation_string," so this will enable backwards compatibility and
enable testing now (before PEFT PR changes have percolated everywhere).
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove duplicate logging
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* feat: Report alora_invocation_string and alora_invocation_tokens from /lora-adapters
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* feat: Set enable_thinking IFF not disabled and supported
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix inverted logic condition for prefill error
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Always parse the enable_thinking kwarg to overwrite the default value
From what I can tell, this started as a Qwen3-specific keyword, but from
the use in `chat.cpp` translates this inputs.enable_thinking to the right
thinking kwarg for the given model, this is now more of a standardized
kwarg, so it should always override the default value when sent as part of
the chat_template_kwargs field in the API.
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Don't limit tempalte expansion check to jinja
With the use_jinja check, non-jinja models would enable thinking and always
fail assistant prefill
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add the error text to json type errors in json_value
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Explicitly reject string values for "enable_thinking"
There are too many possible "truthy" / "falsy" strings and too many
ambiguous strings that don't have a clear truthy/falsy value, so the
simplest thing to do here is to reject the request. Ideally, this would be
a 422 (Unprocessable Entity), but right now it's coming back as a 500.
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Move logic for detecting template enable_thinking support to common
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use raw pointer for common chat template function
Branch: gabe-l-hart/thinking-model-disabled-agent-prefill
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit adds two new command-line options to the
test-backend-ops.cpp that allow users to list all available GGML
operations and to show test coverage of these operations.
The motivation for this is that it can be useful to quickly see which
operations are currently covered by tests and which are not. Also it
migth be useful when using the `support` mode.
* gguf: split gguf writer into base and buf impl
* gguf: templated gguf write out
* gguf: file based writer (avoid writing everything to memory first!)
* examples(llama2c): fix log not being the same level and compiler nits
