* cmake : do not include ./src as public for libllama
ggml-ci
* cmake : rework tests
ggml-ci
* llguidance : remove unicode include
ggml-ci
* cmake : make c++17 private
ggml-ci
* graph : make mla compatible with FA
* metal : add exp FA kernels for DeepSeek models
ggml-ci
* llama : minor naming updates
ggml-ci
* ggml : disable FA for DS head sizes
* tests : add FA tests for MLA shapes
ggml-ci
The grouped query attention optmization doesn't require a power of two ratio,
the only thing relying on it was the modulo operation written as bitwise &.
split_k need not depend on gqa_ratio - enable it any time there's only one
workgroup in the X dimension. The shader gets the split index from the x coord,
and multiple workgroups in the X dimension (pre-split) indicates a larger
FA operation that wouldn't need splitting.
When using group query attention, we have one workgroup per KV batch and this
can be very few workgroups (e.g. just 8 in some models). Enable split_k to
spread the work across SMs. This helps a lot when the KV cache is large.
* (wip) refactor downloading system [no ci]
* fix all examples
* fix mmproj with -hf
* gemma3: update readme
* only handle mmproj in llava example
* fix multi-shard download
* windows: fix problem with std::min and std::max
* fix 2
* ggml : FA with different K, V head sizes (CPU)
ggml-ci
* metal : add FA with HS=192
* metal : extend FA to support different K and V head sizes
ggml-ci
* metal : add FA vector kernels for heads K 192 and V 128
ggml-ci
* ggml : restrict op on other backends to equal head sizes
ggml-ci
* metal : optimize FA-vec kernel
ggml-ci
* metal : FA remove mq registers
* metal : improve MoE mul_mat_id condition
ggml-ci
* metal : fix comments + remove unnecessary addition
ggml-ci
* metal : avoid too much shared memory usage with mul_mat_id
ggml-ci
The OOB calculation could be wrong if the last iteration was during one of
the unrolled loops. Adjust the unrolling counts to avoid this. Add a couple
new backend tests that hit this failure on NVIDIA GPUs.
* tests: add mul_mat perf/functional tests for p021/nc vulkan shaders
* vulkan: Optimize mul_mat_vec p021 and nc shaders.
These shaders are used in attention calculations, and when the KV cache grows
large they start to dominate the run time. For the nc shader (which is called
with large 'k' dimension), use unrolling and vector loads. For the p021 shader
(which is called with large 'm' and small 'k' dimensions), take advantage of
grouped query attention to reuse loads from the A matrix for the whole group,
and reduce the number of workgroups (too much overhead from tiny dispatches).
Using subgroupAdd in the p021 shader also helps, use that conditionally.
- Find out active blocks per SM using cudaOccupancyMaxActiveBlocksPerMultiprocessor API. Use this value to determine the optimal parallel_blocks value.
- Prefer vector flash attention kernels over MMA kernel for BS=1
Fixes Issue: #12182
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* sampler: turn lazy grammar trigger words to regexes
* add scripts/tool_bench.sh & .py
* constrain llama json output regardless of function name if matches at beginning
* update relaxed newline space rule in grammar tests
* support add_generation_prompt query parameter (useful for /apply_template)
* Update src/llama-grammar.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Support fp16 unary operations in the CUDA backend
* cpu: increase fp16 support for unary operators in the CPU backend
* cuda: increase fp16 support for unary operators in the CUDA backend
* Add test cases for fp16 unary operators
* metal: update supports_op for unary operators that don't support fp16, to prevent test-backend-ops from failing
* metal: fix PR comments for unary op support after fp16 unary tests
* Support float16-to-float16 add/sub/mul/div operations in the CUDA backend
* Add fp16 support for add/sub/mul/div on the CPU backend
* Add test cases for fp16 add/sub/mul/div
* Upgrade init_tensor API to return a ggml_status
To prepare for an 'abort-free' ggml
(ggml not to abort on OOMs but return a OOM status),
as agreeed with Diego in the ggml repo,
upgrade the init_tensor() and view_init() APIs
to return a ggml_status.
* misc fixes
---------
Co-authored-by: slaren <slarengh@gmail.com>
* extract & return thoughts in reasoning_content field (unless --reasoning-format) for DeepSeek R1 & Command R7B
* tool-calls: add deepseek r1 template (models/templates/llama-cpp-deepseek-r1.jinja) + hackommodate broken official template
* tool-calls: accommodate variety of wrong tool call opening tags both R1 Qwen 32B and 7B distills like to spit out
* server/oai: ensure content is null when there are tool calls, and reasoning_content appears before content for readability
* tool-calls: add DeepSeek R1 Qwen distills to server/README.md & server tests
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* initial porting of previous LLG patch
* update for new APIs
* build: integrate llguidance as an external project
* use '%llguidance' as marker to enable llg lark syntax
* add some docs
* clarify docs
* code style fixes
* remove llguidance.h from .gitignore
* fix tests when llg is enabled
* pass vocab not model to llama_sampler_init_llg()
* copy test-grammar-integration.cpp to test-llguidance.cpp
* clang fmt
* fix ref-count bug
* build and run test
* gbnf -> lark syntax
* conditionally include llguidance test based on LLAMA_LLGUIDANCE flag
* rename llguidance test file to test-grammar-llguidance.cpp
* add gh action for llg test
* align tests with LLG grammar syntax and JSON Schema spec
* llama_tokenizer() in fact requires valid utf8
* update llg
* format file
* add $LLGUIDANCE_LOG_LEVEL support
* fix whitespace
* fix warning
* include <cmath> for INFINITY
* add final newline
* fail llama_sampler_init_llg() at runtime
* Link gbnf_to_lark.py script; fix links; refer to llg docs for lexemes
* simplify #includes
* improve doc string for LLAMA_LLGUIDANCE
* typo in merge
* bump llguidance to 0.6.12
* add glm edge chat model
* use config partial_rotary_factor as rope ratio
* support for glm edge model
* vision model support
* remove debug info
* fix format
* llava.cpp trailing whitespace
* remove unused AutoTokenizer
* Update src/llama.cpp for not contain <|end|> or </s>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* add edge template
* fix chat template
* fix confict
* fix confict
* fix ci err
* fix format err
* fix template err
* 9b hf chat support
* format
* format clip.cpp
* fix format
* Apply suggestions from code review
* Apply suggestions from code review
* Update examples/llava/clip.cpp
* fix format
* minor : style
---------
Co-authored-by: liyuhang <yuhang.li@zhipuai.cn>
Co-authored-by: piDack <pcdack@hotmail.co>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: liyuhang <yuhang.li@aminer.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Implemented ggml_sycl_op_soft_max() F16 src1(mask) support for which a pragma deprecation warning was added during #5021.
To do this, had to decouple it from ggml_sycl_op_flatten which always considered src1 to be of fp32 type(many OP functions are dependent on it).
* SYCL: SOFTMAX F16 mask support and other fixes
* test-backend-ops: Add F16 mask test cases
Now that we have batched mat-vec mul Vulkan shaders for up to n==8,
these tests weren't actually exercising the mat-mat mul path. Test
n==9 as well. Also, change to use all_types.
