- Add threading support implementation details
- Document ThreadPoolExecutor usage and thread safety
- Add model parameter implementation details
- Include testing results for both features
- Add ThreadPoolExecutor for parallel request processing controlled by --threads
- Add --model argument to specify model name in request data
- Refactor process() to use thread-safe _process_single_case() method
- Update progress tracking to work with concurrent execution
- Create new simplified evaluation script focused only on AIME
- Implement EvalState and Processor dataclasses for structured state management
- Add real-time feedback showing correct/incorrect status per case
- Abstract grading interface for external grader support
- Use structured JSON output for eval state
- Apply HuggingFace dataset caching to avoid repeated downloads
- Remove Levenshtein matching - eval script only sends requests and validates answers
Extract repeating question string into TEST_QUESTION variable and
create make_request() helper function to reduce code duplication.
Add proper error handling for error responses.
Add a standalone Python script that simulates a llama-server HTTP endpoint
for testing the eval script. The simulator:
- Implements /v1/chat/completions endpoint with OpenAI-compatible format
- Loads AIME dataset from HuggingFace with local caching
- Uses Levenshtein distance for intelligent question matching
- Supports configurable success rate for correct/wrong answer generation
- Provides debug logging for troubleshooting
Also includes test scripts and documentation for testing and understanding
the simulator functionality.
* cuda: optimize iq2xxs/iq2xs/iq3xxs dequantization
- load all 8 int8 for a grid position in one load
- calculate signs via popcnt instead of fetching from ksigns table
- broadcast signs to drop individual shift/mask
* cuda: iq2xxs: simplify sum scaling
express `(sum * scale + sum / 2) / 4` as `(sum * (scale * 2 + 1)) / 8`
express `((aux32 >> 28) * 2 + 1)` as `(aux32 >> 27 | 1)`
saves 3 registers for mul_mat_vec_q (152 -> 149) according to nsight
AFAICT no overflow can occur here as iq2xxs values are far too small
* uint -> uint32_t
error: identifier "uint" is undefined
This option was introduced as a workaround because cpp-httplib could not
build on visionOS. Since it has been fixed and now compiles on all platforms,
we can remove it and simplify many things.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
This commit addresses a build issue with the KleidiAI backend when
building multiple cpu backends. Commmit
3a00c98584 ("cmake : fix KleidiAI install
target failure with EXCLUDE_FROM_ALL") introduced a change where
FetchContent_Populate is called instead of FetchContent_MakeAvailable,
where the latter does handle this case (it is idempotent but
FetchContent_Populate is not).
I missed this during my review and I should not have commited without
verifying the CI failure, sorry about that.
* ggml-cpu: FA add GEMM microkernel
* add guard for sizeless vector types
* fix case where DV % GGML_F32_EPR !=0
* move memset out of the loop
* move another memset out of the loop
* use RM=4 for arm
* simd_gemm: convert everything to int
* convert everything to size_t to avoid warnings
* fixup
* add pragma for ignoring aggressive loop optimizations
* cmake: fix KleidiAI install target failure with EXCLUDE_FROM_ALL
Fix for the bug #19501 by adding EXCLUDE_FROM_ALL to FetchContent_Declare. This properly excludes KleidiAI from both build and install targets, preventing install failures when GGML_CPU_KLEIDIAI=ON is used.
The KleidiAI source files are still compiled into libggml-cpu.so, preserving all functionality.
* addressed code review comments
last_graph is only available without OpenMP, but
ggml_graph_compute_thread() is called in both cases.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Refactoring to use new llama_put_adapter_loras
* cont : alternative lora API
---------
Co-authored-by: Jake Chavis <jakechavis6@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml: added cleanups in ggml_quantize_free
Add missing cleanup calls for IQ2_S, IQ1_M quantization types and IQ3XS with 512 blocks during quantization cleanup.
* mmap: Fix Windows handle lifetime
Move hMapping from local variable to member variable so it stays alive for the entire lifetime of the mapping.
The file mapping handle must remain valid until UnmapViewOfFile is called.
Fixes cleanup order in destructor.
* Update llama-mmap.cpp
* Update llama-mmap.cpp
Remove trailing whitespace from line 567
* ggml-hexagon: fa improvements
ggml-hexagon: optimize flash attention calculations with improved variable handling
ggml-hexagon: streamline flash attention operations by removing redundant checks for FP32
ggml-hexagon: optimize hvx_dot_f16_f16_aa_rx2 by simplifying variable handling for unused elements
ggml-hexagon: optimize flash attention by changing slope vector type to F16
* hexfa: fixed test-backend-ops failurs due to leftover element handling
* hexagon: refactor and optimize fa to use local context struct
* ggml-hexagon: optimize flash-attention using hvx_vec_expf
Use HVX for online softmax.
---------
Co-authored-by: chraac <chraac@gmail.com>
Georgi Gerganov
gatbontonpc
David Friehs
Aaron Teo
Adrien Gallouët
Daniel Bevenius
Aman Gupta
SamareshSingh
Sigbjørn Skjæret
Anav Prasad
iMil
Aleksander Grygier
agent-enemy-2
George
Jeff Bolz
Sophon
Max Krasnyansky