* sycl: add softplus unary op implementation
* sycl: add softplus unary op implementation
* docs(ops): mark SYCL SOFTPLUS as supported
* docs: update SYCL status for SOFTPLUS
* vulkan: use coopmat for flash attention p*v matrix multiplication
* fix P loading issue
* fix barrier position
* remove reduction that is no longer needed
* move max thread reduction into loop
* remove osh padding
* add bounds checks and padding
* remove unused code
* fix shmem sizes, loop duration and accesses
* don't overwrite Qf, add new shared psh buffer instead
* add missing bounds checks
* use subgroup reductions
* optimize
* move bounds check, reduce barriers
* support other Bc values and other subgroup sizes
* remove D_split
* replace Of register array with shared memory Ofsh array
* parallelize HSV across the rowgroups
* go back to Of in registers, not shmem
* vectorize sfsh
* don't store entire K tile in shmem
* fixes
* load large k tiles to shmem on Nvidia
* adapt shared memory host check function to shader changes
* remove Bc 32 case
* remove unused variable
* fix missing mask reduction tmspsh barrier
* fix mask bounds check
* fix rowmax f16 under/overflow to inf
* fix flash_attn_cm2 BLOCK_SIZE preprocessor directives
The syclcompat/math.hpp is not used anymore. The change that intrduced it was successfuly reverted (https://github.com/ggml-org/llama.cpp/pull/17826).
This include path will become obsolete and dropped in oneAPI 2026.0 effectively breaking ggml-sycl builds.
Deduplication here relied on the fact that vulkan would return unique
UUID for different physical GPUs. It is at the moment not always the case.
On Mac Pro 2019 running Mac OS, with 2 Vega II Duo cards (so, 4 GPU total),
MotlenVK would assign same UUID to pairs of GPUs, unless they
are connected with Infinity Fabric.
See more details here: KhronosGroup/MoltenVK#2683.
The right way is to fix that in MoltenVK, but until it is fixed,
llama.cpp would only recognize 2 of 4 GPUs in such configuration.
The deduplication logic here is changed to only filter GPUs if UUID is
same but driver is different.
* Boilerplate for q6_K repack
* q6_K repack to q6_Kx8 implementation
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* q6_K generic gemv and gemm
* wip, gemm_q6_K 8x8
* Still WIP: loading of q8s, q6h and q6l
* first working version of q6_K gemm
* Moved q6 loads outside of sb block, Unrolled inner loop
* Replaced modulo with mask
* First implementation of GEMV
* ggml_vdotq_s32 -> vdotq_s32
* Reduce width of accumulators in q6_K gemv
* Bsums instead of calc bias. Preload scales to use vget_lane. Unroll.
* Reuse scales in GEMM (same GEMV opt)
* Added todos for bsum and different qh repack
* Arch fallback
* VSLIQ for merging qh adn ql
* Removed TODO, already tested
* Apply suggestions
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Removed unused import
---------
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* [CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full
With pipeline parallelism, during prompt processing, the CPU-side CUDA command buffer gets full, stalling the CPU. Due to this, enough work doesn't get submitted to the GPU, causing bubbles in the GPU timeline.
Fix this by setting the CUDA environment variable CUDA_SCALE_LAUNCH_QUEUES to 4x to increase the command buffer size.
* Set the env variable in the CUDA backend registry allocation
* Add link to PR in code comment
* Remove warning logs and update documentation
* ggml-cpu: Use tiled FA for prompt-processing
the FA performance is gimped on CPU on long contexts because it essentially uses a vector kernel. This PR adds a tiled FA for PP. Perf tuning for tile sizes done on a AMD EPYC single-socket 64-c machine.
* fix out of bounds for mask
* skip rows where there are all masks
* skip tile if mask is inf
* store mask in worksize
* check inf tile earlier
* Boilerplate for q5_Kx8 REPACK on ARM and fallback
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Implements make_block_q5_Kx8 by extending make_block_q4_Kx8
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* q5_K repack gemm and gemv generics
* Gemm and Gemv ARM implementations (i8mm)
* Improved qh manipulation looking at non-repack vec_dot implementation
* Full unroll
* Apply Q5_K Gemv vand and vshl optimizations to gemm. Improve comments.
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Fix wrong fallback definitions of Q5_K
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Fixed comments. Reverted unnecessary formatting
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Fixed typo in generic definitions
* Switching AND + Shift with Shift Insert. Better op interleaving.
* Vectorize + unroll the block scales
* Apply gemm optimizations to gemv
* Improve bias calculation
---------
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* mla : pass V as a view of K to the FA op
* cuda : adjust mla logic to new layout
* kv-cache : fix rope shift
* tests : remove comment
* cuda : fix reusable_cutoff
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* opencl: add `copy_to_contiguous` and utilize mm kernels
* opencl: only copy to cont for f32 and f16 tensors
* opencl: use cont mm for fallback when dst is large
* opencl: use nb local to copy-to-cont
* opencl: use local offset as well
* vulkan: Remove transfer_ctx, do everything in compute_ctx.
We had a bug where a set_tensor_async (using transfer_ctx) didn't get
submitted before the graph_compute (using compute_ctx) that came after
it. To avoid this sort of issue, just do everything in compute_ctx.
Remove transfer_cmd_pool, which was already unused.
* fix crash with perf logger
Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and
update the indexing calculations in get_offsets.
Mat-vec is faster than mat-mat for small values of n. We don't get the same
reuse of the weights as in the non-ID path, but with this the cost is linear
in n rather than n>1 being far slower than n==1.
* CUDA: Replace `init_offsets` with iterators in argsort
This is a QOL improvement, saving us the cost of materializing the
iterator
* Remove unnecessary include from top-k.cu
