* opencl: add general q5_0 support
* opencl: add general q5_1 support
* opencl: support non-uniform workgrp size
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
Drops the hardcoded f32 GLU kernels in favor of a single template. We now load/store in the native tensor type (half or float) to save memory bandwidth, but keep the actual ALU compute in float to avoid exploding math in geglu/swiglu. Also opened up the dispatch gate to allow f16 inputs.
* vulkan: don't hold the device mutex while compiling pipelines
We need to hold a lock while we traverse all pipelines and lazily initialize
them, but we don't need to hold it while the pipeline is being compiled. And
it doesn't need to be the same lock as the device mutex. We call load_shaders
each time a pipeline is needed, so we only need to compile that one pipeline
(and, for example, don't want to end up compiling a pipeline that another
thread should be compiling).
* remove 'needed'
Q2_K/Q3_K/Q6_K do much better when using MMVQ on Intel BMG even
though they're only 2-byte aligned, and Q3_K still wins on
NVIDIA as well.
mesa isn't all that great at coalescing back-to-back loads from
alternating arrays, so we force it instead. Further, we can do
subtraction directly on a full int32_t rather than an i8vec4
with bit twiddling because the high bit is always free to start.
On Intel BMG on mesa, the switch to MMVQ provides an immediate
~57% perf increase in tg128 for unsloth/Qwen3.5-9B-GGUF:Q3_K and
~78% perf increase in tg128 for unsloth/Qwen3.5-9B-GGUF:Q6_K.
The futher switch to block loads leads to a ~24% perf increase in
tg128 for unsloth/Qwen3.5-9B-GGUF:Q3_K and a ~48% perf increase in
tg128 for unsloth/Qwen3.5-9B-GGUF:Q6_K.
Finally, Xe2 wins on MMVQ even for small k, so we take the NVIDIA
override for K quants on Xe2 as well.
* add to support Q1_0, NVFP4, IQ2_XXS, IQ2_XS, IQ2_S, IQ3_XXS, IQ1_S, IQ1_M, IQ3_S, IQ4_NL, IQ4_XS, I32, MXFP4, Q2_K, Q3_K, Q5_K, and Q6_K in GET_ROWS OP
* correct the link
* vulkan: add flash attention bf16 kv support
* vulkan: bf16 FA coopmat1 support
* vulkan: bf16 FA coopmat2 support
* fix FA bf16 f32 fallback
* fix FA bf16 coopmat1 shader
* fix FA bf16 coopmat2 shader
* code cleanup
* cleanup comment change
* address feedback
* add O_TYPE for cm2 FA
* use O_TYPE for gqaStore function
* reduce BFLOAT16 ifdefs
* CUDA: Check PTX version on host side to guard PDL dispatch
Checking on `__CUDA_ARCH_LIST__` alone is insufficient for JIT, as this
variable doesn't differentiate between compiling for say sm_90, sm_90a
or sm_90f (so forward-jittable PTX vs. arch/family-specific PTX).
Thus, one can have a bug when compiling with
`DCMAKE_CUDA_ARCHITECTURES="89;90a"`, where current code would wrongly
dispatch to PDL on sm_90/sm_120 in forward-JIT mode.
This PR fixes this issue by checking `cudaFuncAttributes::ptxVersion` of
the incoming kernel at runtime. A check on ptxVersion alone is
sufficient, as device-codes will always be >= ptxVersion (and any
violation of this would be a severe bug in CUDA/nvcc), see:
https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#gpu-code-code-code
* Implement MurmurHash3 mixer for better hash distribution
Magic constants were taken from boost:
2698b43803/include/boost/container_hash/detail/hash_mix.hpp (L19-L65)
* Update ggml/src/ggml-cuda/common.cuh
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Address review comments, make seed non-zero
* Apply code-formatting
* Replace std::size_t -> size_t for consistency
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* mmvq Optim: add MMVQ_PARAMETERS_TURING(mmvq_parameter_table_id) for SM75 TURING
* avoid a mismatch for JIT compilation of Turing device code for Ampere or newer
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* hex-fa: clean up qf32/fp32 handling and stride handling
* hex-fa: fix corner case fp NAN issues that were cause bad output from gemma4 on v79
* hex-fa: vectorize leftover handling
* hex-fa: avoid HVX fallback during token gen HMX has more FP16 compute capacity
* hmx-mm: remove dead code
* hmx-mm: use fastdiv in x4x2 dequant
* hmx-mm: sandwich dequant and scatter to improve perf
* hmx-mm: fixed rebase conflicts
* hmx-mm: further improve weight dequant by doing early type dispatch and precomputing fastdiv
* hmx-mm: an even earlier dispatch for per-type dequant
* hmx-mm: dequant linear types like q4_0 and q4_1 without the LUTs
This is a bit faster than LUT.
* hex-cmake: one more tweak for lto
---------
Co-authored-by: Trivikram Reddy <tamarnat@qti.qualcomm.com>
* Updated vec.h/vec.cpp code to accumulate to F32 rather than F16
Change-Id: I0cb789347f2bf60ffaf9047319f727e788c825f8
Signed-off-by: Martin Klacer <martin.klacer@arm.com>
Co-authored-by: Milos Puzovic <Milos.Puzovic@arm.com>
* OP_GATED_DELTA_NET impl
* add back lanes_per_column declaration
* removed has_subgroup_arithmetic and has_subgroup_clustered_reduce
* removed trailing spaces and fixes indentation. Hard coded subgroup size for Adreno and Intel. Return not supported when K>1 state snapshot
* support for K>1 state snapshot
* removed picky indent multiple of 4 fixes
* removed return that won\'t be executed
* hex-mm: add support for Q4_1 matmul/matvec, hvx-only for now
* hmx-mm: add support for Q4_1
* hex-mm: use Q8_1 dynamic quantization to avoid having to compute sums in the vec_dot
* hexagon: fix repack scratch buffer overflow
* hex-mm: fix Q4_1 repack buffer sizing
* hexagon: flip the build order for mm and fa (seems to help LTO)
* hex-mm: add vec_dot 4x1s and minor HMX cleanup after adding Q4_1
* hex-mm: fix fp16 vec_dot fallback to 2x1 and another issue that could cause incorrect output
* hexagon: resurrect early-wake and add support for polling for op-batch completions
With Q4_1 ggml-hexagon now claims pretty much the entire graphs which gives the CPU more time to chilax.
This is a good thing! But it does add extra latency for the pure benchmark runs.
Early wakeup helps recover the latency a bit in the normals runs and op-batch polling is just for benchmarking.
---------
Co-authored-by: Todor Boinovski <todorb@qti.qualcomm.com>
* vulkan: Switch MUL_MAT_VEC to 4 K per iteration for F16/32
Against mesa git, this shows a 4.8% performance improvement for
tg128 on Qwen3.5-9B:BF16 on Intel BMG.
Note that this breaks some tests until the last commit which fixes
OOB A reads.
* vulkan: Use aligned loads in mul_mat_vec when available
Against mesa git, this shows a 3.3% performance improvement for
tg128 on Qwen3.5-9B:BF16 on Intel BMG.
* Make explicit that `num_rows` is <= `NUM_ROWS` in mul_mat_vec
Mesa's UUB logic can't see through conditionals, limiting its
ability to understand the bounds on the `num_rows` field in the
cleanup run. Making it explicit that `num_rows` is, indeed, always
<= `NUM_ROWS` helps mesa make slightly better codegen.
Against mesa git, this currently shows a 1% performance improvement
in tg128 on Qwen3.5-9B:BF16 on Intel BMG.
* vulkan: Fix OOB A reads in MUL_MAT_VEC for odd sizes
There was a TODO to fix the OOB reads from the A matrix which we do
here.
It is within performance noise (+<0.1%) in tg128 for
Qwen3.5-9B:BF16 on Intel BMG.
* feat: extend repeat op for vulkan
* feat: add repeat_f16 vulkan pipeline
* fix: ensure same dst and src types
* fix: use type_size instead of data types
* fix: use int16 and int32 for repeat shader op
* chore: rename repeat_f* to repeat_i*
* chore: rename repeat vulkan pipelines
* ggml-zendnn: fixed naming of matmul function
* ggml-zendnn: fixed naming of mul_mat_id function
* ggml-zendnn: fixed print in mul_mat_id
---------
Co-authored-by: plotnikov.v10 <plotnikov.v10@wb.ru>
* vulkan: add CONV_SHAPE_64x128 for medium-K conv2d
* vulkan: skip conv2d bounds checks when shapes align with tile sizes
* vulkan: use WG_SIZE=128 for CONV_SHAPE_64x32 conv2d
* vulkan: stage cm2 conv2d accumulator through shmem before global store
* vulkan: add coopmat1 conv2d path
* fallback when using too much shared memory. clean up comments
* Require 16x16x16 and subgroup size 32 or 64
* check whether shared memory is sufficient before overwriting conv2d params with coopmat1 values
* hexagon: add support for CONCAT with optimized concat_2d_transposed
qwen3.5 models are quite heavy on the CONCAT with large and transposed src1.
* hex-concat: use fastdiv in generic version
* hex-concat: make checks for transposed a bit more readable
* hex-concat: reoder dma ops for better pipelining
* hex-cont/cpy: optimize CPY and CONT ops
The primary change is to avoid scalar divs in the inner loops.
We were calling hvx_copy_uu(... type_size) where type_size is non a constexpr.
This causes runtime divs by that value which is normally just 4 or 2 (f32/f16).
* hex-get-rows: optimize GET_ROWS for large rows
We now use DMA for larger rows and also split them into chunks to improve perf for Qwen3.5 and other models
that do lots of GET_ROWS with huge (2MB+ rows).
Also bump the DMA queue depth now that we can take advantage of it.
* hex-concat: unroll the inner loops of concat_2d
* hex-concat: more updates to concat_2d to improve perf a bit further
* hex-cpy: fixed n_rows per thread checks in the copy ops
* hmx-fa: fix alignment issues while computing dma sizes
* hex-set-rows: add early returns for idle threads
* hvx-rope: minor optimization to replace loops with fastdiv logic
* hex-rope: replace scalar tail processing with HVX
* hex-rope: optimize rope cache init with HVX
Add hvx-utils sin/cos helpers that use an aprox method (similar to rsqrt, inverse, etc)
Use the helpers to optimize ROPE.
* ggml-webgpu: Add MMVQ path for Q4/Q8/Q2_K/Q4_K
* Fix to editorconfig checking pass
* Remove mul-mat-legacy pipeline
* Fix to use vendor name as is and add dot_product/vendor to shader_lib_ctx
* Only run webgpu CI on my fork
* Add webgpu only workflow
* refactor batch_compute_passes to a per-thread variable, and submit individual passes when it is set to false and no GPU profiling is enabled
* restore build.yml
lhez
Masashi Yoshimura
shaofeiqi
Shrivas Shankar
Jeff Bolz
Winston Ma
Johannes Gäßler
Matt Corallo
Neo Zhang
Georgi Gerganov
Jinyang He
Ruben Ortlam
Reese Levine
Oliver Simons
fairydreaming
Andreas Kieslinger
Max Krasnyansky
fl0rianr
redfox
Jaden_Mach
Martin Klacer
ymcki
l8bloom
Vladislav
Alexey Kopytko
Nikhil Jain