* 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
* TP: fix ggml context size calculation, memory leak
* move split state cache back into the context
* revert to constant ggml context size for cgraphs
* increase headroom for statically allocated tensors
* remove obsolete include
* ggml: implement `gguf_init_from_buffer`
* test: `gguf_init_from_buffer`
* fix: memory breakdown for a model loaded with `no_alloc` from a file is consistent with being loaded from a buffer
* fix: use `GGML_UNUSED`
Co-authored-by: Copilot <copilot@github.com>
* fix: remove `total_size` from `gguf_reader`
* fix: file offset calculation, rename `offset` to `data_offset`
Co-authored-by: Copilot <copilot@github.com>
* refactor: extract model loader bug fixes to another PR
* feat: add `gguf_init_from_callback`
* fix: always require a max expected size
* fix: change `gguf_reader_callback_t`'s `output` type to `void *`, change `max_expected_size` and offsets to `uint64_t`
* fix: harden against offset overflow in buffer read
* fix: remove seek behavior from the callback
* feat: `max_chunk_read == 0` means `SIZE_MAX`
* fix: seeking in a gguf file with no tensors
---------
Co-authored-by: Copilot <copilot@github.com>
- Use OpenMP to parallelize iq2xs_init_impl and iq3xs_init_impl.
- Move the OpenMP detection from ggml-cpu to ggml-base.
- Update OpenMP dependencies in ggml-config.cmake.in.
- change `k_copy_src1_to_contiguous` so that uses a precomputed contiguous mapping where all rows "owned" by an expert are in one slice with a know starts and ends
- switch the `O(n_as * n_routed_rows)` contraption to a counting sort-based procedure with `O(n_as + n_routed_rows)` complexity
* SYCL: add BF16 to DMMV kernel path for ~4x token generation speedup
BF16 models had no dedicated token generation kernel — they fell through
to the generic full-GEMM path, resulting in ~14% memory bandwidth
utilization on Intel Arc GPUs. This adds BF16 support to the DMMV
(dequantize mul-mat-vec) path, matching the existing F16 implementation.
Fixes#20478
* SYCL: fix BF16 DMMV out-of-bounds when ncols % 64 != 0
The qk=1 kernel (used for F16 and BF16) iterates with stride
2*GGML_SYCL_DMMV_X (= 64 on Intel targets where WARP_SIZE=16). When
ncols is a multiple of DMMV_X (32) but not of 2*DMMV_X (64), the last
warp iteration accesses elements at col >= ncols, producing NaN for the
final row and wrong values for interior rows.
Fix: tighten can_use_dequantize_mul_mat_vec to require ne[0] %
(2*DMMV_X) == 0 for F16/BF16 types, and update the ASSERT in the BF16
launcher to match. Quantized types use block-structured kernels with
different access patterns and keep the existing DMMV_X check.
Verified: test-backend-ops MUL_MAT passes 913/913 on Intel Arc Pro B70.
Previously failing: m=128/129 n=1 k=1056 cases (NaN and ERR > 0.0005).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* vulkan: fuse snake activation (mul, sin, sqr, mul, add)
Add snake.comp shader with F32 / F16 / BF16 pipelines and
ggml_vk_snake_dispatch_fused. The matcher recognizes the naive 5 op
decomposition emitted by audio decoders (BigVGAN, Vocos) for snake
activation y = x + sin(a*x)^2 * inv_b and rewrites it to a single
elementwise kernel.
test_snake_fuse from the CUDA PR now also compares CPU naive vs
Vulkan fused across F32 / F16 / BF16.
* vulkan: address jeffbolznv review for fused snake activation
Rename T / C to ne0 / ne1 in the shader and push constants to match
the standard naming convention used across the Vulkan backend.
Tighten ggml_vk_can_fuse_snake: require x and dst to be contiguous
(the shader uses idx = i0 + i1 * ne0) and require a / inv_b to be
tightly packed on the broadcast dim (the shader reads data_a[i1]).
* vulkan: tighten snake fusion type checks for all operands (address jeffbolznv review)
* vulkan: reject snake fusion when ne[2] or ne[3] > 1 (address jeffbolznv review)
* vulkan: address 0cc4m review for fused snake activation
snake.comp is renamed to follow the ggml DATA_A_* / A_TYPE convention.
A_TYPE now applies to the activation tensor data_a instead of the
broadcast multiplier, and the bindings become data_a (A_TYPE), data_b
(float), data_c (float) and data_d (D_TYPE). A header at the top of
the shader maps each buffer to its role in y = x + sin(b * x)^2 * c.
On the C++ side, ggml_vk_can_fuse_snake reuses the existing snake_pattern
constant instead of duplicating the op list, sin_node is extracted as a
named local alongside the other chain nodes, and the broadcast operands
a and inv_b are now required to be GGML_TYPE_F32 to match the hardcoded
float bindings on data_b and data_c (the previous a->type == x->type
would silently reject any future BF16 or F16 chain once the supports_op
gate for SIN / SQR is lifted). ggml_vk_snake_dispatch_fused gets an
explicit GGML_TYPE_F32 case and GGML_ABORT on default in place of the
silent f32 fallback, and a stale comment about data_a[i1] / data_inv_b[i1]
is refreshed to match the new binding names.
* metal : fix GGML_OP_SET kernel threads
* tests : extend test_cpy to support different src/dst shapes
Extend test_cpy to support different source and destination tensor shapes
for CPY operations (reshaping), where the total number of elements must match.
- Renamed ne -> ne_src, added ne_dst parameter (default: use src shape)
- Added 50 new reshaping test cases covering 1D<->2D<->3D<->4D conversions
- Tests exercise 1024 boundary, small shapes, and large dimensionality changes
- Fixed dangling reference bug (storing & to temporary std::array)
- Updated all existing test calls with permute/transpose args for compatibility
Assisted-by: llama.cpp:local pi
* metal : optimize concat kernel with row batching for small widths
When ne0 < 256, batch multiple rows into a single threadgroup to improve
occupancy. This avoids underutilizing the GPU when processing narrow tensors.
- Dispatch nth = min(256, ne0) threads per group
- Calculate nrptg (rows per threadgroup) to fill up to 256 threads
- Update kernel index calculation to handle the row batching
- Add boundary check for i1 >= ne1
Assisted-by: llama.cpp:local pi
* tests : clean-up
* tests : refactor CPY shape tests to use dimension permutations
Replace 75 hardcoded test cases with a loop over permutations of
{3, 5, 7, 32} (total elements: 3360). Each src permutation is tested
against canonical sorted and reverse dst, skipping identical shapes.
Covers F32, F16, and Q4_0 (when both src and dst ne0 == 32).
Assisted-by: llama.cpp:local pi
* hexagon: remove gathers and better handling of vtcm in ssm-conv
* hexagon: relax ssm-conv gating requirements
* hexagon: add new prefill ssm-conv backend test
* hexagon: remove trailing white space
* hex-rope: uninline rope_cache_init, otherwise it breaks after rebaseing with SSM_CONV changes
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* opencl: refactor initialization
* opencl: refactor GPU identification
* opencl: rename for consistency
* opencl: cache global mem size in dev_ctx
* opencl: adjust log level
* opencl: load argsort and flash_attn kernels in supports_op
* argsort kernel must be built for supports_op for querying the max
workgroups
* flash_attn kernel has many variants, only load them when needed
* hmx-mm: update debug logging in hmx-mm
* hmx-mm: update dequant logic to use HVX_vector_x2/4
* hmx-mm: remove non-pipelined version of the quantize matmul
It seems that we don't reall need non-pipelined version
* hmx-mm: use activation depth mode and update naming
Co-authored-by: Kim-Chyan Gan <kgan@qti.qualcomm.com>
* hex-mm: minor hmx matmul naming updates
* hmx-mm: remove unused vars
* snapdragon: scripts bump default ubatch-size to 1K
* hexagon: combine HMX and power and clock settings into a single set_power call
* hmx-mm: remove leftover of the scale repl helper
* hexagon: fix editconf error
---------
Co-authored-by: Kim-Chyan Gan <kgan@qti.qualcomm.com>
* Adds initial PDL setup.
* Adds PDL barriers based on simple heuristic: place "sync" before first input pointer access, and "launch" after last write, e.g. to tensors like dst.
* Further optimization pass of the first half of kernels
* Optimized PDL barriers for the second batch of kernels
* Further refinements after rebase.
* Moves pdl logic to separate function, removes some whitespace
* Strips post-hoc PDL logic
* Adds stream capture PDL setup. Enrolls quantize_q8_1 to leverage pdl to
overlap execution with previous kernels
* Enrolls mul_mat_vec_q, rms_norm_f32 and k_bin_bcast (partly) into PDL
* Enrolls mmvf, rope, set-rows and topk kernels for gpt-oss into PDL
* Introduce ggml_cuda_kernel_launch, to abstract away cudaLaunchKernelEx,
to enable hip/musa compatibility
* Enrolls cpy_scalar_contiguous, k_get_rows_float and rms_norm_f32
* Enrolls flash_attn_combine_results
* Fix: Drops needless and broken check of CUDA arch for PDL. PDL either
works or is without effect.
* Enrolls flash-attention kernels to pdl
* Fix: inlines ggml_cuda_kernel_launch, and uses perfect forwarding for
kernels args. This fixes PDL.
* Perf: Enrolls k_bin_bcast variadic template invocation into PDL, via
and template alias and template expansion
* Enrolls all remaining kernels for qwen3-coder-next into PDL
* Remove all PDL LC calls to create a baseline
* Added LC according to internal guidance and tested kernel performance.
* Enrols missing qwen3-5 kernels passively into PDL.
* Kernel optimizations (LC signals) for qwen3.5
* Enrolls ssm-scan kernels into PDL
* Adds GGML_CUDA_PDL command line option to toggle PDL.
* Fix: Ada and lower compilation by guarding PDL calls correctly
* Cleanup: Removes commented out GGML_CUDA_PDL_LC
* Cleanup: Removes experimental comments
* Adds 90-virtual to build script so that Hopper GPUs can leverage PDL.
* Adds stricter checks to enable PDL, adds env-check to disable it, and removes now superfluous compile option to enable PDL.
* Fix: Correct PDL en/disablement based on device-side arch check. Host
side check is UB. Required moving from macros to inlined functions
* Fix: default-disable PDL. Enable by setting GGML_CUDA_ENABLE_PDL=1
* Enable PDL by default for Hopper+ devices
* Enrolls softcap_f32 and two flash_attn kernels into PDL.
* Improves flash attn PDL barrier placement
* Fix: Perf regression on ada; excludes ada and below from PDL launches
* Improves some sync barrier placements
* Drops superfluous constructor
* Adds #endif guard comments
* Reverts experimental change to top-k-moe.cu, which moved expensive allocations
in front of the PDL barrier. It did not have a meaningful impact.
* Exchanges GGML_CUDA_DISABLE_PDL with GGML_CUDA_PDL. IFF GGML_CUDA_PDL=0
PDL is disabled
* Revert "Drops superfluous constructor". Adds const to remaining
arguments
This reverts commit 12b1d250da0089ae02a9bb71bbb3fd6d70f6f2f1.
* Cleanup: Removes and fixes some comments and whitespace
* Clarifies comment of sync-barrier position
* Relocates and refactors PDL launch functions and accessories
* Adds error checking to the regular kernel launch path
* Drops "auto" in favor of "ggml_cuda_kernel_params"
* Adds "const" to ggml_cuda_kernel_launch_params
* [Whitespace] Adds final newline to common.cuh to make editorconfig CI job happy
* opencl: add q4_k moe support
* opencl: add q5_k moe support
* opencl: add q6_k moe support
* opencl: adjust format
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
* sycl: add GGML_SYCL_USE_ASYNC_MEM_OP env toggle
Signed-off-by: Chun Tao <chun.tao@intel.com>
* Use async mem ops for correctness when SYCL graphs are explicitly on.
Signed-off-by: Tao, Chun <chun.tao@intel.com>
---------
Signed-off-by: Chun Tao <chun.tao@intel.com>
Signed-off-by: Tao, Chun <chun.tao@intel.com>
Co-authored-by: Chun Tao <chun.tao@intel.com>
With the introduction of MTP we can have multiple compute contexts for
the same RPC device. In this case last_graph_uid is not updated properly
when contexts are being switched. This patch fixes this by moving
last_graph_uid to the device context, making sure it is always updated.
closes: #23242
* ggml-hexagon: add PAD op HVX kernel
Implements GGML_OP_PAD on the Hexagon HTP backend using HVX vectorized
kernels. Supports zero-padding and circular padding across all 4 tensor
dimensions.
* hex-ggml: remove duplicate op cases (merge conflict)
* hex-pad: fix editorconfig checks and macro alignment
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
2026-05-25 12:26:07 +03:00
Intel AI Get-to Market Customer Success and Solutions
* ci/run: set explicit SPIR-V Headers search path for macOS vulkan CI
For whatever reason, the files are under additional sub-path
`vulkan/` under the cmake directory, which does not match either
current LunarG macOS Vulkan SDK structure (`lib/cmake/SPIRV-Headers`),
nor what gets installed when you run the cmake build+install for
SPIRV-Headers itself on at least Linux (`share/cmake/SPIRV-Headers`).
This allows for SPIRV-Headers to be found, as currently the CI
runner's setup does not seem to include the relevant path in
list of search locations.
* ggml-vulkan/CMakeLists: add a check for SPIRV-Headers
This is installed by the project if it is built and installed.
Receiving an error during the configuration step is generally
preferred to receiving an error in the middle of a build.
Vladislav
Jeff Bolz
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Alexey Kopytko
Masashi Yoshimura
Nikhil Jain
Johannes Gäßler
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shaofeiqi
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Katostrofik
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Intel AI Get-to Market Customer Success and Solutions
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