Reuse existing rope kernels with a function constant to toggle forward/backward
rotation, avoiding duplicate kernel code.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* metal : add f16 and bf16 support for concat operator
Extend the Metal backend concat operator to support f16 and bf16 tensor
types in addition to the existing f32 and i32 support.
- Template kernel_concat on type T with specializations for float, half,
bfloat, and int
- Add type-specific pipeline getter ggml_metal_library_get_pipeline_concat()
- Update device support check to allow f16 unconditionally and bf16 when
device supports bfloat16
- Update dispatch to select the correct kernel specialization by type
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* metal : extend concat operator to support f16, bf16, i8, i16 and i64
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* ci : add ccache clear action
* ci : split self-hosted GPU jobs into build-self-hosted.yml
Extract self-hosted runner jobs from build.yml into a dedicated
build-self-hosted.yml following the llama.cpp pattern:
- gpu-cuda (NVIDIA Linux)
- gpu-vulkan-nvidia-cm (NVIDIA Linux)
- gpu-vulkan-nvidia-cm2 (NVIDIA Linux + COOPMAT2)
- gpu-metal (macOS ARM64)
- gpu-vulkan (macOS ARM64)
GitHub-hosted CPU jobs remain in build.yml.
Assisted-by: llama.cpp:local pi
* ci : split release jobs into release.yml
Extract release-related jobs from build.yml into a dedicated
release.yml following the llama.cpp pattern:
- determine-tag
- windows (Win32/x64, SDL2)
- windows-blas (Win32/x64, OpenBLAS)
- windows-cublas (x64, CUDA 11.8/12.4)
- ios-xcode-build
- bindings-java (depends on windows)
- release (artifact aggregation + GitHub release)
CoreML job stays in build.yml with its own local tag calculation.
Assisted-by: llama.cpp:local pi
* ci : remove bindings-java job from release.yml
Assisted-by: llama.cpp:local pi
* cont : add manual trigger for build.yml
* cont : remove obsolete ifs
* ci : extract sanitizer job to bild-sanitize.yml
* ci : extract linux jobs into build-linux.yml
* ci : extract macos jobs to build-macos.yml
* ci : extract gcc jobs to build-gcc.yml
* ci : extract clang jobs to build-clang.yml
* ci : extract sycl jobs to build-sycl.yml
* ci : extract windows jobs to build-windows.yml
* ci : extract emscripten job to build-wasm.yml
* ci : extract android jobs into build-android.yml
* ci : extract quantize job to quantize.yml
* ci : extract coreml job into coreml.yml
* ci : extract vad job to vad.yml
* ci : extract cpu jobs to build-cpu.yml
* ci : make naming of yml files consistent
* ci : add --fail to curl download and propagate
This commit adds the --fail option to the model download scripts so that
if the model download returns a server error this is picked up. This is
then detected in run.sh and a error message is displayed and the script
stops and returns an error.
The motivation for this is that currently it is possible for the model
download to fail but this script proceeds and instead of a model file
the contents will be an html page probably with the error. This will
then cause the model to not be able to load due to a missing magic
number. I'm not sure we can do much about the downloading failing,
perhaps a retry but at least this will give a clearer error message.
Refs: https://github.com/danbev/whisper.cpp/actions/runs/26866349389/job/79230794512
* ci : enable command traces to see download command in use
* ci : add retry functionality to download model script
This commit adds curl retry options to the model download script.
The motivation is that currently when CI jobs run huggingface rate limit
the requests and return:
```console
curl: (22) The requested URL returned error: 429
```
This is an attempt to work around this and if it does not work then we
can an authorization token.
* ci : extract freebsd job to build-freebsd.yml
This job has been commented out as it has been flaky in the past. I'll
monitor this and if it continues to be unreliable we can disable it in
the github actions GUI instead of commenting it out like we did before.
* ci : add ccache to jobs (non-docker builds)
The ccache will only be saved on pushed to master.
* ci : bump ccache-action version to v1.2.21
The motivation for this is that the save parameter does not seem to work
with the current version.
* ci : add ccache to docker jobs in build-linux.yml
* ci : add debug statements to linux docker build
* ci : set CCACHE_DIR for build-linux.yml
* ci : add ccache to the remaining docker jobs
* ci : remove build-linux.yml
This commit remove build-linux.yml as the same jobs are also run by
build-gcc.yml, with the exception that build-gcc.yml also run ctest).
So keeping build-gcc.yml and removing the redundant build-linux.yml.
* ci : add linux build artifacts to release
* ci : revert to hendrikmuhs/ccache-action for win job
This is currently causing the following failure:
```console
sccache C:\PROGRA~1\NVIDIA~1\CUDA\v\bin\nvcc.exe -forward-unknown-to-host-compiler -DGGML_BACKEND_BUILD -DGGML_BACKEND_SHARED -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_SCHED_MAX_COPIES=4 -DGGML_SHARED -D_CRT_SECURE_NO_WARNINGS -D_XOPEN_SOURCE=600 -Dggml_cuda_EXPORTS -DCMAKE_INTDIR=\"Release\" -ID:\a\whisper.cpp\whisper.cpp\ggml\src\ggml-cuda\.. -ID:\a\whisper.cpp\whisper.cpp\ggml\src\..\include -isystem "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v\include" -Xcompiler="-MD -O2 -Ob2" -DNDEBUG -std=c++17 -arch=native -use_fast_math -extended-lambda -Xcompiler /Zc:preprocessor -MD -MT ggml\src\ggml-cuda\CMakeFiles\ggml-cuda.dir\Release\allreduce.cu.obj -MF ggml\src\ggml-cuda\CMakeFiles\ggml-cuda.dir\Release\allreduce.cu.obj.d -x cu -c D:\a\whisper.cpp\whisper.cpp\ggml\src\ggml-cuda\allreduce.cu -o ggml\src\ggml-cuda\CMakeFiles\ggml-cuda.dir\Release\allreduce.cu.obj -Xcompiler=-Fdggml\src\ggml-cuda\CMakeFiles\ggml-cuda.dir\Release\,-FS
sccache: encountered fatal error
sccache: error: Could not parse shell line
sccache: caused by: Could not parse shell line
```
Refs: https://github.com/danbev/whisper.cpp/actions/runs/26883673904/job/79290017353
* ci : make static linux artifacts
* ci : make linux release artifact names consistent
This commit removes the tag form the linux release artifacts to be
consistent with the existing artifacts.
If we want to include the tag then we can do that in a follow-up PR.
* ci : fix linux zip files to have a directory
* ci : add HF_TOKEN secret for HF download authorization
This is to avoid the HR rate limiting when downloading model.
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* 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
Fixes#22237 — the find_library(MATH_LIBRARY m) result was being
discarded and the target linked against the literal 'm' string.
This prevents users from overriding the math library (e.g. for AMD AOCL)
via CMake variables. Now the discovered MATH_LIBRARY is used directly.
* ggml : transpose fused GDN state access for coalesced memory reads (llama/20436)
The fused Gated Delta Net kernel accessed the [S_v, S_v] state matrix
column-wise on row-major storage, causing strided reads (stride S_v =
128 floats = 512 bytes) that waste GPU cache bandwidth. This produced a
39% regression on Qwen3.5-9B (Metal, M4 Max) compared to the unfused
path.
Transpose the state indexing so threads read contiguously:
- Metal: s_ptr[is*S_v] -> s_ptr[is] (stride 1 vs S_v)
- CUDA: curr_state[i*S_v+col] -> curr_state[col*S_v+i] (coalesced)
- CPU: restructured loops for row-wise transposed access
Also add --fused-gdn [on|off|auto] CLI flag (mirrors --flash-attn) so
users can control fused GDN independently of auto-detection.
All GATED_DELTA_NET backend-ops tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* ggml : use SIMD dot products in CPU GDN kernel, couple AR/chunked fused flags
- Replace scalar inner loops with ggml_vec_dot_f32 for SIMD-optimized
dot products in the CPU fused GDN kernel (delta and attention output)
- Couple fused_gdn_ar and fused_gdn_ch flags in auto-detection: if one
path lacks device support, disable both to prevent state layout mismatch
between transposed (fused) and non-transposed (unfused) formats
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* llama : rever fgdn argument changes
* graph : remove GDN state transposes
* vulkan : adapt
* cuda : remove obsolete smem code
---------
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
* llama : enable chunked fused GDN path
* models : avoid Q and K repeats when using fused GDA
* cont : fix comment
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix the fix
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix
* metal : add GDN kernel (llama/20361)
* metal : add Metal backend for GGML_OP_GATED_DELTA_NET
Add a fused Metal kernel for the gated delta net recurrence op
(#19504), enabling GPU-accelerated inference for DeltaNet-based
models (Qwen3.5, etc.) on Apple Silicon.
Supports both GDA (scalar gate) and KDA (per-row gate) modes
with head_size 64 and 128. Unsupported configurations (head_size
32, non-contiguous tensors) gracefully fall back to CPU.
Performance: Qwen3.5-0.8B Q4_K_M on M4 Max
tg128: 170 -> 213 t/s (+25%)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* metal : validate contiguity of all input tensors in supports_op
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* metal : add algorithm equivalence comment for GDA decay path
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* cont : unslop + optimize
* cont : clean-up
---------
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* CUDA: AR gated delta net improvements (llama/20391)
* Add FastDiv to gated_delta_net_cuda
* Shard columns across warps
This reduces register pressure (avoids spill for S_v = 128) and gives
the warp-scheduler more CTAs to schedule (thus hiding data-access
latencies).
* Remove unneded include in gated_delta_net.cu
* Improve comments
* Apply code-formating
* Make sharding HIP-compatible
1. Use ggml_cuda_get_physical_warp_size() to determine warp size flexibly
2. Add test with partial warp to test sum reduction on CUDA
* Remove fastdiv_s64, as we can treat neqk1 and rq3 as uint32_t
* Rename variables
* Enable GDN also for prefill, move TODO for chunked_GDN
* Actually remove the TODO from 206890897546bd16602c3b79394fd5ea09ef199f
* Get warp size at runtime
warp_size is not known at compile time in hip host code.
* Don't expose ggml_cuda_get_physical_warp_size on host
---------
Co-authored-by: uvos <devnull@uvos.xyz>
* llama : refactor llm_build_delta_net_base API
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Co-authored-by: uvos <devnull@uvos.xyz>
Georgi Gerganov