* metal : improve naming
* metal : refactor device
ggml-ci
* cont : props
ggml-ci
* metal : apply ggml_mem_ranges_t
ggml-ci
* metal : remove GGML_METAL_USE_BF16
ggml-ci
* metal : refactor device buffer
ggml-ci
* cont : fix naming
* metal : sync before destroying the backend
ggml-ci
* metal : refactor context
ggml-ci
* metal : migrate ggml-metal.m to ggml-metal.cpp
ggml-ci
* metal : adjust ops API
ggml-ci
* metal : use C++ to store piplienes
ggml-ci
* metal : migrate ops to separate functions
ggml-ci
* metal : add ggml_metal_library_t
ggml-ci
* metal : improve naming
ggml-ci
* metal : cleanp
ggml-ci
* metal : add support for GGML_OP_LOG
ggml-ci
* metal : fix error handling
ggml-ci
This commit reverts the change of the runs-on parameter for the
macOS-latest-cmake-x64 job back to macos-13 that was make in
Commit 51abc96bdc ("ci : update
macos-latest* jobs to use macos-latest (#15938)").
The motivation for this is that using macos-latest will cause an ARM
based runner to be used, and not an x64 based runner.
Refs: https://github.com/ggml-org/llama.cpp/pull/15938#issuecomment-3300805127
* CANN: Fix ggml_cann_set_device to avoid redundant device switches
- Added a check to skip aclrtSetDevice if the current device is already set.
- Prevents unnecessary context switches while keeping thread/device consistency.
* CANN: add device default id
This commit updates the runs-on field for the macOS arm64 webgpu build
job to use macos-latest instead of just latest.
The motivation for this is that this job can wait for a runner to pick
up the job for a very long time, sometimes over 7 hours. This is an
attempt to see if this change can help reduce the wait time.
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/17754163447/job/50454257570?pr=16004
* ggml : remove adding extra dim timestep embedding
This commit updates the ggml_timestep_embedding function to no longer
add an extra dimension when the specified dimension is odd.
The motivation for this change is that this introduces an unnecessary
dimension when the dimension is odd, which caused an issue in the
kernels which were not expecting this extra dimension and it resulted in
uninitialized memory for the second to last dimension.
* ggml-cuda : fix padding in timestep embedding kernel
This commit removes the zeroing out of the last dimension now that we
are not adding the extra padding dimension.
* ggml-metal : fix padding in timestep embedding kernel
This commit fixes the zero padding for odd dimensions in
the timestep embedding kernel
* ggml-opencl : fix padding in timestep embedding kernel
This commit fixes the zero padding for odd dimensions in
the timestep embedding kernel.
* ggml-sycl : fix padding in timestep embedding kernel
This commit fixes the zero padding for odd dimensions in
the timestep embedding kernel.
* ggml-vulkan : fix padding in timestep embedding kernel
This commit fixes the zero padding for odd dimensions in
the timestep embedding kernel.
* ggml-cpu : fix padding in timestep embedding function
This commit removes the zeroing out of the last dimension now that we
are not adding the extra padding dimension.
This commit updates the github workflows build.yml file to include steps
for uploading and downloading the xcframework artifact. The
macos-latest-swift job now depends on the ios-xcode-build job and
downloads the xcframework artifact produced by it.
The motivation for this changes is that it takes a long time to build
the xcframework and we are currently doing this twice in the workflow.
With this change, we only build it once and reuse the artifact.
clang-format previously broke long CUDA macros (e.g. __launch_bounds__) into
unreadable line breaks inside template declarations, such as:
template<int D, int ncols, int nwarps, int VKQ_stride,
typename KQ_acc_t, bool use_logit_softcap>
__launch_bounds__(nwarps*ggml_cuda_get_physical_warp_size(), 1)
This change adjusts formatting rules so that CUDA macros remain consistent
and aligned with the surrounding template syntax.
* ci : update macos-latest* jobs to use macos-latest
This commit updates the jobs that are named macos-latest* to use the
macos-latest label instead explicit versions.
The motivation for this is that there is currently a mixuture of
versions in this workflow and there are jobs that are failing because
they require a newer version.
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/17644792595/job/50140010907#step:5:1759
* ci : add xcodebuild -downloadPlatform iOS command
* fix im2col_3d to respect non-contiguous inputs (views)
The CUDA 3D im2col kernel computed source addresses assuming compact layout (products of dims), ignoring nb[] strides.
This patch switches im2col_3d source indexing to use true strides derived from src1->nb[] (in elements), mirroring the approach used in the 2D CUDA im2col path. Destination indexing is unchanged.
* use ggml_element_size() for src strides
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* SYCL: Add COUNT_EQUAL operator support (rebased on master)
* SYCL: remove duplicate op_count_equal definition
* tests: remove test_count_equal_typed and use test_count_equal for all cases
* tests: keep only I32 case for COUNT_EQUAL as suggested
* tests: keep only I32 case for COUNT_EQUAL as requested
* llama-run: Fix model download on Windows
* fix SSL error (SSL peer certificate or SSH remote key was not OK)
* fix program crash on std::filesystem::rename
* llama-run: create a separate method to utilize RAII
* llama-run: handle rename exception
In `llama-perplexity`, when using `--kl-divergence`, the KL divergence statistics output mistakenly displays the 99th percentile twice. This change fixes that and correctly displays the 90th percentile as originally intended (presumably).
* add grok-2 support
* type fix
* type fix
* type fix
* "fix" vocab for invalid sequences
* fix expert tensor mapping and spaces in vocab
* add chat template
* fix norm tensor mapping
* rename layer_out_norm to ffn_post_norm
* ensure ffn_post_norm is mapped
* fix experts merging
* remove erroneous FFN_GATE entry
* concatenate split tensors and add more metadata
* process all expert layers and try cat instead of hstack
* add support for community BPE vocab
* fix expert feed forward length and ffn_down concat
* commit this too
* add ffn_up/gate/down, unsure if sequence is right
* add ffn_gate/down/up to tensor names
* correct residual moe (still not working)
* mess--
* fix embedding scale being applied twice
* add built in chat template
* change beta fast for grok if default value
* remove spm vocab in favor of community bpe vocab
* change attention temp length metadata type to integer
* update attention temp length metadata
* remove comment
* replace M_SQRT2 with std::sqrt(2)
* add yarn metadata, move defaults to hparams
* metal : remove mem pool usage
ggml-ci
* metal : remove mem pool implementation
ggml-ci
* metal : take into account the actual allocated memory of the tensor
ggml-ci
* cont : use ggml_backend_buft_get_alloc_size
ggml-ci
* cont : improve, comments
ggml-ci
* cont : add functions for the extra tensor sizes
* metal : add comments
ggml-ci
* metal : implement .get_alloc_size for the rest of the buffer types
ggml-ci
* metal : remove ggml_metal_heap
ggml-ci
* rocm.Dockerfile: added gfx1200,gfx1201 architectures to support AMD Radeon RX 9000 series
https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html#rdna-os
states the Radeon RX 9000 series is supported support from Ubuntu 24.04.2, and the dockerfile is using 24.04 which is ROCm 6.4.
This fixed the `ROCm error: invalid device function` I was getting when trying to use the rocm container.
* build: fix the cache keys for Windows HIP release job
Update the cache keys to include the HIP SDK version, preventing the
use of outdated ROCm installation caches.
* build: sync changes from release.yml to build.yml
- Update HIP SDK version to 25.Q3 and ROCm version to 6.4.2
- Update the cache keys to reflect the new versions
* build: remove Windows HIP release for gfx1151
since the current stable rocWMMA does not support gfx1151.
Use this to query register count for shader compiles on NVIDIA. Currently
this is only for performance debug, but it could eventually be used in some
heuristics like split_k.
* metal : refactor bin kernels loading
ggml-ci
* metal : refactor rms kernel loading
ggml-ci
* ci : try to add memory leaks check
ggml-ci
* ci : try to enable memory leak detection for Mac
* cont : seems to be working
To pull and run models via: llama-server -dr gemma3
Add some validators and sanitizers for Docker Model urls and metadata
Signed-off-by: Eric Curtin <eric.curtin@docker.com>
Georgi Gerganov
Aleksander Grygier
Xuan-Son Nguyen
Johannes Gäßler
David Ribeiro Alves
Eve
Daniel Bevenius
Jie Fu (傅杰)
Shane A
Chenguang Li
jacekpoplawski
Bowen Han
Yuri Khrustalev
Aman Gupta
Jake Karnes
Diego Devesa
yael-works
Nikolay Popov
ddh0
Sigbjørn Skjæret
Adam
Ruben Ortlam
lcy
Radoslav Gerganov
Aaron Teo
Jeff Bolz
Eric Curtin
Haiyue Wang
Mathieu Baudier
Neo Zhang Jianyu