* hexagon: add fp16 support for binary ops: add,sub,mul,div
* hexagon: fix test-backend-ops failures for fp16 binary ops on older arches (<v79)
* hexagon: decide on n_threads (aka n_jobs) early to avoid overallocating scratchpad
* snapdragon: fix readme link
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* models : add llm_build_delta_net_base
* cont : keep qwen35 and qwen35moe graphs intact
* cont : add comments [no ci]
* add kimi linear to delta-net-base
* removed unnecessary ggml_cont from g_exp_t
* removed ggml_cont from g_diff_exp_t. moved ggml_cont for o to kimi-linear.cpp
* removed unnecessary diag mask
* cont : simplify
* cont : avoid graph splits
* scale q after mul instead of beginning
* scale q after mul instead of beginning
* identical ppl
* cont : fix scale and decay mask
* minor : remove TODO
* block implementation for kda
* remove space at the end of line 101
* concat+pad
* pad+binary row concat
* chunk size 16 for kda
* removed minor differences to master
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Adds CPU-to-CUDA copy capability to
ggml_backend_cuda_cpy_tensor_async()
* Adds function to relax sync requirements between input copies on
supported backends (CUDA for now)
* Exchanges synchronous copy with async copy function.
* Adds macro guards to allow compilation in non-CUDA builds
* Reworked backend detection in ggml-backend.cpp to avoid linking
conflicts
* Relax requirement of checks in async CUDA copies from backend and buffer type to just buffer type, to avoid linking issues
* Minor cleanup
* Makes opt-in to relax use of explicit syncs more general. Backends like
vulkan which require a synchronization between HtoD copies and graph
execution could also adopt this change now.
* Reintroduces stricter check for CPU->CUDA backend async copy via
GGML_DEVICE_TYPE_CPU.
* Corrects initialization of ggml_backend_sync_mode in
ggml_backend_sched_split initialization
* Simplifies synchronizations to adhere to `saaasg` pattern.
* Apply suggestion from @ggerganov (src->buffer to buf_src)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Apply suggestion from @ggerganov (src->buffer to buf_src) v2
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model : fix Qwen3.5 model type detection
* Update src/llama-model.cpp
whoops, my bad
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Enable tmate debugging for investigating thread safety issue
* Refactor wait and submit to operate on vector<wgpu::FutureWaitInfo>, and fix wait to delete only the future that is completed.
* Cleanup
* Remove clear change and run clang-format
* Cleanup
Many models have vocabulary sizes, and thus tensor shapes, with more
than 5 digits (ex: Gemma 3's vocab size is 262,208).
I already fixed this for `llama_format_tensor_shape` but missed it for
`llama_format_tensor_shape` until now. Oops.
* Set C locale for consistent float formatting across all binaries.
* Add C locale setting to all tools binaries
Add std::setlocale(LC_NUMERIC, "C") to all 16 binaries in the tools/
directory to ensure consistent floating-point formatting.
* Apply suggestion from @JohannesGaessler
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ggml-webgpu: fix workgroup dispatch limit for large batch sizes
WebGPU limits workgroup sizes to 65535 per dimension. Large MUL_MAT
operations with batch sizes exceedeing this limi would fail.
* add compute_2d_workgroups() helper to split total workgroup ID across
X/Y dimensions
* update mul_mat_reg_tile.wgsl to reconstruct linear workgroup ID from 2D
dispatch
* update mul_mat_subgroup_matrix.wgsl to reconstruct linear workgroup ID
from 2D dispatch
* update mul_mat.wgsl to compute global index from 2D workgroup
coordinates
* refactor all three mul_mat dispatch paths to use the shared helper
* ggml-webgpu: add bounds checking for over-dispatched workgroups
2D workgroup dispatch can over-dispatch when total workgroups don't
divide evenly into the 65535 per-dimension limit. Extra workgroups
would compute invalid batch indices, causing memory corruption.
* add batch_idx bound check to mul_mat_reg_tile.wgsl and
mul_mat_subgroup_matrix.wgsl to prevent over-dispatched workgroups
from accessing invalid memory
* fixes test failures with large batch sizes (eg., bs=[128, 1024])
* ggml-webgpu: add back TODO for spliting large sizes into batches
* Optimize 2d workgroup provisioning
* Set some parameters that increase speed
---------
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Allow webgpu_buf_pool to resize if needed, remove inflight_threads, and replace inflight_threads with num_kernels for submission
* Run clang-format
* Keep track of num batched kernels that have not been submitted yet
* Run clang-format
* Increase buf pool max size
* Increase param buf pool init size
* Remove webgpu buf pool resizing
* Merge with master
* Add buffer pool growth
* Move buffer pool growth outside of lock
* Reduce max pool size to 32
* Run clang-format
* Only resize param buf pool
* ggml-webgpu: Add binary op support for overlapping and non-contiguous.
* Add newline to binary.wgsl
* Append the test of binary op for src overlapping to test_bin_bcast.
* Remove unnecessary newline.
* vulkan: fix and enable cpy_tensor_async function
* use transfer_queue for async transfers on AMD, synchronize with timeline semaphore
* update offload_op logic
* fix missing transfer submission
* disable async transfer queue on AMD GCN
* revert op batch size change
* fix cpy_tensor_async checks
* Add model metadata loading from huggingface for use with other tests
* Add incremental chunking instead of full redownload, fix caching issue and add warning when it fails
* Add support for split models, load metadata from each individual split file, also avoid mmproj
* Code cleanup, revert incremental downloading
* Only compile when cpp-httplib has SSL support
* Fix formatting
This commit changes the runner for the gguf-publish workflow from
ubuntu-slim back to ubuntu-latest, which was updated in Commit
142cbe2ac6 ("ci : use new 1vCPU runner for
lightweight jobs (#19107)").
The motivation for this is that the action used in the workflow depends
on the docker daemon, which does not seem not available in the
ubuntu-slim runner. This is currently causing an error in the workflow
and preventing the gguf-publish workflow from running successfully.
Today was the the first time since the original change (I think) that
publish task has been run which may be why the issue was not noticed
before.
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/22481900566
* server : support multiple model aliases via comma-separated --alias
* server : update --alias description and regenerate docs
* server : multiple model aliases and tags
- address review feedback from ngxson
- --alias accepts comma-separated values (std::set, no duplicates)
- --tags for informational metadata (not used for routing)
- aliases resolve transparently in router via get_meta/has_model
- /v1/models exposes aliases and tags fields
* regenerate docs
* nits
* server : use first alias as model_name for backward compat
address review feedback from ngxson
* server : add single-model test for aliases and tags
The binary relies on model files that it tries to find. However, when
configuring the build directory to be parallel to the source tree those
heuristics fail.
This sets the working directory for the test executable to be the
source-tree which resolves this issue.
- adapt ggml-zendnn.cpp to the new lowoha::matmul interface
- update the ZenDNN git tag in CMake to the latest release (ZenDNN‑2026‑WW08)
- add static lib support in CMake
Tim Neumann
Piotr Wilkin (ilintar)
Aleksander Grygier
Johannes Gäßler
Georgi Gerganov
Roj234
lhez
YardenTal44
ymcki
Andreas Kieslinger
Eric Zhang
Sigbjørn Skjæret
Marcel Petrick
Max Krasnyansky
Todor Boinovski
Nikhil Jain
Masashi Yoshimura
Adrien Gallouët
ddh0
SamareshSingh
standby24x7
Mickael Desgranges
Charles Xu
shaofeiqi
Abhijit Ramesh
Ruben Ortlam
Aaron Teo
oobabooga
Dmitry Atamanov
Bartowski
Jayant Lohia
Sami Kama
Daniel Bevenius
Aman Gupta
Pascal
Jan Patrick Lehr
Neo Zhang
Vishal Singh