Andrew Marshall [Thu, 29 Jan 2026 14:56:39 +0000 (09:56 -0500)]
webui: Update Svelte to fix effect_update_depth_exceeded errors (#19144)
The upstream fix is first available in 5.38.2, so constrain to at least
that version.
Rebuild pre-compiled webui index.html.gz based on these changes.
See also:
https://github.com/ggml-org/llama.cpp/issues/16347
https://github.com/huntabyte/bits-ui/issues/1687
https://github.com/sveltejs/svelte/issues/16548
This commit updates the `GraniteHybridModel` class' modify_tensors
function to properly delegate to `Mamba2Model.modify_tensors` and
`GraniteMoeModel.modify_tensors` using 'yield from' instead of 'return'.
The motivation for this is that modify_tensors is a generator function
(it uses 'yield from'), but the two calls above use return statements
but don't yield anything which means that the the caller of this
function will not receive any yielded values from it. And this causes
layer tensors to be silently dropped during conversion.
The syclcompat/math.hpp is not used anymore. The change that intrduced it was successfuly reverted (https://github.com/ggml-org/llama.cpp/pull/17826).
This include path will become obsolete and dropped in oneAPI 2026.0 effectively breaking ggml-sycl builds.
Oleksandr Kuvshynov [Wed, 28 Jan 2026 11:35:54 +0000 (06:35 -0500)]
vulkan: handle device dedup on MacOS + Vega II Duo cards (#19058)
Deduplication here relied on the fact that vulkan would return unique
UUID for different physical GPUs. It is at the moment not always the case.
On Mac Pro 2019 running Mac OS, with 2 Vega II Duo cards (so, 4 GPU total),
MotlenVK would assign same UUID to pairs of GPUs, unless they
are connected with Infinity Fabric.
See more details here: KhronosGroup/MoltenVK#2683.
The right way is to fix that in MoltenVK, but until it is fixed,
llama.cpp would only recognize 2 of 4 GPUs in such configuration.
The deduplication logic here is changed to only filter GPUs if UUID is
same but driver is different.
Daniel Bevenius [Wed, 28 Jan 2026 04:59:30 +0000 (05:59 +0100)]
sampling : remove sampling branching in output_reserve (#18811)
* sampling : remove sampling branching in output_reserve
This commit updates output_reserve in llama-context.cpp to always
allocate sampling buffers regardless of whether sampling is needed for
the current batch.
The motivation for this is to avoid reallocations and branching based on
the sampling requirements of the batch.
The XIELU operation was incorrectly using static_cast to convert
float parameters to uint32_t, which converted numeric values instead
of preserving IEEE 754 bit patterns. This caused incorrect values
to be interpreted by the GPU shader.
* Use reinterpret_cast to preserve float bit patterns when passing
through uint32_t params buffer
* Update WGSL shader parameter types from u32 to f32
* Re-enable XIELU support (was disabled due to numerical issues)
Fixes NMSE test failures for XIELU operation on WebGPU backend.
neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though
Co-authored-by: James Contini <redacted> Co-authored-by: Neha Abbas <redacted> Co-authored-by: Abhijit Ramesh <redacted>
* Remove extra code and format
* Add ops documentation (finally)
* ggml webgpu: add SOFTPLUS unary operator
Implements SOFTPLUS (log(1 + exp(x))) with f16/f32 support. Uses f32
precision for intermediate calculations to prevent f16 overflow.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* Follow Vulkan backend numerical stability pattern
* ggml webgpu: add EXPM1 unary operator
Implements EXPM1 (exp(x) - 1) with f16/f32 support.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* ggml webgpu: add FLOOR unary operator
Implements FLOOR (rounds down to nearest integer) with f16/f32 support.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* ggml webgpu: add CEIL unary operator
Implements CEIL (rounds up to nearest integer) with f16/f32 support.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* ggml webgpu: add ROUND unary operator
Implements ROUND (rounds to nearest integer) with f16/f32 support.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* ggml webgpu: add TRUNC unary operator
Implements TRUNC (truncates towards zero) with f16/f32 support.
* Add shader implementation and 4 variants (f32/f16, inplace/non-inplace)
* Register pipelines and device support
* docs : update WebGPU support for unary operators (FLOOR, CEIL, ROUND, TRUNC, EXPM1, SOFTPLUS)
* Updates to webgpu get_memory
* Move shared state (webgpu_context) and device creation out of registration context, device context, and buffer context, and move into backend context
* Small cleanup
* Move Instance, Device, Adapter, Device creation, and capabilities to global state while moving Queue, pipelines, and buffers to per-thread state.
* Cleanups
* More cleanup
* Move staging_buf mutex to global context
* Resolve merge
* Resolve merge
* Resolve merge
* Clean up merge errors, delete forward declaration, and run clang-format
* Rename device_init to backend_init
* Move webgpu_context to backend_context
* Move buffer context members into global context and refactor function calls
* Run clang-format
* Remove commends
* Move parameter buffers to per-thread, add single memset_tensor param buf
* Fix CI compilation issue
* Fix builds for emscripten not supporting subgroups
Gaurav Garg [Tue, 27 Jan 2026 06:52:44 +0000 (06:52 +0000)]
[CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full (#19042)
* [CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full
With pipeline parallelism, during prompt processing, the CPU-side CUDA command buffer gets full, stalling the CPU. Due to this, enough work doesn't get submitted to the GPU, causing bubbles in the GPU timeline.
Fix this by setting the CUDA environment variable CUDA_SCALE_LAUNCH_QUEUES to 4x to increase the command buffer size.
* Set the env variable in the CUDA backend registry allocation
Daniel Bevenius [Tue, 27 Jan 2026 05:16:00 +0000 (06:16 +0100)]
common : clarify HTTPS build options in error message (#19103)
* common : clarify HTTPS build options in error message
This commit updates the https error message to provide clearer
instructions for users who encounter the "HTTPS is not supported" error.
The motivation for this is that it might not be clear to users that only
one of these options are needed to enable HTTPS support.
The LLAMA_OPENSSL option is also added to the message to cover all
possible build configurations.
* clarify that OpenSSL is the default for HTTPS support
Aman Gupta [Sun, 25 Jan 2026 15:25:58 +0000 (23:25 +0800)]
ggml-cpu: Use tiled FA for prompt-processing (#19012)
* ggml-cpu: Use tiled FA for prompt-processing
the FA performance is gimped on CPU on long contexts because it essentially uses a vector kernel. This PR adds a tiled FA for PP. Perf tuning for tile sizes done on a AMD EPYC single-socket 64-c machine.
Daniel Bevenius [Sun, 25 Jan 2026 06:31:42 +0000 (07:31 +0100)]
common : use two decimal places for float arg help messages (#19048)
* common : use two decimal places for float arg help messages
This commit updates the help messages for various command-line arguments
in arg.cpp to display floating-point default values with two decimal
places instead of one.
The motivation for this changes is that currently only having one decimal
place means that values generated using --help or llama-gen-docs will not
display the correct values.
For example, currently the value of top-p in tools/server/README.md is
`0.9`, but the default value is actually '0.95'. And running
llama-gen-docs does not update this value as it uses the output from the
help message, which shows only one decimal place, so the values look
like they are unchanged.
Daniel Bevenius [Fri, 23 Jan 2026 08:01:36 +0000 (09:01 +0100)]
model-conversion : use BUILD_DIR variable in all scripts (#19015)
This commit modifies all the utility scripts to use an optional
BUILD_DIR variable/argument to specify the build directory.
The motivation for this is that Commit 3d55846a5c626e2e608db8c24fa9ee6defaacca9 ("model-conversion : add
BUILD_DIR variable to run-converted-model scripts") introduced this
variable to the causal and embeddings scripts, but I missed the scripts
in the utils directory.
Jeff Bolz [Wed, 21 Jan 2026 17:01:40 +0000 (11:01 -0600)]
vulkan: Remove transfer_ctx, do everything in compute_ctx. (#18945)
* vulkan: Remove transfer_ctx, do everything in compute_ctx.
We had a bug where a set_tensor_async (using transfer_ctx) didn't get
submitted before the graph_compute (using compute_ctx) that came after
it. To avoid this sort of issue, just do everything in compute_ctx.
Remove transfer_cmd_pool, which was already unused.
Jeff Bolz [Wed, 21 Jan 2026 15:22:02 +0000 (09:22 -0600)]
vulkan: Use mul_mat_vec_id for small values of n (#18918)
Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and
update the indexing calculations in get_offsets.
Mat-vec is faster than mat-mat for small values of n. We don't get the same
reuse of the weights as in the non-ID path, but with this the cost is linear
in n rather than n>1 being far slower than n==1.
Matthieu Coudron [Wed, 21 Jan 2026 06:52:46 +0000 (07:52 +0100)]
gguf: display strerrno when cant load a model (#18884)
I've had issues loading models with llama-server:
[44039] E gguf_init_from_file: failed to open GGUF file 'mistral-7b-v0.1.Q8_0.gguf'
and I was sure it could access the file. Seems like --models-dir and
--models-presets dont interact like I thought they would but I salvaged
this snippet that helps troubleshooting
[44039] E gguf_init_from_file: failed to open GGUF file 'mistral-7b-v0.1.Q8_0.gguf' (errno No such file or directory)
Oliver Simons [Wed, 21 Jan 2026 01:34:29 +0000 (02:34 +0100)]
CUDA: Fix builds for older CCCL versions by ifdefing strided_iterator (#18964)
* CUDA: Fix builds for older CCCL versions by ifdefing strided_iterator
Strided iterator was added in [CCCL
3.1](https://github.com/NVIDIA/cccl/releases/tag/v3.1.0), which is packaged into
[CTK
13.1](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#id5)
Daniel Bevenius [Tue, 20 Jan 2026 05:55:24 +0000 (06:55 +0100)]
convert : use n_groups instead of hardcoded values in reshape (#18929)
* convert : use n_groups instead of hardcoded values in reshape
This commit modifies the conversion script for NemotronHModel to use
the 'n_groups' hyperparameter, and allow Python to calculate the the
last dimension, using -1, when reshaping the 'mixer.norm.weight' tensor.
* use self.n_group instead of self.hparams["n_groups"]
overview / pkg / ggml / sources / llama.cpp / log