* Optimize Metal Tensor API usage for matmul2d
Separates the Metal Tensor API (matmul2d) path in kernel_mul_mm into its own standalone kernel, gated by GGML_METAL_HAS_TENSOR.
The legacy simdgroup_matrix kernel is preserved under #else.
Previously both paths were interleaved via #ifdef blocks within a single kernel, forcing the tensor path to share the legacy kernel's data layout and threadgroup memory scheme. Splitting the kernel enabled memory and dispatch optimizations that weren't possible when the two paths shared code structure.
* cont : cleanup
* cont : cleanup
* cont : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Change the default `ftype` in `llama_model_quantize_params` from
`LLAMA_FTYPE_MOSTLY_Q5_1` to `LLAMA_FTYPE_MOSTLY_Q8_0`.
In case some external program naively uses the default quantization
params, we should probably default to a known-good type like Q8_0 rather
than Q5_1, which is rather old.
* opt arc770 for Q4_0
* add for Q4_0
* update the script
* add help script for windows
* update guide
* fix format issue
* convert from dos to unix for format issue
* fix missed -sm parameter
* switch ubuntu-latest to ubuntu-slim
* Fix the path for upload so CI doesn't fail
* Update .github/workflows/build-and-test-snapdragon.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use -slim image for key check and consistent naming for artifact dir
Signed-off-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* Remove check-secret extra job
* move QDC key check for Run QDC jobs step specifically
* add a step before to check the secret for qdc jobs
---------
Signed-off-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* ggml-webgpu: add tile flash attention fallback
* ggml-webgpu: add new fields and discard usage of mnk for tile version
* ggml-webgpu: modify the vec path to discard the mnk parameter
* ggml-webgpu: enable flash attention vec and tile version for broswer
* ggml-webgpu: stagging KV for flash attention tile version
* formatting
* turn on subgroup uniformity check
* remove Q_TILE as it is always 1 for vec path
* make row_max and exp_sum to local register
* make different bindings with same underlying buffer to have the same usage flags
* move path selection into the shader library and have the host consume a single flash-attn decision object.
* turn off skip_validation and address buffer overlapping when nwg==1
* formatting
* merge binding when kv overlap
This change implements the third requested change in issue 20429.
Because defaults.sampling contains the reasoning budget token count and
the reasoning budget message, it's not necessary to assign them to
struct variables.
* hexagon: restore HTP_OPMASK_QUEUE
* hexagon: honor OPMASK_SKIP_COMPUTE in hmx-matmul
* hex-prof: restore op profiling
* hex-prof: enable PMU
* hexagon: simplify and improve op-queuing with full profiling support
Add separate profile descriptors.
* hexagon: remove opsync and rename opmask into opstage
opsync is no longer needed since the profiler is fully async now.
opmask name was confusing and opstage is more accurate.
* hexagon: refactor opbatch queue handling
* hexagon: add iface hooks for enabling profiler from the host
Also move all the PMU setup stuff out of the hex-utils since it's not inteded for normal use.
* hexagon: make profiler mode configurable
On older devices getting PMU counters is expensive so it's now optional.
* hexagon: add support for setting profiler pmu events from env
* hexagon: simplify profiler output (no need to print buffs, etc)
* hexagon: simplify pmu counter formating
* hexagon: add a simple profile post-proc tool
* hex-prof: add support for reading logs from stdin
* hexagon: document GGML_HEXAGON_PROFILE
* hex-prof: update default width for dims field
* hex-prof: fix linter warnings and errors
* Update ggml/src/ggml-hexagon/htp/htp-ops.h
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update scripts/snapdragon/ggml-hexagon-profile.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Trivikram Reddy <tamarnat@qti.qualcomm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add the tests that we want to run on external CI
* remove extra files
* Fixes python issues, reove the deadlock on CI
* remove unecessary changes
* use override to ty.toml
* fix pre-commit and try tests with secret in external repo not upstream
* skip if key is unavailable
* Fix feedback
* switch hexagon to snapdragon
* cleanup
* fix secrets
* remove the copyrights at the top of the files
When testing claude code against llama.cpp, I noticed that only
n_past 18577 was used even when context was 60k or more. The log
in llama-server says:
```
slot update_slots: id 3 | task 10342 | old: ... ; cch= | defa0;You are
slot update_slots: id 3 | task 10342 | new: ... ; cch= | 1c8b4;
```
I observed that the cch value changed every time. Reading about that,
the x-anthropic-billing-header system message seems to be specially
handled inside of the anthropic api. I could remove it, but there
is a meaningful string sometimes included at the end. So instead,
I just replace the changing cch checksum with fffff.
I'm treating this as an anthropic message body API detail - I think this
is the right way to do this, but by all means please correct me!
It's always 5 hexadecimal characters, but I've written the replacement
defensively in case they change the protocol.
* model-conversion : fix mmproj output file name [no ci]
This commit updates the convert-model.sh script to properly handle
mmproj output files.
The motivation for this that currently the same name as the original
model is used as the mmproj file, which causes the original model to
be overwritten and no mmproj-<model_name>.gguf to be created.
* model-conversion : use MODEL_NAME [no ci]
* gitignore: add AGENTS.local
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
* gitignore: rename AGENTS.local to AGENTS.local.md
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
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.
* upgrade oneAPI to 2025.3.3
* update
* seperate SYCL CI and support release binary package for ubuntu 24
* add dependence
* remove wrong copy lines
* add missed line
* remove other task to test the release for SYCL
* rm more for test release
* fix file name
* correct the error in running
* support build for fp32/fp16
* rm ubuntu-24-sycl-fp16 for duplicated
* refactor build setting
* update guide for ubuntu 24 release package, restore the release.yml for other backend
* user docker replace to install oneAPI
* use download installation package to replace docker
* use wget to download and install oneapi, replace the apt cmd
* enable ccache for oneAPI installation
* fix format error
* enable cache for oneAPI installation
* update guide
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/build-sycl.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* sycl : fused MoE mul_mat_vec_q for TG
Create an MMVQ kernel so ggml_sycl_mul_mat_id can consolidate
n_experts_used matmuls in a single kernel launch. The kernel
also reads expert IDs directly, removing a per-call host sync.
This is similar to the CUDA backend's ggml_cuda_mul_mat_vec_q*
paths.
All types supported in the current MMVQ are supported here as well:
Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0
It will fall back to the existing per-expert path when src0 has been rewritten
by opt_for_reorder(), and for any shape the fused path doesn't handle.
test-backend-ops passes for supported type/shape combos.
Benchmark: Qwen3-Next-35B-A3B Q4_K_M on Intel Arc B70 (SYCL0),
baseline 707c0b7a6, 16k context, -fa 0.
build/bin/llama-bench -hf unsloth/Qwen3.5-35B-A3B-GGUF:Q4_K_M \
-p 1024 -n 128 -d 16384 -ngl 99 -fa 0 -ub 2048 -r 2 -dev SYCL0
Before (3 runs on 707c0b7a6):
| test | run 1 | run 2 | run 3 |
| --------------- | ----------------:| ----------------:| ----------------:|
| pp1024 @ d16384 | 533.26 ± 4.87 | 535.20 ± 2.78 | 524.27 ± 3.10 |
| tg128 @ d16384 | 33.47 ± 0.02 | 33.31 ± 0.02 | 33.17 ± 0.05 |
After (3 runs on 707c0b7a6 + this patch):
| test | run 1 | run 2 | run 3 |
| --------------- | ----------------:| ----------------:| ----------------:|
| pp1024 @ d16384 | 534.06 ± 0.97 | 531.95 ± 0.02 | 520.94 ± 20.10 |
| tg128 @ d16384 | 45.85 ± 0.21 | 45.95 ± 0.45 | 46.22 ± 0.12 |
disclosure: Claude wrote it, but I reviewed and understand the implementation
(albeit my C is a little rusty).
* sycl: also support nvfp4 and mxfp4 expert types
* sycl: terser comments/nested dispatch in response to review
* sycl: more comment cleanup in mmvq.cpp/hpp
---------
Co-authored-by: Debian <aaron@openllmi.net.bots.is>
* shader(im2col): implement the im2col shader
* shader(im2col): clean the formatting issues
* shader(im2col): clean the editorconfig checker warning
* fix(shader): address the workgroup issues of im2col and conv2d
In #11362 hip graph was disabled by default as, at the time, its performance impact was negative. Due to improvements in rocm and our usage and construction of graphs this is no longer true, so lets change the default.
* Only run webgpu CI on my fork
* Implement set_tensor_async
* Implement synchronize api
* Implement event creation and deletion API
* Cleanup
* Cleanup
* Comment out jobs for local CI run
* Add webgpu only workflow
* Delete .github/workflows/build-webgpu.yml
* Cleanup
* Cleanup
* Update API with function handlers
* Run clang-format
* Replace one-shot buffer with a direct queue.WriteBuffer using the buffer context
* fused rms_norm_mul + mul
* Add GGML_WEBGPU_DISABLE_FUSION for being able to disable kernel fusion.
* Decouple num_fused_ops from webgpu_context; misc cleanup
* Fix eps handling and remove disable_fusion.
* Fix not to use c++20 initializers.
* chat: fix parallel_tool_calls default setting based on model capabilities, add tests for parallel tool calls and structured outputs
* Fix ty errors.
* Fix flake8 err
* sycl: size mul_mat_id staging buffers by routed rows
Previously src1_contiguous/dst_contiguous in ggml_sycl_mul_mat_id were
sized to ggml_nelements(src1/dst), which over-allocates when ne12 > 1
and can fail with UR_RESULT_ERROR_OUT_OF_HOST_MEMORY on Level Zero for
MoE models (notably with --cpu-moe). Size them by the actual number of
routed rows (ids->ne[1] * n_ids) instead.
* sycl: add bf16 mul_mat fast path via DNNL
When src0 is BF16 (commonly the case for lm_head / output.weight), the
existing f16 path is skipped because bf16 isn't covered, and the f32
fallback dequantizes the entire src0 slab to f32 in a single pool alloc
(row_diff*ne00 floats). For large-vocab models this can reach several
GB and fail with UR_RESULT_ERROR_OUT_OF_HOST_MEMORY on Level Zero.
Add a bf16xbf16 -> f32 DNNL matmul fast path that uses the bf16 storage
in place and only materializes a small src1 bf16 conversion buffer. bf16
matmul accumulates in f32, so it's correct even when the op requests
GGML_PREC_F32 (as lm_head does).
- gemm.hpp: map bfloat16 to dnnl::memory::data_type::bf16.
- convert.{hpp,cpp}: expose ggml_get_to_bf16_sycl for f32/f16/bf16 -> bf16.
- ggml-sycl.cpp: take the bf16 path early in ggml_sycl_op_mul_mat_sycl
when DNNL and GGML_SYCL_HAS_BF16 are both available.
* mtmd, llama : add HunyuanVL vision-language model support
- add LLM_ARCH_HUNYUAN_VL with M-RoPE (XD-RoPE) support
- add PROJECTOR_TYPE_HUNYUANVL with PatchMerger vision encoder
- add HunyuanVL-specific M-RoPE position encoding for image tokens
- add GGUF conversion for HunyuanVL vision and text models
- add smoke test in tools/mtmd/tests.sh
* fix: fix HunyuanVL XD-RoPE h/w section order
* fix: Remove redundant code
* convert : fix HunyuanOCR / HunyuanVL conversion
- Tested locally: both HunyuanOCR and HunyuanVL-4B convert to GGUF
- successfully and produce correct inference output on Metal (F16 / Q8_0).
* clip : fix -Werror=misleading-indentation in bilinear resize
* fix CI: convert_hf_to_gguf type check error
- convert_hf_to_gguf.py: give HunyuanVLTextModel.__init__ an explicit `dir_model: Path` parameter so ty can infer the type for load_hparams instead of reporting `Unknown | None`.
---------
Co-authored-by: wendadawen <wendadawen@tencent.com>
This change refactors the reasoning_budget_message parameter from the
common params into the sampling parameters specifically. It also removes
the reasoning_budget common parameter and standardizes on the existing
reasoning_budget_tokens parameter in the sampling configuration.
Issue: https://github.com/ggml-org/llama.cpp/issues/20429
Original PR: https://github.com/ggml-org/llama.cpp/pull/20297
* ggml(webgpu): fix the busy-polls in Emscripten in the waitAny after #20618, and remove the busy webgpu log
* Merge with upstream
* Fix GET_ROWS packed integer NaN when using f16 as memory buffer in shader quants
* Update Unary wgsl EXP and EXPM1 for f16 stability
* Fix GET_ROWS IQ4_XS strcut for NaN f16 canonicalization
* Fix numerical percision for unary sqrt when working with f16
* Fix NaN canonicalization for packed integers using f16
* Update err threshold for binary div ops when using f16
* backend: Keep one Dawn/WebGPU instance alive for the lifetime of the static backend
* clean: uncomment existing code logs
* clean: clean the unncessary debug info
* Refactor and generalize dequant helpers
* Remove deprecated quant structs
* Refactor shader defines to reduce repetition
* Remove error override for F16 type
* fix: fix the accidential removal of the proper initialization of ctx
* clean: clean legacy and format code
* fix: did not modify tests ops
* shader(conv2d): add conv2d shader kernels and pass f32 and f16 tests
* shader(conv2d): fix the out of bounds memory access in the weight indexing
* shader(conv2d): clean unused variables and optimize the computation
* merge: use the new entries function
* clean: address the formatting issues
* clean: address the warning issues
* clear: clean the shader editorconfig-checker issues
* clear: clean the shader editorconfig-checker with utf-8
---------
Co-authored-by: Jeremy J. Hartmann <jeremy@mtion.tv>
Developer-Ecosystem-Engineering