mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-07 21:41:08 +02:00
Jeff Bolz
0cae43063c
* vulkan: fall back to CPU for GET_ROWS with misaligned offsets
The Vulkan GET_ROWS shader asserts when a tensor's backing-buffer offset
plus view_offs is misaligned w.r.t. minStorageBufferOffsetAlignment
(see init_pushconst_tensor_offsets). Previously this caused a hard crash
on models using ggml_view + ggml_get_rows (e.g. Qwen3-TTS, Qwen3-VL).
Return false from supports_op() in the misaligned case so the scheduler
falls back to CPU, matching the existing pattern for PAD_REFLECT_1D and
other unsupported op/shape combinations.
Repro: llama-tts -m Qwen3-TTS-*.gguf -mm mmproj-*.gguf -ngl 99
Crash: GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && ...)) failed
* vulkan: trim comment for GET_ROWS misalign fallback
* vulkan: fix file corruption in gated_linear_attn struct
* vulkan: properly handle misaligned offsets in GET_ROWS quantized path
- get_rows_quant.comp was missing get_aoffset()/get_boffset()/get_doffset()
calls that are already present in get_rows.comp, causing GGML_ASSERT crashes
when GET_ROWS operates on views with non-zero view_offs, as produced by
KV cache slices in Qwen3-TTS and Qwen3-VL.
- Remove the defensive misalignment GGML_ASSERT in init_pushconst_tensor_offsets
for the binary push-constants specialization, since both get_rows.comp and
get_rows_quant.comp now correctly apply per-tensor base offsets.
- Remove the workaround CPU fallback in supports_op() for GET_ROWS, since the
Vulkan backend now handles misaligned offsets natively (no more bailout).
- Add backend test coverage with view_src0=true (ggml_view_4d into a padded
tensor) for F32, F16, Q4_0, Q4_K, Q8_0, and I32 types, exercising both the
non-quantized (get_rows.comp) and quantized (get_rows_quant.comp) paths
with non-zero view_offs that reproduce the original Qwen3-TTS crash.
* tests: trim redundant comments in test_get_rows vs0 region
* tests: trim redundant comments in test_get_rows vs0 region (follow-up)
* vulkan: bind tensor base for binary ops, pass full view_offs via push constants
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, MUL, etc.),
bind the view_src base and pass the full view_offs divided by type_size via
push constant misalign_offsets. This avoids truncation when misalign_bytes is
not a multiple of quantized block size.
ggml_vk_tensor_subbuffer gains a use_view_offs parameter. When false, the
binding points to vk_tensor_offset (base) and size includes view_offs.
init_pushconst_tensor_offsets<binary> computes a/b/d_offset directly from
tensor->view_offs, which is always row-aligned and therefore exact.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA RTX 5060 Ti).
* vulkan: bind aligned offset for binary ops, pass adjusted misalign via push constants
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, etc.), bind
the buffer to an aligned position near the view offset (not the tensor base)
and pass the adjusted misalignment via push constants.
ggml_vk_get_adjusted_misalign finds the smallest misalign that is both a
multiple of minStorageBufferOffsetAlignment and type_size, ensuring
misalign/type_size is exact (no truncation for quantized block types).
ggml_vk_tensor_subbuffer gains use_view_offs parameter. When false, binds
to (target - adjusted_misalign) instead of the view_src base, keeping the
offset small enough for 16-bit/8-bit push constant fields.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA RTX 5060 Ti).
* vulkan: bind aligned offset for binary ops, fix UMA offset mismatch
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, etc.), bind
the buffer to an aligned position near the view offset (not the tensor base)
and pass the adjusted misalignment via push constants.
Added ggml_vk_tensor_physical_offset to unify physical offset lookup across
UMA and non-UMA devices. On UMA, resolves via ggml_vk_host_get(tensor->data);
otherwise uses vk_tensor_offset(t) + t->view_offs. Both get_misalign_bytes and
the new ggml_vk_get_adjusted_misalign helper build on top of this function,
so buffer bindings and push constant offsets are always consistent regardless
of device memory model.
ggml_vk_get_adjusted_misalign finds the smallest misalign that is both a
multiple of minStorageBufferOffsetAlignment and type_size, ensuring
misalign/type_size is exact (no truncation for quantized block types) while
remaining small enough for 16-bit/8-bit push constant fields
(adjusted_misalign < lcm(align, type_size)).
ggml_vk_tensor_subbuffer gains use_view_offs parameter. When false, binds
to (physical_offset - adjusted_misalign) on both UMA and discrete GPUs,
fixing a bug where the UMA host_get path previously skipped the adjusted
misalign binding and returned the target offset directly.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA GeForce RTX 5060 Ti).
* finish misalignment fix
* supports_op changes for openvino/webgpu
---------
Co-authored-by: AiChiTuDouPian <15327701848@qq.com>
llama.cpp
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF
# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
|
|
|
Description
The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
a wide range of hardware - locally and in the cloud.
- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
The llama.cpp project is build on top of the ggml library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cpprepo and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain