mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-08-30 17:11:19 +02:00
Georgi Gerganov
70aff25250
* metal: dequantize q8_0 KV to f16 before flash attention Add a preprocessing pass for GGML_OP_FLASH_ATTN_EXT on the Metal backend: when the KV cache is quantized (Q8_0 for now), dequantize K and V into a contiguous F16 scratch buffer and run the existing F16 flash attention kernels on it, instead of the in-kernel dequantization path. - new kernel kernel_flash_attn_ext_dequant_to_f16<block_t, QK, deq_t4x4>: one thread per quant block (K then V), stride-aware so permuted KV is supported; instantiated for Q8_0 (extending to Q4_0/Q4_1/Q5_0/Q5_1 is one instantiation + one gate case) - the gate is type-only: dequantize whenever the KV is quantized, regardless of head sizes, GQA ratio or n_kv; the attention kernels themselves are untouched - the F16 copies live in the op's own scratch allocation (ggml_metal_op_flash_attn_ext_extra_dequant_f16); the KV pad kernel reads the dequantized buffers when the path is active - the FA pipeline getters gain a use_f16_kv flag selecting the existing f16 kernels and contiguous strides - ref: https://github.com/ggml-org/llama.cpp/pull/25556 Verification (M2 Ultra): - test-backend-ops test -o FLASH_ATTN_EXT: 4798/4798 pass, including the new q8_0 eval cases (decode/prompt, permuted, sinks+ALiBi+softcap, kv=113 pad path, kv=16384) - llama-perplexity on Qwen2.5-0.5B with -ctk q8_0 -ctv q8_0 matches the f16 KV reference (PPL 1.0008 vs 1.0008) Assisted-by: pi:llama.cpp/Qwen3.8-27B * metal : launch the FA KV dequant kernel separately for K and V Simplify kernel_flash_attn_ext_dequant_to_f16: it now dequantizes a single tensor (its own ne/nb and dst) with no is_v branching, and the op dispatches it twice with the same pipeline - once for K and once for V. The kargs struct shrinks to a single ne/nb set plus nblocks. Assisted-by: pi:llama.cpp/Qwen3.8-27B * metal : dequantize q4_0, q4_1, q5_0 and q5_1 KV to f16 before flash attention The dequant pass now covers all quantized KV types supported by the Metal flash attention kernels. The dequant kernel, kargs, scratch allocation and dispatch are type-generic, so each type is one kernel instantiation plus one gate case. Assisted-by: pi:llama.cpp/Qwen3.8-27B * metal : skip the redundant V dequant when V is a view of K In MLA-based models, the V of the FA op is a view of K (the first ne20 elements of each K row); the dequantized V is then a view of the dequantized K, so skip the second dequant dispatch, do not reserve the V scratch region, and let the pad and attention kernels read V from the K F16 buffer with K's strides. The detection follows the CUDA backend: V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs)) Also fix the FA pipeline getters: ns10/ns20 are function constants baked into the kernels and must be the actual K/V row widths as seen by the kernel. The dispatch now passes them explicitly (nb11_attn/nb10_attn, nb21_attn/nb20_attn) instead of the getters assuming contiguous F16 KV (ns20 = dv), which was wrong when V is read from K with K's row pitch (e.g. 576 vs 512). New test cases: 576/512 q8_0 (MLA shape, V is a view of K) at kv=113 (KV pad), nb=1 (vec) and nb=64 (non-vec). Assisted-by: pi:llama.cpp/Qwen3.8-27B * test : remove backend-specific wording from test-backend-ops comments Assisted-by: pi:llama.cpp/Qwen3.8-27B * pi : avoid backend mentions in test-backend-ops comments Assisted-by: pi:llama.cpp/Qwen3.8-27B * metal : rename the FA dequant_f16 identifiers to kv_f16 Assisted-by: pi:llama.cpp/Qwen3.8-27B * cont : clean-up * cont : remove TODO
llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / compile times / 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 [In Progress] | 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