huggingface/kernels

Última actividad de origen registrada: 15 de julio de 2026 a las 12:48

Catálogo de SkillsMP actualizado: 29 de octubre de 2026 a las 18:30

skills recopiladas: 4

Estrellas en GitHub: 775
Forks en GitHub: 135

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README del repositorio

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# kernels <div align="center"> <a href="https://huggingface.co/kernels"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-dark.png"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-light.png"> <img alt="Kernels" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-light.png" style="max-width: 100%;"> </picture> </a> <p align="center"> <a href="https://pypi.org/project/kernels"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/kernels"></a> <a href="https://github.com/huggingface/kernels/tags"><img alt="GitHub tag" src="https://img.shields.io/github/v/tag/huggingface/kernels"></a> <a href="https://github.com/huggingface/kernels/actions/workflows/test_kernels.yaml"><img alt="Test kernels" src="https://img.shields.io/github/actions/workflow/status/huggingface/kernels/test_kernels.yaml?label=test"></a> </p> </div> <hr/> The Kernel Hub allows Python libraries and applications to load compute kernels directly from the [Hub](https://huggingface.co/). To support this kind of dynamic loading, Hub kernels differ from traditional Python kernel packages in that they are made to be: - Portable: a kernel can be loaded from paths outside `PYTHONPATH`. - Unique: multiple versions of the same kernel can be loaded in the same Python process. - Compatible: kernels must support all recent versions of Python and the different PyTorch build configurations (various CUDA versions and C++ ABIs). Furthermore, older C library versions must be supported. ## Components - You can load kernels from the Hub using the [`kernels`](kernels/) Python package. - If you are a kernel author, you can build your kernels with [kernel-builder](builder/). - Hugging Face maintains a set of kernels in [kernels-community](https://huggingface.co/kernels-community). ## 🚀 Quick Start Install the `kernels` Python package with `pip` (requires `torch>=2.5` and CUDA): ```bash pip install kernels ``` Here is how you would use the [activation](https://huggingface.co/kernels-community/activation) kernels from the Hugging Face Hub: ```python import torch from kernels import get_kernel # Download optimized kernels from the Hugging Face hub activation = get_kernel("kernels-community/activation", version=1) # Random tensor x = torch.randn((10, 10), dtype=torch.float16, device="cuda") # Run the kernel y = torch.empty_like(x) activation.gelu_fast(y, x) print(y) ``` Browse available kernels at [huggingface.co/kernels](https://huggingface.co/kernels). ## 🎯 Hardware Support The `kernel-builder` component of the project supports a number of hardware in different tiers: | Hardware | Kernels Support | Kernel-Builder Support | Kernels Validated in CI | Tier | | ---------- | --------------- | ---------------------- | ----------------------- | ---- | | CUDA | ✓ | ✓ | ✓ | 1 | | CPU | ✓ | ✓ | ✓ | 1 | | ROCm | ✓ | ✓ | ✗ | 2 | | XPU | ✓ | ✓ | ✗ | 2 | | Metal | ✓ | ✓ | ✗ | 2 | | TPU | ✓ | ✓ | ✗ | 2 | | Huawei NPU | ✓ | ✗ | ✗ | 3 | | Neuron | ✓ | x | x | 3 | > [!WARNING] > Neuron support is experimental and packages are not public yet. TPU support is is experimental. ## 📚 Documentation Read the [documentation of kernels and kernel-builder](https://huggingface.co/docs/kernels/).

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xpu-kernels

Desarrolladores de software

Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize),…

Idioma del texto original: inglés

2026-07-15

cuda-kernels

Desarrolladores de software

Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND…

Idioma del texto original: inglés

2026-07-01

cpu-kernels

Desarrolladores de software

Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration…

Idioma del texto original: inglés

2026-06-15

rocm-kernels

Desarrolladores de software

Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle,…

Idioma del texto original: inglés

2026-04-15

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