hf-kernels 0.1.4

Download cuda kernels

hf-kernels

Make sure you have torch==2.5.1+cu124 installed.

import torch

from hf_kernels import get_kernel

# Download optimized kernels from the Hugging Face hub
activation = get_kernel("kernels-community/activation")

# 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)

Docker Reference

build and run the reference example/basic.py in a Docker container with the following commands:

docker build --platform linux/amd64 -t kernels-reference -f docker/Dockerfile.reference .
docker run --gpus all -it --rm -e HF_TOKEN=$HF_TOKEN kernels-reference

Locking kernel versions

Projects that use setuptools can lock the kernel versions that should be used. First specify the accepted versions in pyproject.toml and make sure that hf-kernels is a build dependency:

[build-system]
requires = ["hf-kernels", "setuptools"]
build-backend = "setuptools.build_meta"

[tool.kernels.dependencies]
"kernels-community/activation" = ">=0.0.1"

Then run hf-kernel lock . in the project directory. This generates a kernels.lock file with the locked revisions. The locked revision will be used when loading a kernel with get_locked_kernel:

from hf_kernels import get_locked_kernel

activation = get_locked_kernel("kernels-community/activation")

Note: the lock file is included in the package metadata, so it will only be visible to hf-kernels after doing an (editable or regular) installation of your project.

Pre-downloading locked kernels

Locked kernels can be pre-downloaded by running hf-kernel download . in your project directory. This will download the kernels to your local Hugging Face Hub cache.

The pre-downloaded kernels are used by the get_locked_kernel function. get_locked_kernel will download a kernel when it is not pre-downloaded. If you want kernel loading to error when a kernel is not pre-downloaded, you can use the load_kernel function instead:

```python
from hf_kernels import load_kernel

activation = load_kernel("kernels-community/activation")