Instructions to use Efficient-Large-Model/Sol-Attn-Kernel-Source with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Efficient-Large-Model/Sol-Attn-Kernel-Source with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Efficient-Large-Model/Sol-Attn-Kernel-Source") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - kernels | |
| - cuda | |
| - attention | |
| - triton | |
| - cute-dsl | |
| # Sol-Attn Kernel Builder Source | |
| This repository contains the Hugging Face Kernel Builder packaging for | |
| [Sol-Attn](https://github.com/NVlabs/Sana/tree/sol-engine/techniques/sparse_backends/sol_attn). | |
| The kernel implementation is pinned to NVIDIA's `NVlabs/Sana` commit | |
| [`8a26fb0`](https://github.com/NVlabs/Sana/commit/8a26fb0ec9e353125ead798cb2e312d5ce48cded). | |
| The packaged Python sources preserve that implementation. The only source | |
| relocation required by Kernel Hub is converting internal `sol_attn.*` imports | |
| to package-relative imports, so the kernel remains loadable under the | |
| version-isolated module name assigned by `kernels.get_kernel(...)`. | |
| Published builds are loaded from | |
| [`Efficient-Large-Model/Sol-Attn`](https://huggingface.co/Efficient-Large-Model/Sol-Attn): | |
| ```python | |
| from kernels import get_kernel | |
| kernel = get_kernel("Efficient-Large-Model/Sol-Attn", version=1) | |
| out = kernel.sol_attn( | |
| q, # Contiguous BF16 CUDA tensor [batch, tokens, heads, 128]. | |
| k, # Same shape, dtype, layout, and device as q. | |
| v, # Same shape, dtype, layout, and device as q. | |
| tau=1.0, | |
| thresh_type="exact", | |
| ) | |
| ``` | |
| See [SOURCE.md](SOURCE.md) for provenance and the verification command. | |