Instructions to use pat883/attn-decode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use pat883/attn-decode with Kernels:
# !pip install kernels from kernels import get_kernel # a version (or an explicit revision) is required; see the "Files and versions" tab for the available ones kernel = get_kernel("pat883/attn-decode", version=1) - Notebooks
- Google Colab
- Kaggle
Add kernel card (library_name: kernels)
Browse files
README.md
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---
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library_name: kernels
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tags:
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- kernels
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- rocm
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---
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# Flash-Attention Decode (RDNA4/gfx1201)
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Dense, paged, and fp8 flash-attention decode kernels (GQA, sliding-window, block-table KV cache).
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Built with [kernel-builder](https://github.com/huggingface/kernel-builder) for AMD RDNA4 (gfx1201).
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Load with the [`kernels`](https://github.com/huggingface/kernels) library:
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```python
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from kernels import get_kernel
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kernel = get_kernel("pat883/attn-decode")
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```
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> Requires a ROCm PyTorch build (torch 2.10 / ROCm 7.x) on an RDNA4 card. Built variants:
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> `torch210-cxx11-rocm70` and `torch210-cxx11-rocm71`.
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