feat: expose pytorch api for block sparse attention #375
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yzh119
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Jul 17, 2024
🤖 I have created a release *beep* *boop* --- ## [0.1.0](v0.0.9...v0.1.0) (2024-07-17) ### Features * Add mask to `merge_state_in_place` ([#372](#372)) ([e14fa81](e14fa81)) * expose pytorch api for block sparse attention ([#375](#375)) ([4bba6fa](4bba6fa)) * Fused GPU sampling kernel for joint top-k & top-p sampling ([#374](#374)) ([6e028eb](6e028eb)) --- This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please). Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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The block sparse attention (for any block size (R, C)) are hidden in flashinfer's codebase but it was never exposed explicitly in python. As requested in #367 , this PR implements the PyTorch APIs for block sparse attention, accordingly to our experiments, it can greatly accelerate attention computation with low density (10x for Tree Attention in Sequoia).