Summary of Tree Attention: Topology-aware Decoding For Long-context Attention on Gpu Clusters, by Vasudev Shyam et al.
Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters
by Vasudev Shyam, Jonathan Pilault, Emily Shepperd, Quentin Anthony, Beren Millidge
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our research introduces Tree Attention, an algorithm that efficiently computes exact attention computation across multiple GPUs in parallel. This formulation reveals that the reduction across the sequence axis can be computed in parallel through a tree reduction, enabling cross-device decoding to be performed up to 8x faster than state-of-the-art approaches like Ring Attention. Our implementation requires significantly less communication volume and incurs 2x less peak memory compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our research is about making computers work better when they need to understand long pieces of text. We created a new way for computers to do this called Tree Attention. This new method lets computers work faster and more efficiently, especially when working with very big models like Llama 3.1-8B. Our code is available online so others can use it too. |
Keywords
* Artificial intelligence * Attention * Llama
