Summary of Hashattention: Semantic Sparsity For Faster Inference, by Aditya Desai et al.
HashAttention: Semantic Sparsity for Faster Inference
by Aditya Desai, Shuo Yang, Alejandro Cuadron, Ana Klimovic, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed HashAttention approach tackles the challenge of leveraging token sparsity in scaled dot-product attention (SDPA) to improve AI system performance. By casting pivotal token identification as a recommendation problem, HashAttention efficiently identifies and uses only significant tokens for attention computation, leading to improved efficiency. The method encodes keys and queries in Hamming space using learned mapping functions and employs bitwise operations to identify pivotal tokens. This results in a reduced number of tokens used (by a factor of 1/32) while maintaining average quality loss within 0.6 points on the LongBench dataset. HashAttention outperforms LightLLM and gpt-fast by 3-6 times and 2.5-4.5 times, respectively, on an Nvidia-L4 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HashAttention is a new approach to improve AI system performance by efficiently using token sparsity in scaled dot-product attention (SDPA). The method works by identifying the most important tokens for attention computation and only using those. This makes it faster and more efficient than other methods. It can reduce the number of tokens used by 1/32, which means less computational power is needed. |
Keywords
» Artificial intelligence » Attention » Dot product » Gpt » Token
