Summary of Moa: Mixture Of Sparse Attention For Automatic Large Language Model Compression, by Tianyu Fu et al.
MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression
by Tianyu Fu, Haofeng Huang, Xuefei Ning, Genghan Zhang, Boju Chen, Tianqi Wu, Hongyi Wang, Zixiao Huang, Shiyao Li, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Mixture of Attention (MoA) method effectively mitigates the memory and throughput demands of Large Language Models (LLMs) in long contexts by automatically tailoring distinct sparse attention configurations to different heads and layers. MoA constructs a search space of various attention patterns, evaluates potential configurations, and pinpoints the optimal sparse attention compression plan. This approach increases the effective context length, boosts retrieval accuracy, and narrows the capability gaps between sparse and dense models. MoA achieves significant improvements in GPU memory reduction (1.2-1.4x), decode throughput (6.6-8.2x), and minimal impact on performance, making it a valuable tool for LLMs in long contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super powerful tools that can process very long texts, but they need lots of memory and computing power to do so. Some clever researchers have figured out how to make them more efficient by using something called “sparse attention”. This means the model only focuses on certain parts of the text instead of the whole thing. The problem is that different parts of the text might require different levels of attention, but most methods use a one-size-fits-all approach. The new method, called Mixture of Attention (MoA), solves this problem by allowing each part of the model to adjust its level of attention based on the length of the text. This makes MoA much more effective than previous methods and allows LLMs to process even longer texts with ease. |
Keywords
» Artificial intelligence » Attention » Context length
