Summary of Post-training Sparse Attention with Double Sparsity, by Shuo Yang and Ying Sheng and Joseph E. Gonzalez and Ion Stoica and Lianmin Zheng
Post-Training Sparse Attention with Double Sparsity
by Shuo Yang, Ying Sheng, Joseph E. Gonzalez, Ion Stoica, Lianmin Zheng
First submitted to arxiv on: 11 Aug 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 “Double Sparsity” technique aims to improve the inference process for large language models by reducing Key-Value (KV) cache accesses. This is achieved by combining token sparsity and channel sparsity, which focuses on identifying important tokens and feature channels, respectively. The method involves offline calibration to make it efficient at runtime, allowing accurate and efficient identification of important tokens. Experimental results demonstrate that Double Sparsity can achieve significant memory usage reduction while maintaining accuracy across various tasks. Specifically, it brings up to a 14.1x acceleration in attention operations and a 1.9x improvement in end-to-end inference on GPUs. The code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are powerful tools that can help us understand and generate human-like text. However, they can be slow and use a lot of memory. This paper introduces a new way to make large language models faster and more efficient by reducing the amount of information they need to process at one time. The method works by identifying the most important parts of the input text and only processing those parts. This makes it much faster than traditional methods, while still maintaining good accuracy. |
Keywords
» Artificial intelligence » Attention » Inference » Token
