Summary of Tokenselect: Efficient Long-context Inference and Length Extrapolation For Llms Via Dynamic Token-level Kv Cache Selection, by Wei Wu et al.
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
by Wei Wu, Zhuoshi Pan, Chao Wang, Liyi Chen, Yunchu Bai, Tianfu Wang, Kun Fu, Zheng Wang, Hui Xiong
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper tackles the limitations of Large Language Models (LLMs) in processing extended context sequences, a crucial requirement for modern applications. The authors identify two primary challenges: degrading performance when encountering out-of-distribution sequence lengths and excessively long inference times due to attention’s quadratic computational complexity. To address these issues, they propose Dynamic Token-Level KV Cache Selection (TokenSelect), a training-free method that efficiently handles long-context scenarios without sacrificing accuracy. TokenSelect leverages the observation of non-contiguous attention sparsity by selectively involving critical KV cache tokens in attention calculation through per-head soft voting. The authors also design a Selection Cache and an efficient dot product kernel to further accelerate TokenSelect, achieving up to 23.84x speedup in attention computation and up to 2.28x acceleration in end-to-end latency while maintaining superior performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to improve Large Language Models (LLMs) so they can handle longer pieces of text more efficiently. Right now, LLMs struggle with processing very long texts because it takes them a long time to figure out what’s important. The authors propose a new way to do this called TokenSelect, which is faster and more accurate than existing methods. It works by selecting the most important parts of the text and using those to make decisions, rather than looking at the whole text. This makes LLMs much faster and more efficient, allowing them to be used in applications where long texts are common. |
Keywords
» Artificial intelligence » Attention » Dot product » Inference » Token
