Summary of Razorattention: Efficient Kv Cache Compression Through Retrieval Heads, by Hanlin Tang et al.
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads
by Hanlin Tang, Yang Lin, Jing Lin, Qingsen Han, Shikuan Hong, Yiwu Yao, Gongyi Wang
First submitted to arxiv on: 22 Jul 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 This paper addresses the challenges of deploying long-context language models in memory-constrained environments by proposing a novel compression technique for Key-Value (KV) cache. The authors observe that most attention heads focus on local context, while a few “retrieval heads” attend to all input tokens. They propose RazorAttention, a training-free KV cache compression algorithm that maintains a full cache for retrieval heads and discards remote tokens in non-retrieval heads. A novel compensation token mechanism is also introduced to recover information in dropped tokens. The authors evaluate RazorAttention across various large language models (LLMs) and demonstrate a reduction in KV cache size by over 70% without noticeable impacts on performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RazorAttention is a new way to make big language models work better on computers with limited memory. These models are very good at understanding natural language, but they need a lot of computer power to do it. The problem is that computers often don’t have enough memory for these models. The solution proposed in this paper is called RazorAttention and it helps reduce the amount of memory needed by keeping only the most important information. |
Keywords
* Artificial intelligence * Attention * Token
