A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression

• URL: http://arxiv.org/abs/2406.11430v4
• Date: Sun, 03 Nov 2024 09:42:35 GMT
• Title: A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression
• Authors: Alessio Devoto, Yu Zhao, Simone Scardapane, Pasquale Minervini,
• Abstract summary: Existing approaches to reduce the Key-Value cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. We find a clear correlation between the $L$ and the attention scores over cached KV pairs, where a low $L$ of a key embedding leads to a high attention score during decoding. Our experimental results show that this simple strategy can reduce the KV cache size by 50% on language modelling and needle-in-a-haystack tasks and 90% on passkey retrieval tasks without losing accuracy.
• Score: 13.981807478365452
• License:
• Abstract: The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. We analyse the attention distributions in decoder-only Transformers-based models and observe that attention allocation patterns stay consistent across most layers. Surprisingly, we find a clear correlation between the $L_2$ and the attention scores over cached KV pairs, where a low $L_2$ of a key embedding usually leads to a high attention score during decoding. This finding indicates that the influence of a KV pair is potentially determined by the key embedding itself before being queried. Based on this observation, we compress the KV cache based on the $L_2$ of key embeddings. Our experimental results show that this simple strategy can reduce the KV cache size by 50% on language modelling and needle-in-a-haystack tasks and 90% on passkey retrieval tasks without losing accuracy. Moreover, without relying on the attention scores, this approach remains compatible with FlashAttention, enabling broader applicability.

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