Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference

• URL: http://arxiv.org/abs/2407.11550v3
• Date: Fri, 16 Aug 2024 08:46:33 GMT
• Title: Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
• Authors: Yuan Feng, Junlin Lv, Yukun Cao, Xike Xie, S. Kevin Zhou,
• Abstract summary: Large Language Models have excelled in various fields but encounter challenges in memory and time efficiency. Recent efforts try to reduce KV cache size to a given memory budget by evicting vast non-critical cache elements during runtime.
• Score: 19.447729423696096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models have excelled in various fields but encounter challenges in memory and time efficiency due to the expanding Key-Value (KV) cache required for long-sequence inference. Recent efforts try to reduce KV cache size to a given memory budget by evicting vast non-critical cache elements during runtime, while preserving generation quality. Our revisiting of current eviction methods reveals that they fundamentally minimize an upper bound of the $L_1$ eviction loss between the pre- and post-eviction outputs of multi-head self-attention mechanisms. Moreover, our analysis indicates that the common practices of uniformly assigning budgets across attention heads harm their post-eviction generation quality. In light of these findings, we propose a simple yet effective adaptive budget allocation algorithm. This algorithm not only optimizes the theoretical loss upper bound but also reduces the $L_1$ eviction loss in practice by aligning with the varied characteristics across different heads. By integrating this algorithm into two state-of-the-art methods, we demonstrate the effectiveness of using adaptive budget allocation to optimize KV cache eviction. Extensive evaluations on 16 datasets and the Needle-in-a-Haystack test confirm significant performance improvements across various tasks.

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