SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation

ACL ARR 2024 December Submission1798 Authors

Abstract:

Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation.
Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context
impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs.
Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced.
Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase.
Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies.
Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: NLP in resource-constrained settings

Contribution Types: Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 1798