On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model Inference
This work addresses memory constraints for deploying LLMs in resource-limited environments, offering an incremental improvement in cache management.
The paper tackles the high memory cost of key-value caches in large language models by analyzing and improving eviction policies, introducing RoCo, which outperforms prior methods in experiments across prefilling and decoding stages.
Despite the recent success associated with Large Language Models (LLMs), they are notably cost-prohibitive to deploy in resource-constrained environments due to their excessive memory and computational demands. In addition to model parameters, the key-value cache is also stored in GPU memory, growing linearly with batch size and sequence length. As a remedy, recent works have proposed various eviction policies for maintaining the overhead of key-value cache under a given budget. This paper embarks on the efficacy of existing eviction policies in terms of importance score calculation and eviction scope construction. We identify the deficiency of prior policies in these two aspects and introduce RoCo, a robust cache omission policy based on temporal attention scores and robustness measures. Extensive experimentation spanning prefilling and auto-regressive decoding stages validates the superiority of RoCo. Finally, we release EasyKV, a versatile software package dedicated to user-friendly key-value constrained generative inference. Code available at https://github.com/DRSY/EasyKV.