SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

TL;DR

SepLLM is a novel framework that accelerates large language models by compressing segments between separator tokens, significantly reducing computation and memory usage without sacrificing performance.

Contribution

The paper introduces SepLLM, a plug-and-play method that condenses segment information into separator tokens, enabling faster inference and training for large language models.

Findings

Over 50% reduction in KV cache on GSM8K-CoT benchmark.

Maintains comparable performance with standard models.

Effective in processing sequences of 4 million tokens or more.

Abstract

Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless separator tokens (i.e., punctuations) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM's effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.