Training-Free Exponential Context Extension via Cascading KV Cache

TL;DR

This paper introduces a cascading KV cache mechanism that extends the context window of transformers efficiently, maintaining relevant tokens without increasing cache size, thus enabling long-sequence processing with reduced latency.

Contribution

It proposes a novel cascading sub-cache buffer system that selectively retains important tokens, outperforming linear caching methods in maintaining context and reducing prefill latency.

Findings

Outperforms linear caching in key benchmarks

Retains better retrieval accuracy at 1 million tokens

Reduces prefill latency by a factor of 6.8

Abstract

The transformer's context window is vital for tasks such as few-shot learning and conditional generation as it preserves previous tokens for active memory. However, as the context lengths increase, the computational costs grow quadratically, hindering the deployment of large language models (LLMs) in real-world, long sequence scenarios. Although some recent key-value caching (KV Cache) methods offer linear inference complexity, they naively manage the stored context, prematurely evicting tokens and losing valuable information. Moreover, they lack an optimized prefill/prompt stage strategy, resulting in higher latency than even quadratic attention for realistic context sizes. In response, we introduce a novel mechanism that leverages cascading sub-cache buffers to selectively retain the most relevant tokens, enabling the model to maintain longer context histories without increasing the cache size. Our approach outperforms linear caching baselines across key benchmarks, including streaming perplexity, question answering, book summarization, and passkey retrieval, where it retains better retrieval accuracy at 1M tokens after four doublings of the cache size of 65K. Additionally, our method reduces prefill stage latency by a factor of 6.8 when compared to flash attention on 1M tokens. These innovations not only enhance the computational efficiency of LLMs but also pave the way for their effective deployment in resource-constrained environments, enabling large-scale, real-time applications with significantly reduced latency.