Streaming Attention Approximation via Discrepancy Theory
This work addresses the problem of memory requirements for large language models, which is significant for natural language processing researchers and practitioners working with long-context token generation.
The authors tackled the problem of high memory requirements in large language models for long-context token generation, achieving ε-approximating attention computations with their BalanceKV algorithm, which exhibits empirically validated performance improvements. The algorithm provides strong theoretical guarantees and outperforms existing methods on various long context benchmarks.
Large language models (LLMs) have achieved impressive success, but their high memory requirements present challenges for long-context token generation. In this paper we study the streaming complexity of attention approximation, a key computational primitive underlying token generation. Our main contribution is BalanceKV, a streaming algorithm for $ε$-approximating attention computations based on geometric process for selecting a balanced collection of Key and Value tokens as per Banaszczyk's vector balancing theory. We complement our algorithm with space lower bounds for streaming attention computation. Besides strong theoretical guarantees, BalanceKV exhibits empirically validated performance improvements over existing methods, both for attention approximation and end-to-end performance on various long context benchmarks.