@inproceedings{zhang-etal-2024-efficient,
title = "Efficient Sparse Attention needs Adaptive Token Release",
author = "Zhang, Chaoran and
Zou, Lixin and
Luo, Dan and
Luo, Xiangyang and
Li, Zihao and
Tang, Min and
Li, Chenliang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.837",
pages = "14081--14094",
abstract = "In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their {`}large{'} scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. Therefore, we propose to adaptively release resources from caches and rebuild the necessary key-value states. Particularly, we accomplish this by a lightweight controller module to approximate an ideal top-$K$ sparse attention. This module retains the tokens with the highest top-$K$ attention weights and simultaneously rebuilds the discarded but necessary tokens, which may become essential for future decoding. Comprehensive experiments in natural language generation and modeling reveal that our method is not only competitive with full attention in terms of performance but also achieves a significant throughput improvement of up to $\textbf{221.8}${\%}. The code for replication is available on the https://github.com/WHUIR/ADORE.",
}
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<abstract>In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. Therefore, we propose to adaptively release resources from caches and rebuild the necessary key-value states. Particularly, we accomplish this by a lightweight controller module to approximate an ideal top-K sparse attention. This module retains the tokens with the highest top-K attention weights and simultaneously rebuilds the discarded but necessary tokens, which may become essential for future decoding. Comprehensive experiments in natural language generation and modeling reveal that our method is not only competitive with full attention in terms of performance but also achieves a significant throughput improvement of up to 221.8%. The code for replication is available on the https://github.com/WHUIR/ADORE.</abstract>
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%0 Conference Proceedings
%T Efficient Sparse Attention needs Adaptive Token Release
%A Zhang, Chaoran
%A Zou, Lixin
%A Luo, Dan
%A Luo, Xiangyang
%A Li, Zihao
%A Tang, Min
%A Li, Chenliang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F zhang-etal-2024-efficient
%X In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. Therefore, we propose to adaptively release resources from caches and rebuild the necessary key-value states. Particularly, we accomplish this by a lightweight controller module to approximate an ideal top-K sparse attention. This module retains the tokens with the highest top-K attention weights and simultaneously rebuilds the discarded but necessary tokens, which may become essential for future decoding. Comprehensive experiments in natural language generation and modeling reveal that our method is not only competitive with full attention in terms of performance but also achieves a significant throughput improvement of up to 221.8%. The code for replication is available on the https://github.com/WHUIR/ADORE.
%U https://aclanthology.org/2024.findings-acl.837
%P 14081-14094
Markdown (Informal)
[Efficient Sparse Attention needs Adaptive Token Release](https://aclanthology.org/2024.findings-acl.837) (Zhang et al., Findings 2024)
ACL
- Chaoran Zhang, Lixin Zou, Dan Luo, Xiangyang Luo, Zihao Li, Min Tang, and Chenliang Li. 2024. Efficient Sparse Attention needs Adaptive Token Release. In Findings of the Association for Computational Linguistics ACL 2024, pages 14081–14094, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.