@inproceedings{gong-etal-2025-hata,
title = "{HATA}: Trainable and Hardware-Efficient Hash-Aware Top-$k$ Attention for Scalable Large Model Inference",
author = "Gong, Ping and
Yi, Jiawei and
Wang, Shengnan and
Zhang, Juncheng and
Jin, Zewen and
Zhou, Ouxiang and
Liu, Ruibo and
Xu, Guanbin and
Bai, Youhui and
Ye, Bowen and
Yuan, Kun and
Yang, Tong and
Zhang, Gong and
Chen, Renhai and
Wu, Feng and
Li, Cheng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1275/",
doi = "10.18653/v1/2025.findings-acl.1275",
pages = "24856--24871",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to mitigate redundant computations. While various top-$k$ attention mechanisms have been proposed to accelerate LLM inference by exploiting the inherent sparsity of attention, they often struggled to strike a balance between efficiency and accuracy. In this paper, we introduce HATA (Hash-Aware Top-$k$ Attention), a novel approach that systematically integrates low-overhead learning-to-hash techniques into the Top-$k$ attention process. Different from the existing top-k attention methods which are devoted to seeking an absolute estimation of qk score, typically with a great cost, HATA maps queries and keys into binary hash codes, and acquires the relative qk score order with a quite low cost, which is sufficient for realizing top-k attention. Extensive experiments demonstrate that HATA achieves up to 7.2$\times$ speedup compared to vanilla full attention while maintaining model accuracy. In addition, HATA outperforms the state-of-the-art top-$k$ attention methods in both accuracy and efficiency across multiple mainstream LLM models and diverse tasks. HATA is open source at https://github.com/gpzlx1/HATA."
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<abstract>Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to mitigate redundant computations. While various top-k attention mechanisms have been proposed to accelerate LLM inference by exploiting the inherent sparsity of attention, they often struggled to strike a balance between efficiency and accuracy. In this paper, we introduce HATA (Hash-Aware Top-k Attention), a novel approach that systematically integrates low-overhead learning-to-hash techniques into the Top-k attention process. Different from the existing top-k attention methods which are devoted to seeking an absolute estimation of qk score, typically with a great cost, HATA maps queries and keys into binary hash codes, and acquires the relative qk score order with a quite low cost, which is sufficient for realizing top-k attention. Extensive experiments demonstrate that HATA achieves up to 7.2\times speedup compared to vanilla full attention while maintaining model accuracy. In addition, HATA outperforms the state-of-the-art top-k attention methods in both accuracy and efficiency across multiple mainstream LLM models and diverse tasks. HATA is open source at https://github.com/gpzlx1/HATA.</abstract>
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%0 Conference Proceedings
%T HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
%A Gong, Ping
%A Yi, Jiawei
%A Wang, Shengnan
%A Zhang, Juncheng
%A Jin, Zewen
%A Zhou, Ouxiang
%A Liu, Ruibo
%A Xu, Guanbin
%A Bai, Youhui
%A Ye, Bowen
%A Yuan, Kun
%A Yang, Tong
%A Zhang, Gong
%A Chen, Renhai
%A Wu, Feng
%A Li, Cheng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F gong-etal-2025-hata
%X Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to mitigate redundant computations. While various top-k attention mechanisms have been proposed to accelerate LLM inference by exploiting the inherent sparsity of attention, they often struggled to strike a balance between efficiency and accuracy. In this paper, we introduce HATA (Hash-Aware Top-k Attention), a novel approach that systematically integrates low-overhead learning-to-hash techniques into the Top-k attention process. Different from the existing top-k attention methods which are devoted to seeking an absolute estimation of qk score, typically with a great cost, HATA maps queries and keys into binary hash codes, and acquires the relative qk score order with a quite low cost, which is sufficient for realizing top-k attention. Extensive experiments demonstrate that HATA achieves up to 7.2\times speedup compared to vanilla full attention while maintaining model accuracy. In addition, HATA outperforms the state-of-the-art top-k attention methods in both accuracy and efficiency across multiple mainstream LLM models and diverse tasks. HATA is open source at https://github.com/gpzlx1/HATA.
%R 10.18653/v1/2025.findings-acl.1275
%U https://aclanthology.org/2025.findings-acl.1275/
%U https://doi.org/10.18653/v1/2025.findings-acl.1275
%P 24856-24871
Markdown (Informal)
[HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference](https://aclanthology.org/2025.findings-acl.1275/) (Gong et al., Findings 2025)
ACL
- Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, and Cheng Li. 2025. HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24856–24871, Vienna, Austria. Association for Computational Linguistics.