HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, Song Han


Abstract
Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation. To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search. We first construct a large design space with arbitrary encoder-decoder attention and heterogeneous layers. Then we train a SuperTransformer that covers all candidates in the design space, and efficiently produces many SubTransformers with weight sharing. Finally, we perform an evolutionary search with a hardware latency constraint to find a specialized SubTransformer dedicated to run fast on the target hardware. Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). When running WMT’14 translation task on Raspberry Pi-4, HAT can achieve 3× speedup, 3.7× smaller size over baseline Transformer; 2.7× speedup, 3.6× smaller size over Evolved Transformer with 12,041× less search cost and no performance loss. HAT is open-sourced at https://github.com/mit-han-lab/hardware-aware-transformers.
Anthology ID:
2020.acl-main.686
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7675–7688
Language:
URL:
https://aclanthology.org/2020.acl-main.686
DOI:
10.18653/v1/2020.acl-main.686
Bibkey:
Cite (ACL):
Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, and Song Han. 2020. HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7675–7688, Online. Association for Computational Linguistics.
Cite (Informal):
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (Wang et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.686.pdf
Video:
 http://slideslive.com/38928706
Code
 mit-han-lab/hardware-aware-transformers +  additional community code