Abstract
Improving Transformer efficiency has become increasingly attractive recently. A wide range of methods has been proposed, e.g., pruning, quantization, new architectures and etc. But these methods are either sophisticated in implementation or dependent on hardware. In this paper, we show that the efficiency of Transformer can be improved by combining some simple and hardware-agnostic methods, including tuning hyper-parameters, better design choices and training strategies. On the WMT news translation tasks, we improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU.- Anthology ID:
- 2021.findings-emnlp.357
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4227–4233
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.357
- DOI:
- 10.18653/v1/2021.findings-emnlp.357
- Cite (ACL):
- Ye Lin, Yanyang Li, Tong Xiao, and Jingbo Zhu. 2021. Bag of Tricks for Optimizing Transformer Efficiency. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4227–4233, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Bag of Tricks for Optimizing Transformer Efficiency (Lin et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.357.pdf
- Code
- lollipop321/mini-decoder-network