Numerical Optimizations for Weighted Low-rank Estimation on Language Models
Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin
Abstract
Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameters within the matrix with equal importance, which is a simple but unrealistic assumption. The parameters of a trained neural network model may affect the task performance unevenly, which suggests non-equal importance among the parameters. Compared to SVD, the decomposition method aware of parameter importance is the more practical choice in real cases. Unlike standard SVD, weighed value decomposition is a non-convex optimization problem that lacks a closed-form solution. We systematically investigated multiple optimization strategies to tackle the problem and examined our method by compressing Transformer-based language models.Further, we designed a metric to predict when the SVD may introduce a significant performance drop, for which our method can be a rescue strategy.The extensive evaluations demonstrate that our method can perform better than current SOTA methods in compressing Transformer-based language models.- Anthology ID:
- 2022.emnlp-main.91
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1404–1416
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.91
- DOI:
- 10.18653/v1/2022.emnlp-main.91
- Cite (ACL):
- Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, and Hongxia Jin. 2022. Numerical Optimizations for Weighted Low-rank Estimation on Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1404–1416, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Numerical Optimizations for Weighted Low-rank Estimation on Language Models (Hua et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.91.pdf