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    <abstract>Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access to original training data due to security and privacy concerns has emerged the demand for zero-shot quantization. Most of the cutting-edge zero-shot quantization methods primarily 1) apply to computer vision tasks, and 2) neglect of overfitting problem in the generative adversarial learning process, leading to sub-optimal performance. Motivated by this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ) framework for the zero-shot quantization of various PLMs. The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem. We theoretically prove the convergence rate for the minimax optimization problem and this result can be applied to other nonconvex-PL minimax optimization frameworks. Extensive experiments on 11 tasks demonstrate that our method brings consistent and significant performance gains on both discriminative and generative PLMs, i.e., up to +6.98 average score. Furthermore, we empirically validate that our method can effectively improve the model generalization.</abstract>
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%0 Conference Proceedings
%T Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models
%A Zhu, Miaoxi
%A Zhong, Qihuang
%A Shen, Li
%A Ding, Liang
%A Liu, Juhua
%A Du, Bo
%A Tao, Dacheng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-zero
%X Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access to original training data due to security and privacy concerns has emerged the demand for zero-shot quantization. Most of the cutting-edge zero-shot quantization methods primarily 1) apply to computer vision tasks, and 2) neglect of overfitting problem in the generative adversarial learning process, leading to sub-optimal performance. Motivated by this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ) framework for the zero-shot quantization of various PLMs. The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem. We theoretically prove the convergence rate for the minimax optimization problem and this result can be applied to other nonconvex-PL minimax optimization frameworks. Extensive experiments on 11 tasks demonstrate that our method brings consistent and significant performance gains on both discriminative and generative PLMs, i.e., up to +6.98 average score. Furthermore, we empirically validate that our method can effectively improve the model generalization.
%R 10.18653/v1/2023.emnlp-main.696
%U https://aclanthology.org/2023.emnlp-main.696/
%U https://doi.org/10.18653/v1/2023.emnlp-main.696
%P 11305-11327

Markdown (Informal)

[Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models](https://aclanthology.org/2023.emnlp-main.696/) (Zhu et al., EMNLP 2023)

• Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (Zhu et al., EMNLP 2023)

ACL

• Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11305–11327, Singapore. Association for Computational Linguistics.