Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning

• URL: http://arxiv.org/abs/2302.04453v1
• Date: Thu, 9 Feb 2023 06:14:00 GMT
• Title: Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning
• Authors: Yingchun Wang and Jingcai Guo and Song Guo and Weizhan Zhang
• Abstract summary: Mixed-precision quantization mostly predetermines the model bit-width settings before actual training. We propose a novel Data Quality-aware Mixed-precision Quantization framework, dubbed DQMQ, to dynamically adapt quantization bit-widths to different data qualities.
• Score: 22.31766292657812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal performance. Worse still, the conventional static quality-consistent training setting, i.e., all data is assumed to be of the same quality across training and inference, overlooks data quality changes in real-world applications which may lead to poor robustness of the quantized models. In this paper, we propose a novel Data Quality-aware Mixed-precision Quantization framework, dubbed DQMQ, to dynamically adapt quantization bit-widths to different data qualities. The adaption is based on a bit-width decision policy that can be learned jointly with the quantization training. Concretely, DQMQ is modeled as a hybrid reinforcement learning (RL) task that combines model-based policy optimization with supervised quantization training. By relaxing the discrete bit-width sampling to a continuous probability distribution that is encoded with few learnable parameters, DQMQ is differentiable and can be directly optimized end-to-end with a hybrid optimization target considering both task performance and quantization benefits. Trained on mixed-quality image datasets, DQMQ can implicitly select the most proper bit-width for each layer when facing uneven input qualities. Extensive experiments on various benchmark datasets and networks demonstrate the superiority of DQMQ against existing fixed/mixed-precision quantization methods.

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