SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

• URL: http://arxiv.org/abs/2302.06845v2
• Date: Wed, 23 Aug 2023 03:56:24 GMT
• Title: SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
• Authors: Chen Tang, Kai Ouyang, Zenghao Chai, Yunpeng Bai, Yuan Meng, Zhi Wang, Wenwu Zhu
• Abstract summary: Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation for each layer. This paper proposes a novel method for efficiently searching for effective MPQ policies using a small proxy dataset.
• Score: 50.04951511146338
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using large-scale datasets such as ISLVRC-2012. This limits the practicality of MPQ in real-world deployment scenarios. To address this issue, this paper proposes a novel method for efficiently searching for effective MPQ policies using a small proxy dataset instead of the large-scale dataset used for training the model. Deviating from the established norm of employing a consistent dataset for both model training and MPQ policy search stages, our approach, therefore, yields a substantial enhancement in the efficiency of MPQ exploration. Nonetheless, using discrepant datasets poses challenges in searching for a transferable MPQ policy. Driven by the observation that quantization noise of sub-optimal policy exerts a detrimental influence on the discriminability of feature representations -- manifesting as diminished class margins and ambiguous decision boundaries -- our method aims to identify policies that uphold the discriminative nature of feature representations, i.e., intra-class compactness and inter-class separation. This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset. Our method offers several advantages, including high proxy data utilization, no excessive hyper-parameter tuning, and high searching efficiency. We search high-quality MPQ policies with the proxy dataset that has only 4% of the data scale compared to the large-scale target dataset, achieving the same accuracy as searching directly on the latter, improving MPQ searching efficiency by up to 300 times.

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