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Article | Open Access | Online First

Minimize Quantization Output Error with Bias Compensation

Cheng Gong1, Haoshuai Zheng2, Mengting Hu1, Zheng Lin3, Deng-Ping Fan2,4, Yuzhi Zhang1,5, Tao Li2,5( )
College of Software, Nankai University, Tianjin 300350, China
College of Computer Science, Nankai University, Tianjin 300350, China
Haihe Lab of ITAI, Tianjin 300450, China
Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University, Shenzhen 518045, China
BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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Abstract

Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder model deployment. In this paper, we propose Bias Compensation (BC) to minimize the output error, thus realizing ultra-low-precision quantization without model fine-tuning. Instead of optimizing the non-convex quantization process as in most previous methods, the proposed BC bypasses the step to directly minimize the quantizing output error by identifying a bias vector for compensation. We have established that the minimization of output error through BC is a convex problem and provides an efficient strategy to procure optimal solutions associated with minimal output error, without the need for training or fine-tuning. We conduct extensive experiments on Vision Transformer Models (ViTs) and Large Language Models (LLMs), and the results show that our method notably reduces quantization output error, thereby permitting ultra-low-precision post-training quantization and enhancing the task performance of models. Especially, BC improves the accuracy of ViT-B* with 4-bit PTQ4ViT by 36.89% on the ImageNet-1K task, and decreases the perplexity of OPT-350M with 3-bit GPTQ by 5.97 on WikiText-2. Our codes are publicly available at https://github.com/GongCheng1919/bias-compensation.

Keywords

convex optimizationquantizationminimizing output errorbias compensationlarge language model quantization

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CAAI Artificial Intelligence Research
DOI: 10.26599/AIR.2024.9150036
Cite this article:
Gong C, Zheng H, Hu M, et al. Minimize Quantization Output Error with Bias Compensation. CAAI Artificial Intelligence Research, 2024, https://doi.org/10.26599/AIR.2024.9150036

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Received: 12 May 2024
Revised: 17 June 2024
Accepted: 21 June 2024
Published: 11 September 2024
© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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