Towards Accurate Post-Training Quantization for Vision Transformer
Open accessAbstract
Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a widespread model compression technique, existing post-training quantization methods still cause severe performance drops. We find the main reasons lie in (1) the existing calibration metric is inaccurate in measuring the quantization influence for extremely low-bit representation, and (2) the existing quantization paradigm is unfriendly to the power-law distribution of Softmax. Based on these observations, we propose a novel Accurate Post-training Quantization framework for Vision Transformer, namely APQ-ViT. We first present a unified Bottom-elimination Blockwise Calibration scheme to optimize the calibration metric to perceive the overall quantization disturbance in a blockwise manner and prioritize the crucial quantization errors that influence more on the final output. Then, we design a Matthew-effect Preserving Quantization for Softmax to maintain the power-law character and keep the function of the attention mechanism. Comprehensive experiments on large-scale classification and detection datasets demonstrate that our APQ-ViT surpasses the existing post-training quantization methods by convincing margins, especially in lower bit-width settings (e.g., averagely up to 5.17% improvement for classification and 24.43% for detection on W4A4). We also highlight that APQ-ViT enjoys versatility and works well on diverse transformer variants.
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APQ-ViT implements an asymmetric linear quantization tailored to maintain the Softmax function's Matthew effect, with a quantization step size proportional to the Softmax output's maximum value.
“APQ-ViT [44] addresses the limitations of prior methods, which focused predominantly on mutual information while overlooking the Softmax function's Matthew effect. It implements an asymmetric linear quantization, tailored to maintain this effect during quantization, as shown in eq.10. The method hinges on a quantization step size proportional to the Softmax output's maximum value, promoting a more”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [44]
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- Authors
- Ding, Y., Qin, H., Yan, Q., Chai, Z., Liu, J., Wei, X., & Liu, X.
- Affiliations
- Meituan (China) (CN)
- Beihang University (CN)
- Venue
- Proceedings of the 30th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2022-10-10
- Pages
- 5380-5388
- OpenAlex
- W4304080501
- Cited by
- 79
- References
- 52
- Citations by year
- 2026: 20 · 2025: 40 · 2024: 13 · 2023: 6
- FWCI
- 5.35
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- The National Key Research and Development Plan of China (2021ZD0110503)
- National Natural Science Foundation of China (62022009,61872021)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- post-training quantization, vision transformer, model compression, calibration metrics, power-law distribution, attention mechanism, object detection, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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