Sharpness-aware Quantization for Deep Neural Networks

By Jing Liu, Jianfei Cai, Bohan Zhuang. 2021

30 citations recorded by the source corpus.

Network quantization is a dominant paradigm of model compression. However, the abrupt changes in quantized weights during training often lead to severe loss fluctuations and result in a sharp loss landscape, making the gradients unstable and thus degrading the performance. Recently, Sharpness-Aware Minimization (SAM) has been proposed to smooth the loss landscape and improve the generalization performance of the models. Nevertheless, directly applying SAM to the quantized models can lead to perturbation mismatch or diminishment issues, resulting in suboptimal performance. In this paper, we propose a novel method, dubbed Sharpness-Aware Quantization (SAQ), to explore the effect of SAM in model compression, particularly quantization for the first time. Specifically, we first provide a unified view of quantization and SAM by treating them as introducing quantization noises and adversarial perturbations to the model weights, respectively. According to whether the noise and perturbation terms depend on each other, SAQ can be formulated into three cases, which are analyzed and compared comprehensively. Furthermore, by introducing an efficient training strategy, SAQ only incurs a little additional training overhead compared with the default optimizer (e.g., SGD or AdamW). Extensive experiments on both convolutional neural networks and Transformers across various datasets (i.e., ImageNet, CIFAR-10/100, Oxford Flowers-102, Oxford-IIIT Pets) show that SAQ improves the generalization performance of the quantized models, yielding the SOTA results in uniform quantization. For example, on ImageNet, SAQ outperforms AdamW by 1.2% on the Top-1 accuracy for 4-bit ViT-B/16. Our 4-bit ResNet-50 surpasses the previous SOTA method by 0.9% on the Top-1 accuracy.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Differentiable Model Compression via Pseudo Quantization Noise (2021, methodology): “Other mixed-precision quantization methods are based on Reinforcement Learning (Wang et al., 2019; Elthakeb et al., 2020; Liu et al., 2021), second-order optimization (Dong et al.”
NIPQ: Noise Injection Pseudo Quantization for Automated DNN Optimization (2022, methodology): “Various methods, such as RL-based [28, 29, 30], Hessian-based [31, 32, 33], and differentiable [34, 16] methods, have been proposed.”