  Despite the remarkable performance of Vision Transformers (ViTs) in various
visual tasks, the expanding computation and model size of ViTs have increased
the demand for improved efficiency during training and inference. To address
the heavy computation and parameter drawbacks, quantization is frequently
studied in the community as a representative model compression technique and
has seen extensive use on CNNs. However, due to the unique properties of CNNs
and ViTs, the quantization applications on ViTs are still limited and
underexplored. In this paper, we identify the difficulty of ViT quantization on
its unique variation behaviors, which differ from traditional CNN
architectures. The variations indicate the magnitude of the parameter
fluctuations and can also measure outlier conditions. Moreover, the variation
behaviors reflect the various sensitivities to the quantization of each module.
The quantization sensitivity analysis and comparison of ViTs with CNNs help us
locate the underlying differences in variations. We also find that the
variations in ViTs cause training oscillations, bringing instability during
quantization-aware training (QAT). Correspondingly, we solve the variation
problem with an efficient knowledge-distillation-based variation-aware
quantization method. The multi-crop knowledge distillation scheme can
accelerate and stabilize the training and alleviate the variation's influence
during QAT. We also proposed a module-dependent quantization scheme and a
variation-aware regularization term to suppress the oscillation of weights. On
ImageNet-1K, we obtain a 77.66% Top-1 accuracy on the extremely low-bit
scenario of 2-bit Swin-T, outperforming the previous state-of-the-art quantized
model by 3.35%.
