Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction

Recently, vision transformers (ViT) have replaced convolutional neural network models in numerous tasks, including classification, detection, and segmentation. However, the high computational requirements of ViTs hinder their widespread implementation. To address this issue, researchers have proposed efficient hybrid transformer architectures that combine convolutional and transformer layers and optimize attention computation for linear complexity. Additionally, post-training quantization has been proposed as a means of mitigating computational demands. Combining quantization techniques and efficient hybrid transformer structures is crucial to maximize the acceleration of vision transformers on mobile devices. However, no prior investigation has applied quantization to efficient hybrid transformers. In this paper, at first, we discover that the straightforward manner to apply the existing PTQ methods for ViT to efficient hybrid transformers results in a drastic accuracy drop due to the following challenges: (i) highly dynamic ranges, (ii) zero-point overflow, (iii) diverse normalization, and (iv) limited model parameters (<5M). To overcome these challenges, we propose a new post-training quantization method, which is the first to quantize efficient hybrid vision transformers (MobileViTv1 and MobileViTv2) with a significant margin (an average improvement of 7.75%) compared to existing PTQ methods (EasyQuant, FQ-ViT, and PTQ4ViT). We plan to release our code at https://github.com/Q-HyViT.