SPViT: Enabling Faster Vision Transformers via Soft Token Pruning

• URL: http://arxiv.org/abs/2112.13890v1
• Date: Mon, 27 Dec 2021 20:15:25 GMT
• Title: SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
• Authors: Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang
• Abstract summary: Pruning, a traditional model compression paradigm for hardware efficiency, has been widely applied in various DNN structures. We propose a computation-aware soft pruning framework, which can be set up on vanilla Transformers of both flatten and CNN-type structures. Our framework significantly reduces the computation cost of ViTs while maintaining comparable performance on image classification.
• Score: 38.10083471492964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in industrial production difficult. Pruning, a traditional model compression paradigm for hardware efficiency, has been widely applied in various DNN structures. Nevertheless, it stays ambiguous on how to perform exclusive pruning on the ViT structure. Considering three key points: the structural characteristics, the internal data pattern of ViTs, and the related edge device deployment, we leverage the input token sparsity and propose a computation-aware soft pruning framework, which can be set up on vanilla Transformers of both flatten and CNN-type structures, such as Pooling-based ViT (PiT). More concretely, we design a dynamic attention-based multi-head token selector, which is a lightweight module for adaptive instance-wise token selection. We further introduce a soft pruning technique, which integrates the less informative tokens generated by the selector module into a package token that will participate in subsequent calculations rather than being completely discarded. Our framework is bound to the trade-off between accuracy and computation constraints of specific edge devices through our proposed computation-aware training strategy. Experimental results show that our framework significantly reduces the computation cost of ViTs while maintaining comparable performance on image classification. Moreover, our framework can guarantee the identified model to meet resource specifications of mobile devices and FPGA, and even achieve the real-time execution of DeiT-T on mobile platforms. For example, our method reduces the latency of DeiT-T to 26 ms (26%$\sim $41% superior to existing works) on the mobile device with 0.25%$\sim $4% higher top-1 accuracy on ImageNet. Our code will be released soon.

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