Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions

• URL: http://arxiv.org/abs/2303.08595v1
• Date: Tue, 14 Mar 2023 02:47:57 GMT
• Title: Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
• Authors: Kaiqi Zhao, Animesh Jain, Ming Zhao
• Abstract summary: Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. This paper presents Automatic Attention Pruning (AAP), an adaptive, attention-based, structured pruning approach to automatically generate small, accurate, and hardware-efficient models.
• Score: 5.445935252764351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on unstructured pruning, which yields models that cannot efficiently run on commodity hardware; and they often require users to manually explore and tune the pruning process, which is time-consuming and often leads to sub-optimal results. To address these limitations, this paper presents Automatic Attention Pruning (AAP), an adaptive, attention-based, structured pruning approach to automatically generate small, accurate, and hardware-efficient models that meet user objectives. First, it proposes iterative structured pruning using activation-based attention maps to effectively identify and prune unimportant filters. Then, it proposes adaptive pruning policies for automatically meeting the pruning objectives of accuracy-critical, memory-constrained, and latency-sensitive tasks. A comprehensive evaluation shows that AAP substantially outperforms the state-of-the-art structured pruning works for a variety of model architectures. Our code is at: https://github.com/kaiqi123/Automatic-Attention-Pruning.git.

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