MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models

• URL: http://arxiv.org/abs/2105.14636v1
• Date: Sun, 30 May 2021 22:00:44 GMT
• Title: MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
• Authors: Zhewei Yao, Linjian Ma, Sheng Shen, Kurt Keutzer, Michael W. Mahoney
• Abstract summary: Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. We develop a novel MultiLevel structured Pruning framework, which uses three different levels of structured pruning: head pruning, row pruning, and block-wise sparse pruning.
• Score: 78.45898846056303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current approaches either only explore head pruning, which has a limited pruning ratio, or only focus on unstructured pruning, which has negligible effects on the real inference time and/or power consumption. To address these challenges, we develop a novel MultiLevel structured Pruning (MLPruning) framework, which uses three different levels of structured pruning: head pruning, row pruning, and block-wise sparse pruning. We propose using a learnable Top-k threshold, which employs an adaptive regularization to adjust the regularization magnitude adaptively, to select appropriate pruning ratios for different weight matrices. We also propose a two-step pipeline to combine block-wise pruning with head/row pruning to achieve high structured pruning ratios with minimum accuracy degradation. Our empirical results show that for \bertbase, with \textapprox20\% of remaining weights, \OURS can achieve an accuracy that is comparable to the full model on QQP/MNLI/\squad, with up to \textapprox3.69x speedup. Our framework has been open sourced~\cite{codebase}.

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