Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT

• URL: http://arxiv.org/abs/2203.09055v1
• Date: Thu, 17 Mar 2022 03:33:47 GMT
• Title: Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT
• Authors: Jing Zhao, Yifan Wang, Junwei Bao, Youzheng Wu, Xiaodong He
• Abstract summary: We propose a fine- and coarse-granularity hybrid self-attention that reduces the cost through progressively shortening the computational sequence length in self-attention. We show that FCA offers a significantly better trade-off between accuracy and FLOPs compared to prior methods.
• Score: 22.904252855587348
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transformer-based pre-trained models, such as BERT, have shown extraordinary success in achieving state-of-the-art results in many natural language processing applications. However, deploying these models can be prohibitively costly, as the standard self-attention mechanism of the Transformer suffers from quadratic computational cost in the input sequence length. To confront this, we propose FCA, a fine- and coarse-granularity hybrid self-attention that reduces the computation cost through progressively shortening the computational sequence length in self-attention. Specifically, FCA conducts an attention-based scoring strategy to determine the informativeness of tokens at each layer. Then, the informative tokens serve as the fine-granularity computing units in self-attention and the uninformative tokens are replaced with one or several clusters as the coarse-granularity computing units in self-attention. Experiments on GLUE and RACE datasets show that BERT with FCA achieves 2x reduction in FLOPs over original BERT with <1% loss in accuracy. We show that FCA offers a significantly better trade-off between accuracy and FLOPs compared to prior methods.

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