BinaryBERT: Pushing the Limit of BERT Quantization

• URL: http://arxiv.org/abs/2012.15701v1
• Date: Thu, 31 Dec 2020 16:34:54 GMT
• Title: BinaryBERT: Pushing the Limit of BERT Quantization
• Authors: Haoli Bai, Wei Zhang, Lu Hou, Lifeng Shang, Jing Jin, Xin Jiang, Qun Liu, Michael Lyu, Irwin King
• Abstract summary: We propose BinaryBERT, which pushes BERT quantization to the limit with weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscapes. Empirical results show that BinaryBERT has negligible performance drop compared to the full-precision BERT-base.
• Score: 74.65543496761553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit with weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscapes. Therefore, we propose ternary weight splitting, which initializes the binary model by equivalent splitting from a half-sized ternary network. The binary model thus inherits the good performance of the ternary model, and can be further enhanced by fine-tuning the new architecture after splitting. Empirical results show that BinaryBERT has negligible performance drop compared to the full-precision BERT-base while being $24\times$ smaller, achieving the state-of-the-art results on GLUE and SQuAD benchmarks.

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