Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

TitleQ-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Publication TypeConference Paper
Year of Publication2020
AuthorsShen, S.., Dong Z.., Ye J.., Ma L.., Yao Z.., Gholami A.., Mahoney M. W., & Keutzer K..
Published inProceedings of the AAAI-20 Conference
Abstract

Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However, BERT based models have a prohibitive memory footprint and latency. As a result, deploying BERT based models in resource constrained environments has become a challenging task. In this work, we perform an extensive analysis of fine-tuned BERT models using second order Hessian information, and we use our results to propose a novel method for quantizing BERT models to ultra low precision. In particular, we propose a new group-wise quantization scheme, and we use a Hessian based mix-precision method to compress the model further. We extensively test our proposed method on BERT downstream tasks of SST-2, MNLI, CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at most 2.3% performance degradation, even with ultra-low precision quantization down to 2 bits, corresponding up to 13× compression of the model parameters, and up to 4× compression of the embedding table as well as activations. Among all tasks, we observed the highest performance loss for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as well as visualization, we show that this is related to the fact that current training/fine-tuning strategy of BERT does not converge for SQuAD

Acknowledgment

We would like to thank Prof. Joseph Gonzalez, Prof. Dan Klein, and Prof. David Patterson for their valuable feedback. This work was supported by a gracious fund from Intel corporation, Berkeley Deep Drive (BDD), and Berkeley AI Research (BAIR) sponsors. We would like to thank the Intel VLAB team for providing us with access to their computing cluster. We also thank gracious support from Google for providing cloud compute. MWM would also like to acknowledge ARO, DARPA, NSF, ONR, and Intel for providing partial support of this work.

URLhttps://arxiv.org/pdf/1909.05840.pdf
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