PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination

Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal, Ashish Verma
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3690-3699, 2020.

Abstract

We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate transformer block outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with < 1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with < 1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at https://github.com/IBM/PoWER-BERT.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-goyal20a, title = {{P}o{WER}-{BERT}: Accelerating {BERT} Inference via Progressive Word-vector Elimination}, author = {Goyal, Saurabh and Choudhury, Anamitra Roy and Raje, Saurabh and Chakaravarthy, Venkatesan and Sabharwal, Yogish and Verma, Ashish}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {3690--3699}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/goyal20a/goyal20a.pdf}, url = {https://proceedings.mlr.press/v119/goyal20a.html}, abstract = {We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate transformer block outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with < 1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with < 1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at https://github.com/IBM/PoWER-BERT.} }
Endnote
%0 Conference Paper %T PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination %A Saurabh Goyal %A Anamitra Roy Choudhury %A Saurabh Raje %A Venkatesan Chakaravarthy %A Yogish Sabharwal %A Ashish Verma %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-goyal20a %I PMLR %P 3690--3699 %U https://proceedings.mlr.press/v119/goyal20a.html %V 119 %X We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate transformer block outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with < 1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with < 1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at https://github.com/IBM/PoWER-BERT.
APA
Goyal, S., Choudhury, A.R., Raje, S., Chakaravarthy, V., Sabharwal, Y. & Verma, A.. (2020). PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:3690-3699 Available from https://proceedings.mlr.press/v119/goyal20a.html.

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