PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance

Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26809-26823, 2022.

Abstract

Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts of parameters, which restrict their deployment to real-world applications. To reduce the model size, researchers prune these models based on the weights’ importance scores. However, such scores are usually estimated on mini-batches during training, which incurs large variability/uncertainty due to mini-batch sampling and complicated training dynamics. As a result, some crucial weights could be pruned by commonly used pruning methods because of such uncertainty, which makes training unstable and hurts generalization. To resolve this issue, we propose PLATON, which captures the uncertainty of importance scores by upper confidence bound of importance estimation. In particular, for the weights with low importance scores but high uncertainty, PLATON tends to retain them and explores their capacity. We conduct extensive experiments with several Transformer-based models on natural language understanding, question answering and image classification to validate the effectiveness of PLATON. Results demonstrate that PLATON manifests notable improvement under different sparsity levels. Our code is publicly available at https://github.com/QingruZhang/PLATON.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-zhang22ao, title = {{PLATON}: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance}, author = {Zhang, Qingru and Zuo, Simiao and Liang, Chen and Bukharin, Alexander and He, Pengcheng and Chen, Weizhu and Zhao, Tuo}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {26809--26823}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/zhang22ao/zhang22ao.pdf}, url = {https://proceedings.mlr.press/v162/zhang22ao.html}, abstract = {Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts of parameters, which restrict their deployment to real-world applications. To reduce the model size, researchers prune these models based on the weights’ importance scores. However, such scores are usually estimated on mini-batches during training, which incurs large variability/uncertainty due to mini-batch sampling and complicated training dynamics. As a result, some crucial weights could be pruned by commonly used pruning methods because of such uncertainty, which makes training unstable and hurts generalization. To resolve this issue, we propose PLATON, which captures the uncertainty of importance scores by upper confidence bound of importance estimation. In particular, for the weights with low importance scores but high uncertainty, PLATON tends to retain them and explores their capacity. We conduct extensive experiments with several Transformer-based models on natural language understanding, question answering and image classification to validate the effectiveness of PLATON. Results demonstrate that PLATON manifests notable improvement under different sparsity levels. Our code is publicly available at https://github.com/QingruZhang/PLATON.} }
Endnote
%0 Conference Paper %T PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance %A Qingru Zhang %A Simiao Zuo %A Chen Liang %A Alexander Bukharin %A Pengcheng He %A Weizhu Chen %A Tuo Zhao %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-zhang22ao %I PMLR %P 26809--26823 %U https://proceedings.mlr.press/v162/zhang22ao.html %V 162 %X Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts of parameters, which restrict their deployment to real-world applications. To reduce the model size, researchers prune these models based on the weights’ importance scores. However, such scores are usually estimated on mini-batches during training, which incurs large variability/uncertainty due to mini-batch sampling and complicated training dynamics. As a result, some crucial weights could be pruned by commonly used pruning methods because of such uncertainty, which makes training unstable and hurts generalization. To resolve this issue, we propose PLATON, which captures the uncertainty of importance scores by upper confidence bound of importance estimation. In particular, for the weights with low importance scores but high uncertainty, PLATON tends to retain them and explores their capacity. We conduct extensive experiments with several Transformer-based models on natural language understanding, question answering and image classification to validate the effectiveness of PLATON. Results demonstrate that PLATON manifests notable improvement under different sparsity levels. Our code is publicly available at https://github.com/QingruZhang/PLATON.
APA
Zhang, Q., Zuo, S., Liang, C., Bukharin, A., He, P., Chen, W. & Zhao, T.. (2022). PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:26809-26823 Available from https://proceedings.mlr.press/v162/zhang22ao.html.

Related Material