Detached Error Feedback for Distributed SGD with Random Sparsification

An Xu, Heng Huang
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24550-24575, 2022.

Abstract

The communication bottleneck has been a critical problem in large-scale distributed deep learning. In this work, we study distributed SGD with random block-wise sparsification as the gradient compressor, which is ring-allreduce compatible and highly computation-efficient but leads to inferior performance. To tackle this important issue, we improve the communication-efficient distributed SGD from a novel aspect, that is, the trade-off between the variance and second moment of the gradient. With this motivation, we propose a new detached error feedback (DEF) algorithm, which shows better convergence bound than error feedback for non-convex problems. We also propose DEF-A to accelerate the generalization of DEF at the early stages of the training, which shows better generalization bounds than DEF. Furthermore, we establish the connection between communication-efficient distributed SGD and SGD with iterate averaging (SGD-IA) for the first time. Extensive deep learning experiments show significant empirical improvement of the proposed methods under various settings. Our reproducible codes and scripts for all experiments in this work will be made publicly available.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-xu22c, title = {Detached Error Feedback for Distributed {SGD} with Random Sparsification}, author = {Xu, An and Huang, Heng}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {24550--24575}, 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/xu22c/xu22c.pdf}, url = {https://proceedings.mlr.press/v162/xu22c.html}, abstract = {The communication bottleneck has been a critical problem in large-scale distributed deep learning. In this work, we study distributed SGD with random block-wise sparsification as the gradient compressor, which is ring-allreduce compatible and highly computation-efficient but leads to inferior performance. To tackle this important issue, we improve the communication-efficient distributed SGD from a novel aspect, that is, the trade-off between the variance and second moment of the gradient. With this motivation, we propose a new detached error feedback (DEF) algorithm, which shows better convergence bound than error feedback for non-convex problems. We also propose DEF-A to accelerate the generalization of DEF at the early stages of the training, which shows better generalization bounds than DEF. Furthermore, we establish the connection between communication-efficient distributed SGD and SGD with iterate averaging (SGD-IA) for the first time. Extensive deep learning experiments show significant empirical improvement of the proposed methods under various settings. Our reproducible codes and scripts for all experiments in this work will be made publicly available.} }
Endnote
%0 Conference Paper %T Detached Error Feedback for Distributed SGD with Random Sparsification %A An Xu %A Heng Huang %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-xu22c %I PMLR %P 24550--24575 %U https://proceedings.mlr.press/v162/xu22c.html %V 162 %X The communication bottleneck has been a critical problem in large-scale distributed deep learning. In this work, we study distributed SGD with random block-wise sparsification as the gradient compressor, which is ring-allreduce compatible and highly computation-efficient but leads to inferior performance. To tackle this important issue, we improve the communication-efficient distributed SGD from a novel aspect, that is, the trade-off between the variance and second moment of the gradient. With this motivation, we propose a new detached error feedback (DEF) algorithm, which shows better convergence bound than error feedback for non-convex problems. We also propose DEF-A to accelerate the generalization of DEF at the early stages of the training, which shows better generalization bounds than DEF. Furthermore, we establish the connection between communication-efficient distributed SGD and SGD with iterate averaging (SGD-IA) for the first time. Extensive deep learning experiments show significant empirical improvement of the proposed methods under various settings. Our reproducible codes and scripts for all experiments in this work will be made publicly available.
APA
Xu, A. & Huang, H.. (2022). Detached Error Feedback for Distributed SGD with Random Sparsification. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:24550-24575 Available from https://proceedings.mlr.press/v162/xu22c.html.

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