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In data-parallel synchronous training of deep neural networks, differentdevices (replicas) run the same program with different partitions of thetraining batch, but weight update computation is repeated on all replicas,because the weights do not have a batch dimension to partition. This can be abottleneck for performance and scalability in typical language models withlarge weights, and models with small per-replica batch size which is typical inlarge-scale training. This paper presents an approach to automatically shardthe weight update computation across replicas with efficient communicationprimitives and data formatting, using static analysis and transformations onthe training computation graph. We show this technique achieves substantialspeedups on typical image and language models on Cloud TPUs, requiring nochange to model code. This technique helps close the gap between traditionallyexpensive (ADAM) and cheap (SGD) optimizers, as they will only take a smallpart of training step time and have similar peak memory usage. It helped us toachieve state-of-the-art training performance in Google's MLPerf 0.6submission.

Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training