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Machine learning frameworks adopt iterative optimizers to train neuralnetworks. Conventional eager execution separates the updating of trainableparameters from forward and backward computations. However, this approachintroduces nontrivial training time overhead due to the lack of data localityand computation parallelism. In this work, we propose to fuse the optimizerwith forward or backward computation to better leverage locality andparallelism during training. By reordering the forward computation, gradientcalculation, and parameter updating, our proposed method improves theefficiency of iterative optimizers. Experimental results demonstrate that wecan achieve an up to 20% training time reduction on various configurations.Since our methods do not alter the optimizer algorithm, they can be used as ageneral "plug-in" technique to the training process.

Optimizer Fusion: Efficient Training with Better Locality and Parallelism