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Recent advances in deep learning are driven by the growing scale ofcomputation, data, and models. However, efficiently training large-scale modelson distributed systems requires an intricate combination of data, operator, andpipeline parallelism, which exerts heavy burden on machine learningpractitioners. To this end, we propose AutoDDL, a distributed trainingframework that automatically explores and exploits new parallelization schemeswith near-optimal bandwidth cost. AutoDDL facilitates the description andimplementation of different schemes by utilizing OneFlow's Split, Broadcast,and Partial Sum (SBP) abstraction. AutoDDL is equipped with an analyticalperformance model combined with a customized Coordinate Descent algorithm,which significantly reduces the scheme searching overhead. We conductevaluations on Multi-Node-Single-GPU and Multi-Node-Multi-GPU machines usingdifferent models, including VGG and Transformer. Compared to theexpert-optimized implementations, AutoDDL reduces the end-to-end training timeby up to 31.1% and 10% for Transformer and up to 17.7% and 71.5% for VGG on thetwo parallel systems, respectively.

AutoDDL: Automatic Distributed Deep Learning with Near-Optimal Bandwidth Cost