  Transformer models have achieved state-of-the-art performance on various
domains of applications and gradually becomes the foundations of the advanced
large deep learning (DL) models. However, how to train these models over
multiple GPUs efficiently is still challenging due to a large number of
parallelism choices. Existing DL systems either rely on manual efforts to make
distributed training plans or apply parallelism combinations within a very
limited search space. In this approach, we propose Galvatron, a new system
framework that incorporates multiple popular parallelism dimensions and
automatically finds the most efficient hybrid parallelism strategy. To better
explore such a rarely huge search space, we 1) involve a decision tree to make
decomposition and pruning based on some reasonable intuitions, and then 2)
design a dynamic programming search algorithm to generate the optimal plan.
Evaluations on four representative Transformer workloads show that Galvatron
could perform automatically distributed training with different GPU memory
budgets. Among all evluated scenarios, Galvatron always achieves superior
system throughput compared to previous work with limited parallelism.
