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With deep learning models rapidly growing in size, systems-level solutionsfor large-model training are required. We present Amazon SageMaker modelparallelism, a software library that integrates with PyTorch, and enables easytraining of large models using model parallelism and other memory-savingfeatures. In contrast to existing solutions, the implementation of theSageMaker library is much more generic and flexible, in that it canautomatically partition and run pipeline parallelism over arbitrary modelarchitectures with minimal code change, and also offers a general andextensible framework for tensor parallelism, which supports a wider range ofuse cases, and is modular enough to be easily applied to new training scripts.The library also preserves the native PyTorch user experience to a much largerdegree, supporting module re-use and dynamic graphs, while giving the user fullcontrol over the details of the training step. We evaluate performance overGPT-3, RoBERTa, BERT, and neural collaborative filtering, and demonstratecompetitive performance over existing solutions.

Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training