torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

• URL: http://arxiv.org/abs/2004.09910v1
• Date: Tue, 21 Apr 2020 11:27:00 GMT
• Title: torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models
• Authors: Chiheon Kim, Heungsub Lee, Myungryong Jeong, Woonhyuk Baek, Boogeon Yoon, Ildoo Kim, Sungbin Lim, Sungwoong Kim
• Abstract summary: We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe. We show that each component is necessary to fully benefit from pipeline parallelism in such environment, and demonstrate the efficiency of the library.
• Score: 19.024035785367044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019). In particular, we develop a set of design components to enable pipeline-parallel gradient computation in PyTorch's define-by-run and eager execution environment. We show that each component is necessary to fully benefit from pipeline parallelism in such environment, and demonstrate the efficiency of the library by applying it to various network architectures including AmoebaNet-D and U-Net. Our library is available at https://github.com/kakaobrain/torchgpipe .

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