Summarization
This blog introduces PipeTransformer, a holistic solution that combines elastic pipeline-parallel and data-parallel for distributed training using PyTorch Distributed APIs. More specifically, PipeTransformer incrementally freezes layers in the pipeline, packs remaining active layers into fewer GPUs, and forks more pipeline replicas to increase the data-parallel width. Evaluations on ViT and BERT models show that compared to the state-of-the-art baseline, PipeTransformer attains up to 2.83× speedups without accuracy loss.
Reference
[1] Li, S., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li,T., Paszke, A., Smith, J., Vaughan, B., Damania, P., et al. Pytorch Distributed:  Experiences on Accelerating Dataparallel Training. Proceedings of the VLDB Endowment,13(12), 2020
[2] Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT, 2019