Howard Huang

A Case Study of Torch.compile-induced Timeouts in Distributed - Simon Fan & Brian Hirsh, Meta Activation Checkpoint 2: A New Graph-capture-based Activation Checkpoint - Jeffrey Wan, Meta Automatic Parallelism in PyTorch - Will Constable & Francisco Massa, Meta Breaking Less: Tackling Graph Breaks...

Pipeline parallelism is a technique employed in distributed deep learning that enhances model execution by dividing the model into distinct segments, or "stages." As large language models and other memory-intensive models become more common, pipeline parallelism has grown increasingly important for...