Bin Bao

This lightning talk introduces torch.compile's C++ wrapper mode, a powerful feature that reduces CPU overhead and significantly improves model performance. As modern GPUs become increasingly powerful and compiler optimizations make GPU kernels run faster, CPU overhead has become more visible as the...

These sessions give participants an opportunity to meet some of the developers leading PyTorch to foster collaboration, gather feedback, and inspire contributions and collaboration. PyTorch Compiler: In 2022, we brought compiler technologies to PyTorch with TorchDynamo providing sound graph capture...

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...

Purge the GIL: Improved Torch.DataLoader - Michal Szolucha & Rostan Tabet, NVIDIA XFormers - Daniel Haziza, Meta AI  TritonCC: AOT Triton Workflow for TorchScript C++ Runtime - Sijia Chen & Huamin Li, Meta The PyTorch 2.0 Inference Story - Angela Yi, Bin Bao, Sheng Qin & Sherlock Huang, Meta Tensor...

This talk introduces AOTInductor, an Ahead-Of-Time version of TorchInductor. AOTInductor enables the compilation of exported models into shared libraries, offering a seamless and high-performance deployment solution for PyTorch models in non-Python environments. By leveraging torch.export,...