PyTorch 2.12 Release Live Q&A

PyTorch 2.12 introduced major updates across compilation, distributed systems, export, graph capture, and accelerator support. Highlights included a new device-agnostic torch.accelerator.Graph API, up to 100x faster batched eigenvalue decomposition on CUDA, support for microscaling quantization formats in torch.export.save, and expanded CUDA, ROCm, XPU, MPS, and Arm platform support.

On May 20, Andrey Talman, Alban Desmaison, and Joe Spisak joined moderator Chris Gottbrath for a live Q&A covering the PyTorch 2.12 release. The panel provided an overview of the release and answered questions from the community live.

Topics included:

• Device-Agnostic Accelerator Graph Capture
• ProcessGroup Support in Custom Ops
• torch.export.save Support for Microscaling Quantization Formats
• Fused Adagrad Optimizer Support
• FlightRecorder Updates
• Multi-GPU and Multi-Node Profiling Improvements
• Updated Backend Selection for torch.linalg.eigh on CUDA
• Expanded CUDA, ROCm, XPU, MPS, and Arm Platform Support

Panelists: Andrey Talman is a Software Engineer at Meta, primarily focused on open source releases for PyTorch and its ecosystem libraries. He works on release management, continuous integration, and process improvements, ensuring high-quality and timely delivery of PyTorch and related projects.

Alban Desmaison is a Research Engineer at Meta and the Lead Core Maintainer of PyTorch.

Joe Spisak is Vice President of Product and Head of Open Source at Reflection AI. He is a PyTorch core maintainer, serves on the PyTorch Foundation Governing Board, and previously worked at Meta.

Moderator

Chris Gottbrath is a Group Technical Program Manager supporting PyTorch at Meta and Chair of the PyTorch Foundation Marketing Committee.