Video by PyTorch via YouTube
PyTorch 2.13 introduces updates across attention, compilation, distributed training, memory efficiency, Python support, and accelerator platforms. Highlights include FlexAttention support on Apple Silicon with up to approximately 12x speedup over SDPA on sparse patterns, the CuTeDSL "Native DSL" backend for key GPU operations, and nn.LinearCrossEntropyLoss to reduce peak GPU memory by up to 4x during large-vocabulary language model training.
On Wednesday, July 22, 2026, at 11 a.m. PT, PyTorch maintainers and contributors will provide a brief overview of the PyTorch 2.13 release and answer questions from the community live.
Topics will include:
– FlexAttention support on Apple Silicon and deterministic backward computation on CUDA
– The CuTeDSL "Native DSL" backend for Inductor
– nn.LinearCrossEntropyLoss for reducing peak GPU memory
– torchcomms for large-cluster training
– FSDP2 communication overlap improvements
– Torch wheel support for Python 3.15 on Linux, including free-threaded 3.15t builds
– Expanded ROCm, Arm, and Intel XPU platform support
The live Q&A will feature expert panelists Alban Desmaison, Andrey Talman, and Piotr Bialecki, with Chris Gottbrath moderating.
PyTorch 2.13 includes 3,328 commits from 526 contributors since PyTorch 2.12.
Register for the live Q&A today!