by                                                Team PyTorch                                             We are excited to an

PyTorch 2.3 Release Blog

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2024-04-25 00:30:03

by Team PyTorch

We are excited to announce the release of PyTorch® 2.3 (release note)! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance regressions or graph breaks. Tensor Parallelism improves the experience for training Large Language Models using native PyTorch functions, which has been validated on training runs for 100B parameter models. As well, semi-structured sparsity implements semi-structured sparsity as a Tensor subclass, with observed speedups of up to 1.6 over dense matrix multiplication.

This release is composed of 3393 commits and 426 contributors since PyTorch 2.2. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.3. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

Allows for PyTorch code that contains triton kernels to be executed natively using torch.compile. This enables users to migrate code containing triton kernels from eager PyTorch to torch.compile without running into performance regressions or graph breaks. Native support also creates an opportunity for Torch Inductor to precompile the user-defined Triton kernel as well as better organize code around the Triton kernel allowing for further optimizations.

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