PyTorch 发布: 2.9 Release Notes

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# PyTorch 2.9.0 Release Notes - [Highlights](#highlights) - [Backwards Incompatible Changes](#backwards-incompatible-changes) - [Deprecations](#deprecations) - [New Features](#new-features) - [Improvements](#improvements) - [Bug fixes](#bug-fixes) - [Performance](#performance) - [Documentation](#documentation) - [Developers](#developers) - [Security](#security) # Highlights Unstable (API-Unstable) Updates to the stable libtorch ABI for third-party C++/CUDA extensions Symmetric memory that enables easy programming of multi-GPU kernels The ability to arbitrarily toggle error or resume on graph breaks in torch.compile Expanded wheel variant support to include ROCm, XPU and CUDA 13 FlexAttention enablement on Intel GPUs Flash decoding optimization based on FlexAttention on X86 CPU ARM Platform improvements and optimizations Enablement of Linux aarch64 binary wheel builds across all supported CUDA versions For more details about these highlighted features, you can look at the [release blogpost](https://pytorch.org/blog/pytorch-2-9/). Below are the full release notes for this release. # Backwards Incompatible Changes ## Min supported Python version is now 3.10 (#162310) The minimum version of Python required for PyTorch 2.9.0 is 3.10. We also have 3.14 and 3.14t available as preview with this release. ## Undefined behavior when an output of a custom operator shares storage with an input This is a reminder that outputs of PyTorch custom operators (that are registered using the `torch.library` or `TORCH_LIBRARY` APIs) are not allowed to return Tensors that share storage with input tensors. The violation of this condition leads to undefined behavior: sometimes the result will be correct, sometimes it will be garbage. After [#163227](https://github.com/pytorch/pytorch/pull/163227