PyTorch 发布: PyTorch 2.13.0 Release

来源摘要

# PyTorch 2.13.0 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) # Highlights FlexAttention lands on Apple Silicon (MPS), with up to ~12x speedup over SDPA on sparse patterns, and gains a deterministic backward path on CUDA for reproducible gradient computation. CuTeDSL "Native DSL" backend gives Inductor a second high-performance code path (alongside Triton) for key GPU operations, with faster compilation. [Prototype] nn.LinearCrossEntropyLoss combines the final prediction and loss computation to cut peak GPU memory by up to 4x for large-vocabulary language model training. torchcomms, a new communications backend for PyTorch Distributed, improves fault tolerance, scalability, and debuggability for large-cluster training. FSDP2 now overlaps reduce-scatter and all-gather communications via a dedicated process group (opt-in), increasing distributed training throughput. Python 3.15 wheel support for PyTorch on Linux via the pytorch repository index, including builds compatible with free-threaded 3.15t. Broader platform support: ROCm gains AOTriton 0.12b with native HIP CMake, Arm adds Armv9-A torch.compile targeting, and Intel XPU exposes new device telemetry APIs. For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release. # Tracked Regressions ### ROCm wheels break `torch.compile` on CPU in environments without a GPU Running a `torch==2.13.0+rocm7.2` wheel in an environment where no GPU is available (`torch.cuda.is_availa