PyTorch 2.13.0 Release

# 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