PyTorch 发布: PyTorch 2.12.0 Release

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# PyTorch 2.12.0 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 Batched linalg.eigh on CUDA is up to 100x faster due to updated cuSolver backend selection. New torch.accelerator.Graph API unifies graph capture and replay across CUDA, XPU, and out-of-tree backends. torch.export.save now supports Microscaling (MX) quantization formats, enabling full export of aggressively compressed models. Adagrad now supports fused=True, joining Adam, AdamW, and SGD with a single-kernel optimizer implementation. torch.cond control flow can now be captured and replayed inside CUDA Graphs. ROCm users gain expandable memory segments, rocSHMEM symmetric memory collectives, and FlexAttention pipelining. For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release. # Backwards Incompatible Changes ## Build Frontend - Strengthened SVE compile checks in `FindARM.cmake`, which may reject previously accepted but incorrect SVE configurations ([#176646](https://github.com/pytorch/pytorch/pull/176646)) Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build. - Updated the minimum CUDA version required to build PyTorch from s