PyTorch 2.14.0 Release

# PyTorch 2.14.0 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) - [Security](#security) - [Developers](#developers) # Highlights NVGEMM brings CuTeDSL-generated CUTLASS kernels to Inductor, with epilogue fusion, scaled and NVFP4 GEMM, and grouped-reduction epilogues autotuned alongside Triton and ATen torch.switch generalizes torch.cond to multi-way branching, and torch.while_loop can now be captured in a CUDA graph Declarative dynamic shapes via @dynamic_spec, shared across torch.compile, torch.export and make_fxExperimental torch.compile support for complex-valued tensors: Opt-in support decomposes supported complex operations into real and imaginary computations, enabling compiler backends to optimize more complex-number workloads. A preview of our rewritten NCCL backend for PyTorch, ported from torchcomms, implementing the full collective contract with nonblocking communicators and eager communicator splitting and advanced features such as fault tolerance and windows designed as a drop-in replacement of existing NCCL c10d backend Fault tolerance becomes a first-class c10d concept, with in-place process-group reconfiguration, one-sided RMA windows, and a Flight Recorder that works for any backend rather than only NCCL Apple Silicon gains native linear algebra, including Jacobi-kernel SVD, eigh, QR and Cholesky, alongside a five-part reduction rewrite and a further MPSGraph to Metal kernel migration Broader platform support: ROCm 7.14 wheels are produced from the TheRock pip SDK, Intel XPU adds native graph capture, and Inductor targets Rubin (sm_107