PyTorch 2.3: User-Defined Triton Kernels in torch.compile, Tensor Parallelism in Distributed

# PyTorch 2.3 Release notes * Highlights * Backwards Incompatible Changes * Deprecations * New Features * Improvements * Bug fixes * Performance * Documentation # Highlights We are excited to announce the release of PyTorch® 2.3! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance complications or graph breaks. As well, Tensor Parallelism improves the experience for training Large Language Models using native PyTorch functions, which has been validated on training runs...