PyTorch 2.1: automatic dynamic shape compilation, distributed checkpointing

# PyTorch 2.1 Release Notes - Highlights - Backwards Incompatible Change - Deprecations - New Features - Improvements - Bug fixes - Performance - Documentation - Developers - Security # Highlights We are excited to announce the release of PyTorch® 2.1! PyTorch 2.1 offers automatic dynamic shape support in torch.compile, torch.distributed.checkpoint for saving/loading distributed training jobs on multiple ranks in parallel, and torch.compile support for the NumPy API. In addition, this release offers numerous performance improvements (e.g. CPU inductor improvements, AVX512 support, scaled-dot-product-attention support) as well as a prototype release of torch.export, a sound full-graph capture mechanism, and `torch.export`-based quantization. Along with 2.1, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog. This release is composed of 6,682 commits and 784 contributors since 2.0. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.1. More information about how to get started with the PyTorch 2-series can be found at our [Getting Started](https://pytorch.org/get-started/pytorch-2.0/) page. Summary: - `torch.compile` now includes automatic support for detecting and minimizing recompilations due to tensor shape changes using automatic dynamic shapes. - `torch.distributed.checkpoint` enables saving and loading models from multiple ranks in parallel, as well as resharding due to changes in cluster topology. - `torch.compile` can now compile NumPy operations via translating them into PyTorch-equivalent operations. - `torch.compile` now includes improved support for Python 3.11. - New CPU performance features include inductor improvements (e.g. bfloat16 support and dynamic shapes), AVX512 kernel support, and scaled-dot-product-attention kernels.