PyTorch 2.3.1 Release, bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

Torch.compile:

• Remove runtime dependency on JAX/XLA, when importing torch.__dynamo (#124634)
• Hide Plan failed with a cudnnException warning (#125790)
• Fix CUDA memory leak (#124238) (#120756)

Distributed:

• Fix format_utils executable, which was causing it to run as a no-op (#123407)
• Fix regression with device_mesh in 2.3.0 during initialization causing memory spikes (#124780)
• Fix crash of FSDP + DTensor with ShardingStrategy.SHARD_GRAD_OP (#123617)
• Fix failure with distributed checkpointing + FSDP if at least 1 forward/backward pass has not been run. (#121544) (#127069)
• Fix error with distributed checkpointing + FSDP, and with use_orig_params = False and activation checkpointing (#124698) (#126935)
• Fix set_model_state_dict errors on compiled module with non-persistent buffer with distributed checkpointing (#125336) (#125337)

MPS:

• Fix data corruption when coping large (>4GiB) tensors (#124635)
• Fix Tensor.abs() for complex (#125662)

Packaging:

• Fix UTF-8 encoding on Windows .pyi files (#124932)
• Fix import torch failure when wheel is installed for a single user on Windows(#125684)
• Fix compatibility with torchdata 0.7.1 (#122616)
• Fix aarch64 docker publishing to https://ghcr.io (#125617)
• Fix performance regression an aarch64 linux (pytorch/builder#1803)

Other:

• Fix DeepSpeed transformer extension build on ROCm (#121030)
• Fix kernel crash on tensor.dtype.to_complex() after ~100 calls in ipython kernel (#125154)

Release tracker #125425 contains all relevant pull requests related to this release as well as links to related issues.