Build(deps): Bump torch from 2.0.1 to 2.1.0

[dependabot]

Bumps torch from 2.0.1 to 2.1.0.

Release notes

Sourced from torch's releases.

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 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.
• torch.export, a sound full-graph capture mechanism is introduced as a prototype feature, as well as torch.export-based quantization.
• torch.sparse now includes prototype support for semi-structured (2:4) sparsity on NVIDIA® GPUs.

... (truncated)

Commits

• 7bcf7da Add tensorboard to pip requirements (#109349) (#109823)
• 1841d54 [CI] Add torch.compile works without numpy test (#109624) (#109818)
• fca4233 Fix the parameter error in test_device_mesh.py (#108758) (#109826)
• 539a971 [Release-2.1]Add finfo properties for float8 dtypes (#109808)
• 9287a0c [Release/2.1][JIT] Fix typed enum handling in 3.11 (#109807)
• c464075 [release only] Docker build - Setup release specific variables (#109809)
• 1b4161c [Release/2.1] [Docs] Fix compiler.list_backends invocation (#109800)
• 2822053 [Release/2.1] [Docs] Fix typo in torch.unflatten (#109801)
• da9639c Remove torchtext from Build Official Docker images (#109799) (#109803)
• e534243 Add docs for torch.compile(numpy) (#109789)
• Additional commits viewable in compare view

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[github-actions]

I'm approving this pull request because it includes a patch or minor update