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Update torch requirement from <2.1,>=1.13.0 to >=1.13.0,<2.2 #279

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@dependabot dependabot bot commented on behalf of github Oct 5, 2023

Updates the requirements on torch to permit the latest version.

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)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

PyTorch version Python Stable CUDA Experimental CUDA
2.1 >=3.8, <=3.11 CUDA 11.8, CUDNN 8.7.0.84 CUDA 12.1, CUDNN 8.9.2.26
2.0 >=3.8, <=3.11 CUDA 11.7, CUDNN 8.5.0.96 CUDA 11.8, CUDNN 8.7.0.84
1.13 >=3.7, <=3.10 CUDA 11.6, CUDNN 8.3.2.44 CUDA 11.7, CUDNN 8.5.0.96

... (truncated)

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📚 Documentation preview 📚: https://frouros--279.org.readthedocs.build/en/279/

Note
Automatic rebases have been disabled on this pull request as it has been open for over 30 days.

Updates the requirements on [torch](https://github.com/pytorch/pytorch) to permit the latest version.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.13.0...v2.1.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@jaime-cespedes-sisniega jaime-cespedes-sisniega merged commit 20d8da5 into main Dec 28, 2023
6 checks passed
@dependabot dependabot bot deleted the dependabot/pip/torch-gte-1.13.0-and-lt-2.2 branch December 28, 2023 12:05
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