Bump torch from 1.10.0+cpu to 1.11.0

[dependabot[bot]]

Bumps torch from 1.10.0+cpu to 1.11.0.

Release notes

Sourced from torch's releases.

PyTorch 1.11, TorchData, and functorch are now available

PyTorch 1.11 Release Notes

• Highlights
• Backwards Incompatible Change
• New Features
• Improvements
• Performance
• Documentation

Highlights

We are excited to announce the release of PyTorch 1.11. This release is composed of over 3,300 commits since 1.10, made by 434 contributors. Along with 1.11, we are releasing beta versions of TorchData and functorch. We want to sincerely thank our community for continuously improving PyTorch.

• TorchData is a new library for common modular data loading primitives for easily constructing flexible and performant data pipelines. View it on GitHub.
• functorch, a library that adds composable function transforms to PyTorch, is now available in beta. View it on GitHub.
• Distributed Data Parallel (DDP) static graph optimizations available in stable.

You can check the blogpost that shows the new features here.

Backwards Incompatible changes

Python API

Fixed python deepcopy to correctly copy all attributes on Tensor objects (#65584)

This change ensures that the deepcopy operation on Tensor properly copies all the attributes (and not just the plain Tensor properties).

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

• General Overview
• Cutting a release branch preparations
• Cutting release branches
  • pytorch/pytorch
  • pytorch/builder / PyTorch domain libraries
  • Making release branch specific changes for PyTorch
  • Making release branch specific changes for domain libraries
• Drafting RCs (https://github.com/pytorch/pytorch/blob/master/Release Candidates) for PyTorch and domain libraries
  • Release Candidate Storage
  • Cherry Picking Fixes
• Promoting RCs to Stable
• Additonal Steps to prepare for release day
  • Modify release matrix
  • Open Google Colab issue
• Patch Releases
  • Patch Release Criteria
  • Patch Release Process
    • Triage
    • Building a release schedule / cherry picking
    • Building Binaries / Promotion to Stable
• Hardware / Software Support in Binary Build Matrix
  • Python
    • TL;DR
  • Accelerator Software
    • Special support cases
• Special Topics
  • Updating submodules for a release

General Overview

Releasing a new version of PyTorch generally entails 3 major steps:

0. Cutting a release branch preparations
1. Cutting a release branch and making release branch specific changes
2. Drafting RCs (Release Candidates), and merging cherry picks
3. Promoting RCs to stable and performing release day tasks

Cutting a release branch preparations

Following Requirements needs to be met prior to final RC Cut:

• Resolve all outstanding issues in the milestones(for example 1.11.0)before first RC cut is completed. After RC cut is completed following script should be executed from builder repo in order to validate the presence of the fixes in the release branch : python github_analyze.py --repo-path ~/local/pytorch --remote upstream --branch release/1.11 --milestone-id 26 --missing-in-branch
• Validate that all new workflows have been created in the PyTorch and domain libraries included in the release. Validate it against all dimensions of release matrix, including operating systems(Linux, MacOS, Windows), Python versions as well as CPU architectures(x86 and arm) and accelerator versions(CUDA, ROCm).

... (truncated)

Commits

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[dependabot[bot]]

The following labels could not be found: dependencies.

[pcaversaccio]

@dependabot rebase

[dependabot[bot]]

Looks like this PR has been edited by someone other than Dependabot. That means Dependabot can't rebase it - sorry!

If you're happy for Dependabot to recreate it from scratch, overwriting any edits, you can request @dependabot recreate.

[pcaversaccio]

@dependabot recreate