Update torch to 1.13.1

[pyup-bot]

This PR updates torch from 1.9.0 to 1.13.1.

Changelog

1.13.1

2. Critical fixes for: silent correctness, backwards compatibility, crashes, deadlocks, (large) memory leaks
3. Fixes to new features being introduced in this release
4. Documentation improvements
5. Release branch specific changes (e.g. blocking ci fixes, change version identifiers)

Building a release schedule / cherry picking

> Main POC: Patch Release Managers

1. After regressions / fixes have been triaged Patch Release Managers will work together and build /announce a schedule for the patch release
 * *NOTE*: Ideally this should be ~2-3 weeks after a regression has been identified to allow other regressions to be identified

1.12

git log --oneline

* Perform tag and push it to github (this will trigger the binary release build)

1.12.0rc2

Pushing a release candidate should trigger the `binary_builds` workflow within CircleCI using [`pytorch/pytorch-probot`](https://github.com/pytorch/pytorch-probot)'s [`trigger-circleci-workflows`](trigger-circleci-workflows) functionality.

This trigger functionality is configured here: [`pytorch-circleci-labels.yml`](https://github.com/pytorch/pytorch/blob/master/.github/pytorch-circleci-labels.yml)

To view the state of the release build, please navigate to [HUD](https://hud.pytorch.org/hud/pytorch/pytorch/release%2F1.12). and make sure all binary builds are successful.
Release Candidate Storage

Release candidates are currently stored in the following places:

* Wheels: https://download.pytorch.org/whl/test/
* Conda: https://anaconda.org/pytorch-test
* Libtorch: https://download.pytorch.org/libtorch/test

Backups are stored in a non-public S3 bucket at [`s3://pytorch-backup`](https://s3.console.aws.amazon.com/s3/buckets/pytorch-backup?region=us-east-1&tab=objects)

Release Candidate health validation

Validate the release jobs for pytorch and domain libraries should be green. Validate this using following HUD links:
* [Pytorch](https://hud.pytorch.org/hud/pytorch/pytorch/release%2F1.12)
* [TorchVision](https://hud.pytorch.org/hud/pytorch/vision/release%2F1.12)
* [TorchAudio](https://hud.pytorch.org/hud/pytorch/audio/release%2F1.12)
* [TorchText](https://hud.pytorch.org/hud/pytorch/text/release%2F1.12)

Validate that the documentation build has completed and generated entry corresponding to the release in  [docs folder](https://github.com/pytorch/pytorch.github.io/tree/site/docs/) of pytorch.github.io repository

Cherry Picking Fixes

Typically within a release cycle fixes are necessary for regressions, test fixes, etc.

For fixes that are to go into a release after the release branch has been cut we typically employ the use of a cherry pick tracker.

An example of this would look like:
* https://github.com/pytorch/pytorch/issues/51886

Please also make sure to add milestone target to the PR/issue, especially if it needs to be considered for inclusion into the dot release.

**NOTE**: The cherry pick process is not an invitation to add new features, it is mainly there to fix regressions


Promoting RCs to Stable

Promotion of RCs to stable is done with this script:
[`pytorch/builder:release/promote.sh`](https://github.com/pytorch/builder/blob/master/release/promote.sh)

Users of that script should take care to update the versions necessary for the specific packages you are attempting to promote.

Promotion should occur in two steps:
* Promote S3 artifacts (wheels, libtorch) and Conda packages
* Promote S3 wheels to PyPI

**NOTE**: The promotion of wheels to PyPI can only be done once so take caution when attempting to promote wheels to PyPI, (see https://github.com/pypa/warehouse/issues/726 for a discussion on potential draft releases within PyPI)

Additional Steps to prepare for release day

The following should be prepared for the release day

Modify release matrix

Need to modify release matrix for get started page. See following [PR](https://github.com/pytorch/pytorch.github.io/pull/959) as reference.

After modifying published_versions.json you will need to regenerate regenerate the quick-start-module.js file run following command

python3 scripts/gen_quick_start_module.py >assets/quick-start-module.js

Please note: This PR needs to be merged on the release day and hence it should be absolutely free of any failures. To test this PR, open another test PR but pointing to to the Release candidate location as above [Release Candidate Storage](RELEASE.mdrelease-candidate-storage)

Open Google Colab issue

This is normally done right after the release is completed. We would need to create Google Colab Issue see following [PR](https://github.com/googlecolab/colabtools/issues/2372)

Patch Releases

A patch release is a maintenance release of PyTorch that includes fixes for regressions found in a previous minor release. Patch releases typically will bump the `patch` version from semver (i.e. `[major].[minor].[patch]`

Patch Release Criteria

Patch releases should be considered if a regression meets the following criteria:

1. Does the regression break core functionality (stable / beta features) including functionality in first party domain libraries?
 * First party domain libraries:
     * [pytorch/vision](https://github.com/pytorch/vision)
     * [pytorch/audio](https://github.com/pytorch/audio)
     * [pytorch/text](https://github.com/pytorch/text)
3. Is there not a viable workaround?
 * Can the regression be solved simply or is it not overcomable?

> *NOTE*: Patch releases should only be considered when functionality is broken, documentation does not typically fall within this category

Patch Release Process

Triage

> Main POC: Triage Reviewers

1. Tag issues / pull requests that are candidates for a potential patch release with `triage review`
 * ![adding triage review label](https://user-images.githubusercontent.com/1700823/132589089-a9210a14-6159-409d-95e5-f79067f6fa38.png)
2. Triage reviewers will then check if the regression / fix identified fits within above mentioned [Patch Release Criteria](patch-release-criteria)
3. Triage reviewers will then add the issue / pull request to the related milestone (i.e. `1.9.1`) if the regressions if found to be within the [Patch Release Criteria](patch-release-criteria)
 * ![adding to milestone](https://user-images.githubusercontent.com/1700823/131175980-148ff38d-44c3-4611-8a1f-cd2fd1f4c49d.png)

Issue Tracker for Patch releases

For patch releases issue tracker needs to be created. For patch release, we require all cherry-pick changes to have links to either a high-priority Github issue or a CI failure from previous RC. An example of this would look like:
* https://github.com/pytorch/pytorch/issues/51886

Only following issues are accepted:

1.12.0rc1

You can use following commands to perform tag from pytorch core repo (not fork):
* Checkout and validate the repo history before tagging

1.9.1

Building Binaries / Promotion to Stable

> Main POC: Patch Release managers

1. Patch Release Managers will follow the process of [Drafting RCs (Release Candidates)](drafting-rcs-release-candidates)
2. Patch Release Managers will follow the process of [Promoting RCs to Stable](promoting-rcs-to-stable)

Hardware / Software Support in Binary Build Matrix

PyTorch has a support matrix across a couple of different axis. This section should be used as a decision making framework to drive hardware / software support decisions

Python

For versions of Python that we support we follow the [NEP 29 policy](https://numpy.org/neps/nep-0029-deprecation_policy.html), which was originally drafted by numpy.

TL;DR

* All minor versions of Python released 42 months prior to the project, and at minimum the two latest minor versions.
* All minor versions of numpy released in the 24 months prior to the project, and at minimum the last three minor versions.

Accelerator Software

For accelerator software like CUDA and ROCm we will typically use the following criteria:
* Support latest 2 minor versions

Special support cases

In some instances support for a particular version of software will continue if a need is found. For example, our CUDA 11 binaries do not currently meet
the size restrictions for publishing on PyPI so the default version that is published to PyPI is CUDA 10.2.

These special support cases will be handled on a case by case basis and support may be continued if current PyTorch maintainers feel as though there may still be a
need to support these particular versions of software.

Special Topics

Updating submodules for a release

In the event a submodule cannot be fast forwarded and a patch must be applied we can take two different approaches:

* (preferred) Fork the said repository under the pytorch GitHub organization, apply the patches we need there, and then switch our submodule to accept our fork.
* Get the dependencies maintainers to support a release branch for us

Editing submodule remotes can be easily done with: (running from the root of the git repository)

git config --file=.gitmodules -e


An example of this process can be found here:

* https://github.com/pytorch/pytorch/pull/48312

Links

• PyPI: https://pypi.org/project/torch
• Changelog: https://pyup.io/changelogs/torch/
• Repo: https://github.com/pytorch/pytorch/tags
• Homepage: https://pytorch.org/