Update torch requirement from <1.12.0,>=1.10.0 to >=1.10.0,<1.13.0

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

PyTorch 1.12: TorchArrow, Functional API for Modules and nvFuser, are now available

PyTorch 1.12 Release Notes

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

Highlights

We are excited to announce the release of PyTorch 1.12! This release is composed of over 3124 commits, 433 contributors. Along with 1.12, we are releasing beta versions of AWS S3 Integration, PyTorch Vision Models on Channels Last on CPU, Empowering PyTorch on Intel® Xeon® Scalable processors with Bfloat16 and FSDP API. We want to sincerely thank our dedicated community for your contributions.

Summary:

• Functional Module API to functionally apply module computation with a given set of parameters
• Complex32 and Complex Convolutions in PyTorch
• DataPipes from TorchData fully backward compatible with DataLoader
• Functorch with improved coverage for APIs
• nvFuser a deep learning compiler for PyTorch
• Changes to float32 matrix multiplication precision on Ampere and later CUDA hardware
• TorchArrow, a new beta library for machine learning preprocessing over batch data

Backwards Incompatible changes

Python API

Updated type promotion for torch.clamp (#77035)

In 1.11, the ‘min’ and ‘max’ arguments in torch.clamp did not participate in type promotion, which made it inconsistent with minimum and maximum operations. In 1.12, the ‘min’ and ‘max’ arguments participate in type promotion.

1.11

>>> import torch
>>> a = torch.tensor([1., 2., 3., 4.], dtype=torch.float32)
>>> b = torch.tensor([2., 2., 2., 2.], dtype=torch.float64)
>>> c = torch.tensor([3., 3., 3., 3.], dtype=torch.float64)
>>> torch.clamp(a, b, c).dtype
torch.float32

1.12

>>> import torch
>>> a = torch.tensor([1., 2., 3., 4.], dtype=torch.float32)
>>> b = torch.tensor([2., 2., 2., 2.], dtype=torch.float64)
>>> c = torch.tensor([3., 3., 3., 3.], dtype=torch.float64)
</tr></table> 

... (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
  • Release Candidate health validation
  • 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

... (truncated)

Commits

• 67ece03 Disable AVX512 CPU dispatch by default (#80253) (#80356)
• bcfb424 [JIT] Imbue stringbuf with C locale (#79929) (#79983)
• 8186aa7 [DataLoader] Share seed via Distributed Store to get rid of CUDA dependency (...
• 01d9324 nn: Disable nested tensor by default (#79884)
• 5009086 Fix release doc builds (#79865)
• bfb6b24 [JIT] Nested fix (#79480) (#79816)
• 681a6e3 [v1.12.0] Fix non-reentrant hooks based checkpointing (#79490)
• 92437c6 Revert behavior of Dropout2d on 3D inputs to 1D channel-wise dropout behavior...
• 566286f Add Dropout1d module (#79610)
• ac30861 [DataLoader] Fix the world_size when distributed sharding MapDataPipe (#79524...
• Additional commits viewable in compare view

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[epwalsh]

There are some spooky errors happening in the GPU tests. This will take some more debugging. Is it even worth it?

[dependabot]

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.