Bump torch from 1.13.0 to 1.13.1

[dependabot[bot]]

Bumps torch from 1.13.0 to 1.13.1.

Release notes

Sourced from torch's releases.

PyTorch 1.13.1 Release, small bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

• RuntimeError by torch.nn.modules.activation.MultiheadAttention with bias=False and batch_first=True #88669
• Installation via pip on Amazon Linux 2, regression #88869
• Installation using poetry on Mac M1, failure #88049
• Missing masked tensor documentation #89734
• torch.jit.annotations.parse_type_line is not safe (command injection) #88868
• Use the Python frame safely in _pythonCallstack #88993
• Double-backward with full_backward_hook causes RuntimeError #88312
• Fix logical error in get_default_qat_qconfig #88876
• Fix cuda/cpu check on NoneType and unit test #88854 and #88970
• Onnx ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504
• Onnx operator_export_type on the new registry #87735
• torchrun AttributeError caused by file_based_local_timer on Windows #85427

The release tracker should contain all relevant pull requests related to this release as well as links to related issues

Changelog

Sourced from torch's changelog.

Releasing PyTorch

• Release Compatibility Matrix
• 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
• Additional Steps to prepare for release day
  • Modify release matrix
  • Open Google Colab issue
• Patch Releases
  • Patch Release Criteria
  • Patch Release Process
    • Triage
    • Issue Tracker for Patch releases
    • 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

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

PyTorch version	Python	Stable CUDA	Experimental CUDA
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
1.12	>=3.7, <=3.10	CUDA 11.3, CUDNN 8.3.2.44	CUDA 11.6, CUDNN 8.3.2.44

General Overview

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

... (truncated)

Commits

• 49444c3 [BE] Do not package caffe2 in wheel (#87986) (#90433)
• 56de8a3 Add manual cuda deps search logic (#90411) (#90426)
• a4d16e0 Fix ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops (#88504) (#90104)
• 80abad3 Handle Tensor.deepcopy via clone(), on IPU (#89129) (#89999)
• 73a852a [Release only change] Fix rocm5.1.1 docker image (#90321)
• 029ec16 Add platform markers for linux only extra_install_requires (#88826) (#89924)
• 197c5c0 Fix cuda/cpu check on NoneType (#88854) (#90068)
• aadbeb7 Make TorchElastic timer importable on Windows (#88522) (#90045)
• aa94433 Mark IPU device as not supports_as_strided (#89130) (#89998)
• 59b4f3b Use the Python frame safely in _pythonCallstack (#89997)
• Additional commits viewable in compare view

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