Bump torch from 1.12.0 to 1.13.1

[dependabot[bot]]

Bumps torch from 1.12.0 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

PyTorch 1.13: beta versions of functorch and improved support for Apple’s new M1 chips are now available

Pytorch 1.13 Release Notes

• Highlights
• Backwards Incompatible Changes
• New Features
• Improvements
• Performance
• Documentation
• Developers

Highlights

We are excited to announce the release of PyTorch 1.13! This includes stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.

Summary:

• The BetterTransformer feature set supports fastpath execution for common Transformer models during Inference out-of-the-box, without the need to modify the model. Additional improvements include accelerated add+matmul linear algebra kernels for sizes commonly used in Transformer models and Nested Tensors is now enabled by default.

• Timely deprecating older CUDA versions allows us to proceed with introducing the latest CUDA version as they are introduced by Nvidia®, and hence allows support for C++17 in PyTorch and new NVIDIA Open GPU Kernel Modules.

• Previously, functorch was released out-of-tree in a separate package. After installing PyTorch, a user will be able to import functorch and use functorch without needing to install another package.

• PyTorch is offering native builds for Apple® silicon machines that use Apple's new M1 chip as a beta feature, providing improved support across PyTorch's APIs.

Stable	Beta	Prototype
Better TransformerCUDA 10.2 and 11.3 CI/CD Deprecation	Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIsExtend NNC to support channels last and bf16Functorch now in PyTorch Core LibraryBeta Support for M1 devices	Arm® Compute Library backend support for AWS Graviton CUDA Sanitizer

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

Backwards Incompatible changes

... (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
• 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

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 :

... (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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