Bump torch from 1.8.1 to 1.12.1

[dependabot[bot]]

Bumps torch from 1.8.1 to 1.12.1.

Release notes

Sourced from torch's releases.

PyTorch 1.12.1 Release, small bug fix release

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

Optim

• Remove overly restrictive assert in adam pytorch/pytorch#80222

Autograd

• Convolution forward over reverse internal asserts in specific case pytorch/pytorch#81111
• 25% Performance regression from v0.1.1 to 0.2.0 when calculating hessian pytorch/pytorch#82504

Distributed

• Fix distributed store to use add for the counter of DL shared seed pytorch/pytorch#80348
• Raise proper timeout when sharing the distributed shared seed pytorch/pytorch#81666

NN

• Allow register float16 weight_norm on cpu and speed up test pytorch/pytorch#80600
• Fix weight norm backward bug on CPU when OMP_NUM_THREADS <= 2 pytorch/pytorch#80930
• Weight_norm is not working with float16 pytorch/pytorch#80599
• New release breaks torch.nn.weight_norm backwards pass and breaks all Wav2Vec2 implementations pytorch/pytorch#80569
• Disable src mask for transformer and multiheadattention fastpath pytorch/pytorch#81277
• Make nn.stateless correctly reset parameters if the forward pass fails pytorch/pytorch#81262
• torchvision.transforms.functional.rgb_to_grayscale() + torch.nn.Conv2d() don`t work on 1080 GPU pytorch/pytorch#81106
• Transformer and CPU path with src_mask raises error with torch 1.12 pytorch/pytorch#81129

Data Loader

• [Locking lower ranks seed recepients https://github-redirect.dependabot.com/pytorch/pytorch/pull/81071

CUDA

• os.environ["CUDA_VISIBLE_DEVICES"] has no effect pytorch/pytorch#80876
• share_memory() on CUDA tensors no longer no-ops and instead crashes pytorch/pytorch#80733
• [Prims] Unbreak CUDA lazy init pytorch/pytorch#80899
• PyTorch 1.12 cu113 wheels cudnn discoverability issue pytorch/pytorch#80637
• Remove overly restrictive checks for cudagraph pytorch/pytorch#80881

ONNX

• ONNX cherry picks pytorch/pytorch#82435

MPS

• MPS cherry picks pytorch/pytorch#80898

Other

• Don't error if _warned_capturable_if_run_uncaptured not set pytorch/pytorch#80345
• Initializing libiomp5.dylib, but found libomp.dylib already initialized. pytorch/pytorch#78490
• Assertion error - _dl_shared_seed_recv_cnt - pt 1.12 - multi node pytorch/pytorch#80845
• Add 3.10 stdlib to torch.package pytorch/pytorch#81261
• CPU-only c++ extension libraries (functorch, torchtext) built against PyTorch wheels are not fully compatible with PyTorch wheels pytorch/pytorch#80489

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

PyTorch 1.12 Release Notes

... (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

• 664058f Pin windows numpy (#82652) (#82686)
• efc2d08 Revert #75195 (#82504) (#82662)
• 9a9dceb ONNX cherry picks for 1.12.1 (#82435)
• 617c4fe Fix invalid read in masked softmax (#82272) (#82272) (#82405)
• f469bc1 [ci] Release only change: bump macos worker instance type (#82113)
• 66f6e79 Fix deserialization of TransformerEncoderLayer (#81832) (#81832) (#82094)
• 35eb488 [CI] Disable ios-12-5-1-x86-64 (#81612) (#81612) (#82096)
• e65e4ac 1.12.1/bt fix (#81952)
• e8534b9 MPS cherry picks for 1.12.1 (#81976)
• 03b82bd Disable XLA builds (#80099) (#80099) (#81977)
• Additional commits viewable in compare view

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[dependabot[bot]]

Looks like torch is up-to-date now, so this is no longer needed.