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allenai / allennlp

An open-source NLP research library, built on PyTorch.
http://www.allennlp.org
Apache License 2.0
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Update torch requirement from <1.12.0,>=1.10.0 to >=1.10.0,<1.13.0 #5680

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 2 years ago

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)
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Changelog

Sourced from torch's changelog.

Releasing PyTorch

General Overview

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

  1. Cutting a release branch preparations
  2. Cutting a release branch and making release branch specific changes
  3. Drafting RCs (Release Candidates), and merging cherry picks
  4. 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


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epwalsh commented 2 years ago

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

dependabot[bot] commented 1 year ago

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.