Update torch requirement from <1.14.0,>=1.7.0 to >=1.7.0,<3.0.0

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

PyTorch 2.0: Our next generation release that is faster, more Pythonic and Dynamic as ever

PyTorch 2.0 Release notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation

Highlights

We are excited to announce the release of PyTorch® 2.0 (release note) which we highlighted during the PyTorch Conference on 12/2/22! PyTorch 2.0 offers the same eager-mode development and user experience, while fundamentally changing and supercharging how PyTorch operates at compiler level under the hood with faster performance and support for Dynamic Shapes and Distributed.

This next-generation release includes a Stable version of Accelerated Transformers (formerly called Better Transformers); Beta includes torch.compile as the main API for PyTorch 2.0, the scaled_dot_product_attention function as part of torch.nn.functional, the MPS backend, functorch APIs in the torch.func module; and other Beta/Prototype improvements across various inferences, performance and training optimization features on GPUs and CPUs. For a comprehensive introduction and technical overview of torch.compile, please visit the 2.0 Get Started page.

Along with 2.0, we are also releasing a series of beta updates to the PyTorch domain libraries, including those that are in-tree, and separate libraries including TorchAudio, TorchVision, and TorchText. An update for TorchX is also being released as it moves to community supported mode. More details can be found in this library blog.

This release is composed of over 4,541 commits and 428 contributors since 1.13.1. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.0 and the overall 2-series this year.

Summary:

• torch.compile is the main API for PyTorch 2.0, which wraps your model and returns a compiled model. It is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition.
• As an underpinning technology of torch.compile, TorchInductor with Nvidia and AMD GPUs will rely on OpenAI Triton deep learning compiler to generate performant code and hide low level hardware details. OpenAI Triton-generated kernels achieve performance that's on par with hand-written kernels and specialized cuda libraries such as cublas.
• Accelerated Transformers introduce high-performance support for training and inference using a custom kernel architecture for scaled dot product attention (SPDA). The API is integrated with torch.compile() and model developers may also use the scaled dot product attention kernels directly by calling the new scaled_dot_product_attention() operator.
• Metal Performance Shaders (MPS) backend provides GPU accelerated PyTorch training on Mac platforms with added support for Top 60 most used ops, bringing coverage to over 300 operators.
• Amazon AWS optimize the PyTorch CPU inference on AWS Graviton3 based C7g instances. PyTorch 2.0 improves inference performance on Graviton compared to the previous releases, including improvements for Resnet50 and Bert.
• New prototype features and technologies across TensorParallel, DTensor, 2D parallel, TorchDynamo, AOTAutograd, PrimTorch and TorchInductor.

... (truncated)

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

• c263bd4 [inductor] use triu ref instead of lowering (#96040) (#96462)
• c9913cf Add jinja2 as mandatory dependency (#95691) (#96450)
• 2f7d8bb Fix expired deprecation of comparison dtype for NumPy 1.24+ (#91517) (#96452)
• ca0cdf5 dl_open_guard should restore flag even after exception (#96231) (#96457)
• 9cfa076 [Release/2.0] Use Triton from PYPI (#96010)
• 8e05e41 [Release/2.0] Use builder release branch for tests
• d8ffc60 Remove mention of dynamo.optimize() in docs (#95802) (#96007)
• 1483723 [MPS] Disallow reshape in slice (#95905) (#95978)
• c4572aa [MPS] Add fixes for div with floor (#95869)
• 82b078b [MPS] Fix views with 3 or more sliced dimensions (#95762) (#95871)
• Additional commits viewable in compare view

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[codecov-commenter]

Codecov Report

Merging #758 (e267fed) into master (3992180) will decrease coverage by 0.34%.
The diff coverage is n/a.

❗ Current head e267fed differs from pull request most recent head 5a8e2ed. Consider uploading reports for the commit 5a8e2ed to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #758      +/-   ##
==========================================
- Coverage   81.03%   80.69%   -0.34%     
==========================================
  Files         146      144       -2     
  Lines        9721     9577     -144     
==========================================
- Hits         7877     7728     -149     
- Misses       1844     1849       +5     

Flag	Coverage Δ
ubuntu-latest-3.7	80.69% <ø> (?)

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see 26 files with indirect coverage changes

[jklaise]

@mauicv as you're pushing to this branch, could you change the upper version bound to <3.0 and change the title of the PR too please?

[dependabot]

A newer version of torch exists, but since this PR has been edited by someone other than Dependabot I haven't updated it. You'll get a PR for the updated version as normal once this PR is merged.

[jklaise]

Due to pytorch/pytorch#97580 to unblock our CI running with torch 2.0 and tensorflow 2.12 we should see if we can force the import order to be torch first then tensorflow. Nothing that alibi doesn't have this issue.

[julien-blanchon]

Yes please bump the pytorch to 2.0

[ascillitoe]

Additional note, from the torch docs:

Currently, PyTorch on Windows only supports Python 3.8-3.11; Python 2.x is not supported.

torch 2.0 doesn't support Windows, so we'll have to reflect that in the Windows tests... Correction, the Windows tests will simply run an older torch version...

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