Bump torch from 1.13.1 to 2.0.0

[dependabot]

Bumps torch from 1.13.1 to 2.0.0.

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)

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 #2075 (f48c251) into main (ecf1534) will decrease coverage by 60.95%.
The diff coverage is n/a.

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@@             Coverage Diff             @@
##             main    #2075       +/-   ##
===========================================
- Coverage   85.63%   24.68%   -60.95%     
===========================================
  Files         297      297               
  Lines       26515    26514        -1     
  Branches     4867     4810       -57     
===========================================
- Hits        22705     6545    -16160     
- Misses       2572    19853    +17281     
+ Partials     1238      116     -1122     

see 232 files with indirect coverage changes

[dependabot]

Superseded by #2144.