Bump torch from 2.2.2 to 2.3.0

[dependabot[bot]]

Bumps torch from 2.2.2 to 2.3.0.

Release notes

Sourced from torch's releases.

PyTorch 2.3: User-Defined Triton Kernels in torch.compile, Tensor Parallelism in Distributed

PyTorch 2.3 Release notes

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

Highlights

We are excited to announce the release of PyTorch® 2.3! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance complications or graph breaks. As well, Tensor Parallelism improves the experience for training Large Language Models using native PyTorch functions, which has been validated on training runs for 100B parameter models.

This release is composed of 3393 commits and 426 contributors since PyTorch 2.2. 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 2.3. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

... (truncated)

Commits

• 97ff6cf [Release only] Release 2.3 start using triton package from pypi (#123580)
• fb38ab7 Fix for MPS regression in #122016 and #123178 (#123385)
• 23961ce [Release/2.3] Set py3.x build-environment name consistently (#123446)
• 634cf50 [Wheel] Change libtorch_cpu OpenMP search path (#123417) (#123442)
• 12d0e69 update submodule onnx==1.16.0 (#123387)
• 38acd81 [MPS] Fwd-fix for clamp regression (#122148) (#123383)
• b197f54 Use numpy 2.0.0rc1 in CI (#123356)
• dc81d19 [CI] Test that NumPy-2.X builds are backward compatible with 1.X (#123354)
• 108305e Upgrade submodule pybind to 2.12.0 (#123355)
• a8b0091 Make PyTorch compilable against upcoming Numpy-2.0 (#121880) (#123380)
• Additional commits viewable in compare view

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