Bump the pip group across 1 directory with 2 updates

[dependabot]

Bumps the pip group with 2 updates in the / directory: pytorch-lightning and torch.

Updates pytorch-lightning from 1.6.4 to 2.4.0

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI ⚡ is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

• Made saving non-distributed checkpoints fully atomic (#20011)
• Added dump_stats flag to AdvancedProfiler (#19703)
• Added a flag verbose to the seed_everything() function (#20108)
• Added support for PyTorch 2.4 (#20010)
• Added support for Python 3.12 (20078)
• The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
• Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)

• Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
• Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
• NumPy is no longer a required dependency (#20090)

• Removed support for PyTorch 2.1 (#20009)
• Removed support for Python 3.8 (#20071)

• Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
• Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
• Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
• Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
• Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

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Commits

• 2129fdf fix(ci): resolve input str -> num conversion (#20169)
• cf24a19 fix(docs): remove dead link from readme (#20170)
• a3e60ad ci/docs: disable optional cache pkg (#20168)
• 87ffd8c ci: fix cleaning caches (#20167)
• b3ee85d Prepare Lightning 2.4.0 release (#20154)
• 631911c Add special logic for 'step' in _optimizer_to_device (#20019)
• 345450b Fix parameter count in ModelSummary when parameters are DTensors (#20163)
• 3de60f4 docs: fix typo in linkcheck_ignore (#20164)
• e9d4ef8 Add diffusion example to README (#20161)
• d4de8e2 Count number of modules in train/eval mode in ModelSummary (#20159)
• Additional commits viewable in compare view

Updates torch from 1.12.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

• Highlights
• Tracked Regressions
• Backward incompatible changes
• Deprecations
• New features
• Improvements
• Bug Fixes
• Performance
• Documentation
• Developers
• Security

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

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Commits

• d990dad [CMAKE] Look for Development.Module instead of Development (#129729)
• e4ee3be [Release only] use triton 3.0.x from pypi (#130336)
• 9afe4ec Update torchbench model expected accuracy values after pinning numpy (#129986)
• 499621e [CherryPick][FSDP2+TP] Disable 2D state_dict (#129519) (#129923)
• e5bda62 [CherryPick][DCP] Fix Optimizer Learning Rate not being loaded correctly (#12...
• 705e3ae Improve error message for weights_only load (#129783)
• b26cde4 [Windows] remove mkl shared library dependency. (#129740)
• 12ad767 [distributed] NCCL result code update (#129704)
• 1164d3c Add threadfence to 2-stage reduction for correct writes visibility (#129701)
• 9533637 Inductor to fail gracefully on Voltas for bf16 tensors (#129699)
• Additional commits viewable in compare view

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