Bump pytorch-lightning from 2.0.0 to 2.1.0

[dependabot]

Bumps pytorch-lightning from 2.0.0 to 2.1.0.

Release notes

Sourced from pytorch-lightning's releases.

Lightning 2.1: Train Bigger, Better, Faster

Lightning AI is excited to announce the release of Lightning 2.1 ⚡ It's the culmination of work from 79 contributors who have worked on features, bug-fixes, and documentation for a total of over 750+ commits since v2.0.

The theme of 2.1 is "bigger, better, faster": Bigger because training large multi-billion parameter models has gotten even more efficient thanks to FSDP, efficient initialization and sharded checkpointing improvements, better because it's easier than ever to scale models without making substantial code changes or installing third-party packages and faster because it leverages the latest hardware features to speed up training in low-bit precision thanks to new precision plugins like bitsandbytes and transformer engine. And of course, as the name implies, this release fully leverages the latest features in PyTorch 2.1 🎉

• Highlights
  • Improvements To Large-Scale Training With FSDP
  • True Half-Precision
  • Bitsandbytes Quantization
  • Transformer Engine
  • Lightning on TPU Goes Brrr
  • Granular Control Over Checkpoints in Fabric
• Backward Incompatible Changes
  • PyTorch Lightning
  • Lightning Fabric
• Full Changelog
  • PyTorch Lightning
  • Lightning Fabric
  • Lightning App
• Contributors

Highlights

Improvements To Large-Scale Training With FSDP

The FSDP strategy for training large billion-parameter models gets substantial improvements and new features in Lightning 2.1, both in Trainer and Fabric (in case you didn't know, Fabric is the latest addition to the Lightning family of tools to scale models without the boilerplate code). FSDP is now more user-friendly to configure, has memory management and speed improvements, and we have a brand new end-to-end user guide with best practices (Trainer, Fabric).

Efficient Saving and Loading of Large Checkpoints

When training large billion-parameter models with FSDP, saving and resuming training, or even just loading model parameters for finetuning can be challenging, as users are are often plagued by out-of-memory errors and speed bottlenecks.

In 2.1, we made several improvements. Starting with saving checkpoints, we added support for distributed/sharded checkpoints, enabled through the setting state_dict_type in the strategy (#18364, #18358):

Trainer:

import lightning as L
from lightning.pytorch.strategies import FSDPStrategy
Default used by the strategy
strategy = FSDPStrategy(state_dict_type="full")
Enable saving distributed checkpoints
</tr></table>

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Commits

• 6f6c07d Revert removal of empty-parameters check for configure_optimizers() with FS...
• 20ce3ae docs: setting cron for periodical update tutorials (#18783)
• 9f17324 Update probot-check-group.yml to v5.4 (#18782)
• c5e3c45 Save ModelCheckpoint's last.ckpt as symlink if possible (#18748)
• 7434c47 Raise an exception when calling fit twice with spawn (#18776)
• 5a83f54 Minor strategy fixes [TPU] (#18774)
• 4df6e13 Update version and changelog (#18767)
• 83abe5e Bugfix: Pin lightning-cloud version (#18778)
• 27ad9e9 xfail collective tests (#18779)
• c39f680 Fix deletion of resumed checkpoints (#18750)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #4.