Bump lightning[pytorch-extra] from 2.2.5 to 2.3.0 in /requirements

[dependabot]

Bumps lightning[pytorch-extra] from 2.2.5 to 2.3.0.

Release notes

Sourced from lightning[pytorch-extra]'s releases.

Lightning v2.3: Tensor Parallelism and 2D Parallelism

Lightning AI is excited to announce the release of Lightning 2.3 ⚡

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.

This release introduces experimental support for Tensor Parallelism and 2D Parallelism, PyTorch 2.3 support, and several bugfixes and stability improvements.

• Highlights
  • Tensor Parallelism (beta)
  • 2D Parallelism (beta)
  • Training Mode in Model Summary
  • Special Forward Methods in Fabric
• Notable Changes
• Full Changelog
  • PyTorch Lightning
  • Lightning Fabric
• Contributors

Highlights

Tensor Parallelism (beta)

Tensor parallelism (TP) is a technique that splits up the computation of selected layers across GPUs to save memory and speed up distributed models. To enable TP as well as other forms of parallelism, we introduce a ModelParallelStrategy for both Lightning Trainer and Fabric. Under the hood, TP is enabled through new experimental PyTorch APIs like DTensor and torch.distributed.tensor.parallel.

PyTorch Lightning

Enabling TP in a model with PyTorch Lightning requires you to implement the LightningModule.configure_model() method where you convert selected layers of a model to paralellized layers. This is an advanced feature, because it requires a deep understanding of the model architecture. Open the tutorial Studio to learn the basics of Tensor Parallelism.

 

import lightning as L
from lightning.pytorch.strategies import ModelParallelStrategy
from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
from torch.distributed.tensor.parallel import parallelize_module
1. Implement the configure_model() method in LightningModule
class LitModel(L.LightningModule):
def init(self):
super().init()
self.model = FeedForward(8192, 8192)
</tr></table>

... (truncated)

Commits

• a42484c Fix failing app tests (#19971)
• f6fd046 Release 2.3.0 (#19954)
• a97814a Update README.md
• fa5da26 Update README.md (#19968)
• 06ea3a0 Fix resetting epoch loop restarting flag in LearningRateFinder (#19819)
• 5fa32d9 Ignore parameters causing ValueError when dumping to YAML (#19804)
• 4f96c83 Sanitize argument-free object params before logging (#19771)
• a611de0 Removing numpy requirement from all files in examples/pytorch/domain_template...
• 812ffde Fix save_last type annotation for ModelCheckpoint (#19808)
• 7668a6b Flexible and easy to use HSDP setting (#19504)
• Additional commits viewable in compare view

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[adamjstewart]

Broken due to Lightning-AI/pytorch-lightning#19977

[robmarkcole]

Why not 2.3.1? https://github.com/Lightning-AI/pytorch-lightning/releases/tag/2.3.1

[adamjstewart]

2.3.0 and 2.3.1 are both broken due to the same bug. I don't know why dependabot opened two separate PRs for 2.3.0 and 2.3.1, but we can't upgrade to either just yet.

[adamjstewart]

dependabot/dependabot-core#10126

[CarlosGomes98]

Should the pyproject.toml set the lightning dependency to <2.3.0? At the moment, if I understand correctly, lightning==2.3.1 will be installed, which breaks the trainers

[adamjstewart]

@CarlosGomes98 let me dig a bit deeper to understand what changed and whether or not it's only the format of the config files or also command-line usage. Also want to determine if this was intentional or accidental. Once I know the answers to those questions, we can either fix the bug in lightning or change TorchGeo to support newer lightning. I don't want to get stuck on older versions of lightning for a silly reason like this. Our release cycle is slow enough that by the time a version pin made it into a released version, the bug will likely be fixed already anyway (for better or for worse, I would like to speed up our release cycle someday).

[adamjstewart]

@CarlosGomes98 I think you're right, let's add an upper bound. Hopefully this will get fixed before our next release, but just in case let's prevent broken installs.

[adamjstewart]

Closing until Lightning-AI/pytorch-lightning#19977 is resolved.

[dependabot]

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