fix(deps): update dependency lightning to v2.3.1 #1205
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This PR contains the following updates:
2.2.5
->2.3.1
Release Notes
Lightning-AI/lightning (lightning)
v2.3.1
: Patch release v2.3.1Compare Source
Includes minor bugfixes and stability improvements.
Full Changelog: Lightning-AI/pytorch-lightning@2.3.0...2.3.1
v2.3.0
: Lightning v2.3: Tensor Parallelism and 2D ParallelismCompare Source
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)
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 andtorch.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.Full training example (requires at least 2 GPUs).
Lightning Fabric
Applying TP in a model with Fabric requires you to implement a special function 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.
Full training example (requires at least 2 GPUs).
2D Parallelism (beta)
Tensor Parallelism by itself can be very effective for efficient inference of very large models. For training, TP is typically combined with other forms of parallelism, such as FSDP, to increase throughput and scalability on large clusters with 100s of GPUs. The new
ModelParallelStrategy
in this release supports the combination of TP + FSDP, which is referred to as 2D parallelism.For an introduction to this feature, please also refer to the tutorial Studios (PyTorch Lightning, Lightning Fabric). At the moment, the PyTorch team is reimplementing FSDP under the name FSDP2 with the aim to make it compose well with other parallelisms such as TP. Therefore, for the experimental 2D parallelism support, you'll need to switch to using FSDP2 with the new
ModelParallelStrategy
. Please refer to our docs (PyTorch Lightning, Lightning Fabric) and stay tuned for future releases as these APIs mature.Training Mode in Model Summary
The model summary table that gets displayed when you run
Trainer.fit()
now contains a new column "Mode" that shows the training mode each layer is in (#19468).A module in PyTorch is always either in
train
(default) oreval
mode.This improvement should give users more visibility into the state of their model and help debug issues, for example when you need to make sure certain layers of the model are frozen.
Special Forward Methods in Fabric
Until now, Lightning Fabric warned the user in case the forward pass of the model or a subset of its modules was conducted through methods other than the dedicated
forward
method of the PyTorch module. The reason for this is that PyTorch needs to run special hooks in case of DDP/FSDP and other strategies to function properly, and not running through the realforward
method would skip these hooks and lead to correctness issues.In Lightning Fabric 2.3, we added a feature to explicitly mark alternative forward methods so that Fabric can add the necessary rerouting behind the scenes:
Find the full example and more details in our docs.
Notable Changes
The 2.0 series of Lightning releases guarantees core API stability: No name changes, argument renaming, hook removals etc. on core interfaces (Trainer, LightningModule, etc.) unless a feature is specifically marked experimental. Here we list a few behavioral changes made in places where the change was justified if it significantly improves the user experience, improves performance, or fixes the correctness of a feature. These changes will likely not impact most users.
Skipping the training step in DDP
It is no longer allowed to skip
training_step()
by returningNone
in distributed training (#19918). The following usage was previously possible but would result in unpredictable hangs and timeouts in distributed training:We decided to raise an error if the user attempts to return
None
when running in a multi-GPU setting.Miscellaneous Changes
prepare_data()
hook inLightningModule
andLightningDataModule
is now subject to a barrier without timeout to avoid long-running tasks to be interrupted (#19448). Similarly, also in Fabric theFabric.rank_zero_first
context manager now uses an infinite barrier (#19448).CHANGELOG
PyTorch Lightning
Added
ModelSummary
andRichModelSummary
callbacks now display the training mode of each layer in the column "Mode" (#19468)load_from_checkpoint
support forLightningCLI
when using dependency injection (#18105)on_exception
hook toLightningDataModule
(#19601)ModelParallelStrategy
to support 2D parallelism (#19878, #19888)torch.distributed.destroy_process_group
in atexit handler if process group needs destruction (#19931)FSDPStrategy(device_mesh=...)
argument (#19504)Changed
prepare_data()
hook inLightningModule
andLightningDataModule
is now subject to a barrier without timeout to avoid long-running tasks to be interrupted (#19448)drop_last
for prediction (#19678)training_step()
by returningNone
in distributed training (#19918)Removed
Trainer(strategy="bagua")
) (#19445)Fixed
WandbLogger.log_hyperparameters()
raising an error if hyperparameters are not JSON serializable (#19769)ModelCheckpoint(save_last=...)
argument (#19808)epoch_loop.restarting
to avoid full validation run afterLearningRateFinder
(#19818)Lightning Fabric
Added
fabric consolidate
in the new CLI (#19560)_FabricModule.mark_forward_method()
(#19690)ModelParallelStrategy
to support 2D parallelism (#19846, #19852, #19870, #19872)torch.distributed.destroy_process_group
in atexit handler if process group needs destruction (#19931)FSDPStrategy(device_mesh=...)
argument (#19504)Changed
lightning run model
tofabric run
(#19442, #19527)Fabric.rank_zero_first
context manager now uses a barrier without timeout to avoid long-running tasks to be interrupted (#19448)fabric.backward()
when it is needed by the strategy or precision selection (#19447, #19493)_BackwardSyncControl
can now control what to do when gradient accumulation is disabled (#19577)Removed
Fixed
Full commit list: 2.2.0 -> 2.3.0
Contributors
We thank all our contributors who submitted pull requests for features, bug fixes and documentation updates.
New Contributors
Did you know?
Chuck Norris is a big fan and daily user of Lightning Studio.
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