Bump pytorch-lightning from 1.4.2 to 1.6.0

[dependabot]

Bumps pytorch-lightning from 1.4.2 to 1.6.0.

Release notes

Sourced from pytorch-lightning's releases.

PyTorch Lightning 1.6: Support Intel's Habana Accelerator, New efficient DDP strategy (Bagua), Manual Fault-tolerance, Stability and Reliability.

The core team is excited to announce the PyTorch Lightning 1.6 release ⚡

• Highlights
• Backward Incompatible Changes
• Full Changelog
• Contributors

Highlights

PyTorch Lightning 1.6 is the work of 99 contributors who have worked on features, bug-fixes, and documentation for a total of over 750 commits since 1.5. This is our most active release yet. Here are some highlights:

Introducing Intel's Habana Accelerator

Lightning 1.6 now supports the Habana® framework, which includes Gaudi® AI training processors. Their heterogeneous architecture includes a cluster of fully programmable Tensor Processing Cores (TPC) along with its associated development tools and libraries and a configurable Matrix Math engine.

You can leverage the Habana hardware to accelerate your Deep Learning training workloads simply by passing:

trainer = pl.Trainer(accelerator="hpu")
single Gaudi training
trainer = pl.Trainer(accelerator="hpu", devices=1)
distributed training with 8 Gaudi
trainer = pl.Trainer(accelerator="hpu", devices=8)

The Bagua Strategy

The Bagua Strategy is a deep learning acceleration framework that supports multiple, advanced distributed training algorithms with state-of-the-art system relaxation techniques. Enabling Bagua, which can be considerably faster than vanilla PyTorch DDP, is as simple as:

trainer = pl.Trainer(strategy="bagua")
or to choose a custom algorithm
trainer = pl.Trainer(strategy=BaguaStrategy(algorithm="gradient_allreduce")  # default

Towards stable Accelerator, Strategy, and Plugin APIs

The Accelerator, Strategy, and Plugin APIs are a core part of PyTorch Lightning. They're where all the distributed boilerplate lives, and we're constantly working to improve both them and the overall PyTorch Lightning platform experience.

In this release, we've made some large changes to achieve that goal. Not to worry, though! The only users affected by these changes are those who use custom implementations of Accelerator and Strategy (TrainingTypePlugin) as well as certain Plugins. In particular, we want to highlight the following changes:

• All TrainingTypePlugins have been renamed to Strategy (#11120). Strategy is a more appropriate name because it encompasses more than simply training communcation. This change is now aligned with the changes we implemented in 1.5, which introduced the new strategy and devices flags to the Trainer.

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.6.0] - 2022-03-29

Added

• Allow logging to an existing run ID in MLflow with MLFlowLogger (#12290)
• Enable gradient accumulation using Horovod's backward_passes_per_step (#11911)
• Add new DETAIL log level to provide useful logs for improving monitoring and debugging of batch jobs (#11008)
• Added a flag SLURMEnvironment(auto_requeue=True|False) to control whether Lightning handles the requeuing (#10601)
• Fault Tolerant Manual
  • Add _Stateful protocol to detect if classes are stateful (#10646)
  • Add _FaultTolerantMode enum used to track different supported fault tolerant modes (#10645)
  • Add a _rotate_worker_indices utility to reload the state according the latest worker (#10647)
  • Add stateful workers (#10674)
  • Add an utility to collect the states across processes (#10639)
  • Add logic to reload the states across data loading components (#10699)
  • Cleanup some fault tolerant utilities (#10703)
  • Enable Fault Tolerant Manual Training (#10707)
  • Broadcast the _terminate_gracefully to all processes and add support for DDP (#10638)
• Added support for re-instantiation of custom (subclasses of) DataLoaders returned in the *_dataloader() methods, i.e., automatic replacement of samplers now works with custom types of DataLoader (#10680)
• Added a function to validate if fault tolerant training is supported. (#10465)
• Added a private callback to manage the creation and deletion of fault-tolerance checkpoints (#11862)
• Show a better error message when a custom DataLoader implementation is not well implemented and we need to reconstruct it (#10719)
• Show a better error message when frozen dataclass is used as a batch (#10927)
• Save the Loop's state by default in the checkpoint (#10784)
• Added Loop.replace to easily switch one loop for another (#10324)
• Added support for --lr_scheduler=ReduceLROnPlateau to the LightningCLI (#10860)
• Added LightningCLI.configure_optimizers to override the configure_optimizers return value (#10860)
• Added LightningCLI(auto_registry) flag to register all subclasses of the registerable components automatically (#12108)
• Added a warning that shows when max_epochs in the Trainer is not set (#10700)
• Added support for returning a single Callback from LightningModule.configure_callbacks without wrapping it into a list (#11060)
• Added console_kwargs for RichProgressBar to initialize inner Console (#10875)
• Added support for shorthand notation to instantiate loggers with the LightningCLI (#11533)
• Added a LOGGER_REGISTRY instance to register custom loggers to the LightningCLI (#11533)
• Added info message when the Trainer arguments limit_*_batches, overfit_batches, or val_check_interval are set to 1 or 1.0 (#11950)
• Added a PrecisionPlugin.teardown method (#10990)
• Added LightningModule.lr_scheduler_step (#10249)
• Added support for no pre-fetching to DataFetcher (#11606)
• Added support for optimizer step progress tracking with manual optimization (#11848)
• Return the output of the optimizer.step. This can be useful for LightningLite users, manual optimization users, or users overriding LightningModule.optimizer_step (#11711)
• Teardown the active loop and strategy on exception (#11620)
• Added a MisconfigurationException if user provided opt_idx in scheduler config doesn't match with actual optimizer index of its respective optimizer (#11247)
• Added a loggers property to Trainer which returns a list of loggers provided by the user (#11683)
• Added a loggers property to LightningModule which retrieves the loggers property from Trainer (#11683)
• Added support for DDP when using a CombinedLoader for the training data (#11648)
• Added a warning when using DistributedSampler during validation/testing (#11479)
• Added support for Bagua training strategy (#11146)
• Added support for manually returning a poptorch.DataLoader in a *_dataloader hook (#12116)
• Added rank_zero module to centralize utilities (#11747)
• Added a _Stateful support for LightningDataModule (#11637)
• Added _Stateful support for PrecisionPlugin (#11638)

... (truncated)

Commits

• 44e3edb Cleanup CHANGELOG (#12507)
• e3893b9 Merge pull request #12509 from RobertLaurella/patch-1
• 041da41 Remove TPU Availability check from parse devices (#12326)
• 4fe0076 Prepare for the 1.6.0 release
• 17215ed Fix titles capitalization in docs
• a775804 Update Plugins doc (#12440)
• 71e25f3 Update CI in README.md (#12495)
• c6cb634 Add usage of Jupyter magic command for loggers (#12333)
• 42169a2 Add typing to LightningModule.trainer (#12345)
• 2de6a9b Fix warning message formatting in save_hyperparameters (#12498)
• Additional commits viewable in compare view

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[brycedrennan]

FYI in my fork I switched to a newer pytorch lightning and it did require several code changes to get training working. Not sure if inference is affected as well.

[soulteary]

FYI in my fork I switched to a newer pytorch lightning and it did require several code changes to get training working. Not sure if inference is affected as well.

In PyTorch 2.x (2.0/2.1/2.2) version, the corresponding 2.x version of lightning inference works.

Time flies, and maybe this PR in the repository is more suitable for now. #281