Refactor CombinedLoader using pytrees

src/lightning/pytorch/CHANGELOG.md

@@ -62,6 +62,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Renamed `TQDMProgressBar.main_progress_bar` to `TQDMProgressBar.train_progress_bar` ([#16695](https://github.com/Lightning-AI/lightning/pull/16695))
 
+- Marked `lightning.pytorch.utilities.supporters.CombinedDataset` as protected ([#16714](https://github.com/Lightning-AI/lightning/pull/16714))
 
 ### Deprecated
 
@@ -223,6 +224,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Removed the unused `lightning.pytorch.utilities.metrics.metrics_to_scalars` function ([#16681](https://github.com/Lightning-AI/lightning/pull/16681))
 
+- Removed the unused `lightning.pytorch.utilities.supporters.{SharedCycleIteratorState,CombinedLoaderIterator}` classes ([#16714](https://github.com/Lightning-AI/lightning/pull/16714))
+
 ### Fixed
 
 - Fixed an unintended limitation for calling `save_hyperparameters` on mixin classes that don't subclass `LightningModule`/`LightningDataModule` ([#16369](https://github.com/Lightning-AI/lightning/pull/16369))

src/lightning/pytorch/loops/fetchers.py

@@ -17,7 +17,7 @@
 from torch.utils.data.dataloader import DataLoader
 
 from lightning.fabric.utilities.data import has_len
-from lightning.pytorch.trainer.supporters import CombinedLoader
+from lightning.pytorch.trainer.supporters import _shutdown_workers_and_reset_iterator, CombinedLoader
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 
 
@@ -45,14 +45,6 @@ def dataloader(self) -> Iterable:
             )
         return self._dataloader
 
-    @property
-    def loader_iters(self) -> Any:
-        if self.dataloader_iter is None:
-            raise MisconfigurationException("The `dataloader_iter` isn't available outside the __iter__ context.")
-        if isinstance(self.dataloader, CombinedLoader):
-            return self.dataloader_iter.loader_iters
-        return self.dataloader_iter
-
     def __iter__(self) -> "_DataFetcher":
         self.reset()
         self.dataloader_iter = iter(self.dataloader)
@@ -80,7 +72,7 @@ def teardown(self) -> None:
         if isinstance(self._dataloader, CombinedLoader):
             self._dataloader.reset()
         if isinstance(self._dataloader, DataLoader):
-            CombinedLoader._shutdown_workers_and_reset_iterator(self._dataloader)
+            _shutdown_workers_and_reset_iterator(self._dataloader)
         self.dataloader_iter = None
 
 

src/lightning/pytorch/trainer/connectors/data_connector.py

@@ -28,7 +28,7 @@
 from lightning.pytorch.overrides.distributed import UnrepeatedDistributedSamplerWrapper
 from lightning.pytorch.strategies import DDPSpawnStrategy
 from lightning.pytorch.trainer.states import RunningStage, TrainerFn
-from lightning.pytorch.trainer.supporters import CombinedLoader, CycleIterator
+from lightning.pytorch.trainer.supporters import _LITERAL_SUPPORTED_MODES, CombinedLoader
 from lightning.pytorch.utilities.data import _is_dataloader_shuffled, _update_dataloader, has_len_all_ranks
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.model_helpers import is_overridden
@@ -40,7 +40,7 @@
 
 
 class DataConnector:
-    def __init__(self, trainer: "pl.Trainer", multiple_trainloader_mode: str = "max_size_cycle"):
+    def __init__(self, trainer: "pl.Trainer", multiple_trainloader_mode: _LITERAL_SUPPORTED_MODES = "max_size_cycle"):
         self.trainer = trainer
         self.multiple_trainloader_mode = multiple_trainloader_mode
         self._train_dataloader_source = _DataLoaderSource(None, "")
@@ -239,28 +239,17 @@ def _prepare_dataloader(
         """This function handles the following functionalities:
 
         - Injecting a `DistributedDataSamplerWrapper` into the `DataLoader` if on a distributed environment
-        - Wrapping the datasets and samplers into fault-tolerant components
         - Wrapping the dataloader based on strategy-specific logic
         """
         if isinstance(dataloader, CombinedLoader):
-            # apply `_prepare_dataloader` on all the collection of loaders
-            dataloader.loaders = apply_to_collection(
-                dataloader.loaders, (DataLoader, CycleIterator), self._prepare_dataloader, shuffle, mode=mode
-            )
-            # the length need to recomputed across all dataloaders in case of special behavior.
-            dataloader._apply_cycle_iterator_length()
+            for i, dl in enumerate(dataloader._loaders_flattened):
+                dataloader._update_index(self._prepare_dataloader(dl, shuffle=shuffle, mode=mode), i)
             return dataloader
 
         # don't do anything if it's not a dataloader
-        if not isinstance(dataloader, (DataLoader, CycleIterator)):
+        if not isinstance(dataloader, DataLoader):
             return dataloader
 
-        cycle_iterator: Optional[CycleIterator] = None
-
-        if isinstance(dataloader, CycleIterator):
-            cycle_iterator = dataloader
-            dataloader = dataloader.loader
-
         if (
             self._requires_distributed_sampler(dataloader)  # sets the distributed sampler
             or mode == RunningStage.PREDICTING  # to track indices for the predictions
@@ -277,10 +266,6 @@ def _prepare_dataloader(
 
         dataloader = self.trainer.strategy.process_dataloader(dataloader)
 
-        if cycle_iterator is not None:
-            cycle_iterator.loader = dataloader
-            return cycle_iterator
-
         return dataloader
 
     def _resolve_sampler(