Sequential CombinedLoader to flatten the eval and predict loops

src/lightning/pytorch/CHANGELOG.md

@@ -42,6 +42,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added a new method `Strategy.on_exception` to the strategy base interface ([#16646](https://github.com/Lightning-AI/lightning/pull/16646))
 
 
+- Added support for `predict_step(dataloader_iter, batch_index)` ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
+- Added support for arbitrary iterables as dataloaders ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
 - Added "sequential" mode support to `CombinedLoader` to consume multiple iterables in sequence ([#16743](https://github.com/Lightning-AI/lightning/pull/16743), [#16784](https://github.com/Lightning-AI/lightning/pull/16784))
 
 ### Changed
@@ -87,6 +93,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Renamed `CombinedLoader.loaders` to `CombinedLoader.iterables` ([#16743](https://github.com/Lightning-AI/lightning/pull/16743))
 
 
+- The top-level loops now own the data sources and combined dataloaders ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
+- The `trainer.*_dataloader` properties now return what the user returned in their `LightningModule.*_dataloader()` hook ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
 - The `dataloader_idx` argument is now optional for the `on_{validation,test,predict}_batch_{start,end}` hooks. Remove it or default it to 0 if you don't use multiple dataloaders ([#16753](https://github.com/Lightning-AI/lightning/pull/16753))
 
 
@@ -210,6 +222,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
   * The fetching classes are now marked as protected ([#16664](https://github.com/Lightning-AI/lightning/pull/16664))
 
 
+- Removed the `DataLoaderLoop`, `EvaluationEpochLoop`, and `PredictionEpochLoop` classes ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
+- Removed `trainer.reset_*_dataloader()` methods in favor of `Loop.setup_data()` for the top-level loops ([#16726](https://github.com/Lightning-AI/lightning/pull/16726))
+
+
 - Removed special support for truncated backpropagation through time (TBPTT) ([#16172](https://github.com/Lightning-AI/lightning/pull/16172))
   * Removed the `LightningModule.truncated_bptt_steps` attribute
   * Removed the `LightningModule.tbptt_split_batch` hook

src/lightning/pytorch/callbacks/batch_size_finder.py

@@ -128,24 +128,25 @@ def __init__(
     def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
         if trainer._accelerator_connector.is_distributed:
             raise MisconfigurationException("The Batch size finder is not supported with distributed strategies.")
-
-        running_stage = trainer.state.stage
-        assert running_stage is not None
-        dl_source = getattr(trainer._data_connector, f"_{running_stage.dataloader_prefix}_dataloader_source")
-
         # TODO: check if this can be enabled (#4040)
-        if not trainer._data_connector._train_dataloader_source.is_module():
+        if not trainer.fit_loop._data_source.is_module():
             raise MisconfigurationException(
                 "The Batch size finder cannot be used with dataloaders passed directly to `.fit()`. Please disable"
                 " the feature or incorporate the dataloader into your LightningModule or LightningDataModule."
             )
 
         # TODO: Add support for multiple eval dataloader
         if stage != "fit":
-            dataloaders = dl_source.dataloader()
-            if isinstance(dataloaders, list) and len(dataloaders) > 1:
+            loop = trainer._active_loop
+            assert loop is not None
+            loop.setup_data()
+            combined_loader = loop._combined_loader
+            assert combined_loader is not None
+            if len(combined_loader._flattened) > 1:
+                stage = trainer.state.stage
+                assert stage is not None
                 raise MisconfigurationException(
-                    f"The Batch size finder cannot be used with multiple {running_stage.dataloader_prefix} dataloaders."
+                    f"The Batch size finder cannot be used with multiple {stage.dataloader_prefix} dataloaders."
                 )
 
         if not lightning_hasattr(pl_module, self._batch_arg_name):
@@ -167,7 +168,6 @@ def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: O
     def scale_batch_size(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         new_size = _scale_batch_size(
             trainer,
-            pl_module,
             self._mode,
             self._steps_per_trial,
             self._init_val,

src/lightning/pytorch/callbacks/prediction_writer.py

@@ -140,7 +140,7 @@ def on_predict_batch_end(
     ) -> None:
         if not self.interval.on_batch:
             return
-        batch_indices = trainer.predict_loop.epoch_loop.current_batch_indices
+        batch_indices = trainer.predict_loop.current_batch_indices
         self.write_on_batch_end(trainer, pl_module, outputs, batch_indices, batch, batch_idx, dataloader_idx)
 
     def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:

src/lightning/pytorch/loops/__init__.py

@@ -12,7 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from lightning.pytorch.loops.loop import _Loop  # noqa: F401 isort: skip (avoids circular imports)
-from lightning.pytorch.loops.dataloader import _DataLoaderLoop, _EvaluationLoop, _PredictionLoop  # noqa: F401
-from lightning.pytorch.loops.epoch import _EvaluationEpochLoop, _PredictionEpochLoop, _TrainingEpochLoop  # noqa: F401
+from lightning.pytorch.loops.epoch import _TrainingEpochLoop  # noqa: F401
+from lightning.pytorch.loops.evaluation_loop import _EvaluationLoop  # noqa: F401
 from lightning.pytorch.loops.fit_loop import _FitLoop  # noqa: F401
 from lightning.pytorch.loops.optimization import _AutomaticOptimization, _ManualOptimization  # noqa: F401
+from lightning.pytorch.loops.prediction_loop import _PredictionLoop  # noqa: F401

src/lightning/pytorch/loops/epoch/__init__.py

@@ -12,6 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lightning.pytorch.loops.epoch.evaluation_epoch_loop import _EvaluationEpochLoop  # noqa: F401
-from lightning.pytorch.loops.epoch.prediction_epoch_loop import _PredictionEpochLoop  # noqa: F401
 from lightning.pytorch.loops.epoch.training_epoch_loop import _TrainingEpochLoop  # noqa: F401