Deprecate TrainerDataLoadingMixin and move logic to DataConnector

CHANGELOG.md

@@ -225,6 +225,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `TrainerCallbackHookMixin` ([#11148](https://github.com/PyTorchLightning/pytorch-lightning/pull/11148))
 
 
+- Deprecated `TrainerDataLoadingMixin` and moved functionality to `Trainer` and `DataConnector` ([#11282](https://github.com/PyTorchLightning/pytorch-lightning/pull/11282))
+
+
 - Deprecated function `pytorch_lightning.callbacks.device_stats_monitor.prefix_metric_keys` ([#11254](https://github.com/PyTorchLightning/pytorch-lightning/pull/11254))
 
 

pytorch_lightning/loops/dataloader/evaluation_loop.py

@@ -188,7 +188,7 @@ def _reload_evaluation_dataloaders(self) -> None:
         """Reloads dataloaders if necessary."""
         if self.trainer.testing:
             self.trainer.reset_test_dataloader()
-        elif self.trainer.val_dataloaders is None or self.trainer._should_reload_val_dl:
+        elif self.trainer.val_dataloaders is None or self.trainer._data_connector._should_reload_val_dl:
             self.trainer.reset_val_dataloader()
 
     def _on_evaluation_start(self, *args: Any, **kwargs: Any) -> None:

pytorch_lightning/loops/fit_loop.py

@@ -203,7 +203,7 @@ def on_advance_start(self) -> None:  # type: ignore[override]
         model = self.trainer.lightning_module
 
         # reset train dataloader
-        if not self._is_fresh_start_epoch and self.trainer._should_reload_train_dl:
+        if not self._is_fresh_start_epoch and self.trainer._data_connector._should_reload_train_dl:
             self.trainer.reset_train_dataloader(model)
         self._is_fresh_start_epoch = False
 
@@ -223,6 +223,7 @@ def on_advance_start(self) -> None:  # type: ignore[override]
 
     def advance(self) -> None:  # type: ignore[override]
         """Runs one whole epoch."""
+        assert self.trainer.train_dataloader is not None
         dataloader = self.trainer.strategy.process_dataloader(self.trainer.train_dataloader)
         data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader)
 

pytorch_lightning/strategies/ipu.py

@@ -121,8 +121,8 @@ def setup(self, trainer: "pl.Trainer") -> None:
         # patch the dataloader creation function with the custom `poptorch.DataLoader`.
         # this violates the intended control flow for the plugins, but since this is experimental, we have chosen
         # to use the simpler solution before adding abstractions to override the `DataLoader` class
-        self._update_dataloader_original = pl.trainer.data_loading._update_dataloader
-        pl.trainer.data_loading._update_dataloader = self._convert_to_poptorch_loader
+        self._update_dataloader_original = pl.trainer.connectors.data_connector._update_dataloader
+        pl.trainer.connectors.data_connector._update_dataloader = self._convert_to_poptorch_loader
 
         super().setup(trainer)
 
@@ -278,7 +278,7 @@ def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
     def teardown(self) -> None:
         super().teardown()
         # undo dataloader patching
-        pl.trainer.data_loading._update_dataloader = self._update_dataloader_original
+        pl.trainer.connectors.data_connector._update_dataloader = self._update_dataloader_original
 
         for model in self.poptorch_models.values():
             model.destroy()

pytorch_lightning/trainer/trainer.py

@@ -27,6 +27,7 @@
 import torch
 from packaging.version import Version
 from torch.optim import Optimizer
+from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
 from pytorch_lightning.accelerators import Accelerator, IPUAccelerator
@@ -70,6 +71,7 @@
 from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
 from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
 from pytorch_lightning.trainer.states import RunningStage, TrainerFn, TrainerState, TrainerStatus
+from pytorch_lightning.trainer.supporters import CombinedLoader
 from pytorch_lightning.tuner.lr_finder import _LRFinder
 from pytorch_lightning.tuner.tuning import Tuner
 from pytorch_lightning.utilities import (
@@ -85,14 +87,17 @@
     rank_zero_info,
     rank_zero_warn,
 )
+from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.argparse import (
     _defaults_from_env_vars,
     add_argparse_args,
     from_argparse_args,
     parse_argparser,
     parse_env_variables,
 )
+from pytorch_lightning.utilities.auto_restart import _add_capture_metadata_collate
 from pytorch_lightning.utilities.cloud_io import get_filesystem
+from pytorch_lightning.utilities.data import _auto_add_worker_init_fn, has_len_all_ranks
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
@@ -119,9 +124,9 @@
 
 
 class Trainer(
-    TrainerCallbackHookMixin,
-    TrainerOptimizersMixin,
-    TrainerDataLoadingMixin,
+    TrainerCallbackHookMixin,  # TODO: Remove in v1.8
+    TrainerOptimizersMixin,  # TODO: Remove in v1.8
+    TrainerDataLoadingMixin,  # TODO: Remove in v1.8
 ):
     # Needed because of LightningOptimizer
     _lightning_optimizers = None
@@ -2364,6 +2369,151 @@ def terminate_on_nan(self, val: bool) -> None:
         )
         self._terminate_on_nan = val  # : 212
 
+    def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
+        """Resets the train dataloader and initialises required variables (number of batches, when to validate,
+        etc.).
+
+        Args:
+            model: The ``LightningModule`` if calling this outside of the trainer scope.
+        """
+        self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING, model=model)
+
+        if self.overfit_batches > 0:
+            self.train_dataloader = self._data_connector._resolve_overfit_batches(self.train_dataloader)
+
+        # automatically add samplers
+        self.train_dataloader = apply_to_collection(
+            self.train_dataloader,
+            DataLoader,
+            self._data_connector._prepare_dataloader,
+            shuffle=True,
+            mode=RunningStage.TRAINING,
+        )
+
+        # check the workers recursively
+        apply_to_collection(self.train_dataloader, DataLoader, self._data_connector._worker_check, "train_dataloader")
+
+        # add worker_init_fn for correct seeding in worker processes
+        apply_to_collection(self.train_dataloader, DataLoader, _auto_add_worker_init_fn, rank=self.global_rank)
+
+        # add collate_fn to collect metadata for fault tolerant training
+        if _fault_tolerant_training():
+            apply_to_collection(self.train_dataloader, DataLoader, _add_capture_metadata_collate)
+
+        # wrap the sequence of train loaders to a CombinedLoader object for computing the num_training_batches
+        self.train_dataloader = CombinedLoader(self.train_dataloader, self._data_connector.multiple_trainloader_mode)
+
+        module = model or self.lightning_module or self.datamodule
+        self.num_training_batches = (
+            len(self.train_dataloader)
+            if has_len_all_ranks(self.train_dataloader, self.strategy, module)
+            else float("inf")
+        )
+
+        if isinstance(self.limit_train_batches, int) or self.limit_train_batches == 0.0:
+            self.num_training_batches = min(self.num_training_batches, int(self.limit_train_batches))
+        elif self.num_training_batches != float("inf"):
+            self.num_training_batches = int(self.num_training_batches * self.limit_train_batches)
+        elif self.limit_train_batches != 1.0:
+            raise MisconfigurationException(
+                "When using an IterableDataset for `limit_train_batches`,"
+                " `Trainer(limit_train_batches)` must be `0.0`, `1.0` or an int. An int k specifies"
+                " `num_training_batches` to use."
+            )
+
+        # determine when to check validation
+        # if int passed in, val checks that often
+        # otherwise, it checks in [0, 1.0] % range of a training epoch
+        if isinstance(self.val_check_interval, int):
+            self.val_check_batch = self.val_check_interval
+            if self.val_check_batch > self.num_training_batches:
+                raise ValueError(
+                    f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
+                    f"to the number of the training batches ({self.num_training_batches}). "
+                    "If you want to disable validation set `limit_val_batches` to 0.0 instead."
+                )
+        else:
+            if not has_len_all_ranks(self.train_dataloader, self.strategy, module):
+                if self.val_check_interval == 1.0:
+                    self.val_check_batch = float("inf")
+                else:
+                    raise MisconfigurationException(
+                        "When using an IterableDataset for `train_dataloader`,"
+                        " `Trainer(val_check_interval)` must be `1.0` or an int. An int k specifies"
+                        " checking validation every k training batches."
+                    )
+            else:
+                self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
+                self.val_check_batch = max(1, self.val_check_batch)
+
+        if self.logger and self.num_training_batches < self.log_every_n_steps:
+            rank_zero_warn(
+                f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
+                f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
+                " you want to see logs for the training epoch.",
+                category=PossibleUserWarning,
+            )
+
+        # store epoch of dataloader reset for reload_dataloaders_every_n_epochs
+        self._last_train_dl_reload_epoch = self.current_epoch
+
+    def reset_val_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
+        """Resets the validation dataloader and determines the number of batches.
+
+        Args:
+            model: The ``LightningModule`` if called outside of the trainer scope.
+        """
+        source = self._data_connector._val_dataloader_source
+        pl_module = self.lightning_module or model
+        has_step = is_overridden("validation_step", pl_module)
+        if source.is_defined() and has_step:
+            self.num_val_batches, self.val_dataloaders = self._data_connector._reset_eval_dataloader(
+                RunningStage.VALIDATING, model=pl_module
+            )
+
+            # store epoch of dataloader reset for reload_dataloaders_every_n_epochs
+            self._last_val_dl_reload_epoch = self.current_epoch
+
+    def reset_test_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
+        """Resets the test dataloader and determines the number of batches.
+
+        Args:
+            model: The ``LightningModule`` if called outside of the trainer scope.
+        """
+        source = self._data_connector._test_dataloader_source
+        pl_module = self.lightning_module or model
+        has_step = is_overridden("test_step", pl_module)
+        if source.is_defined() and has_step:
+            self.num_test_batches, self.test_dataloaders = self._data_connector._reset_eval_dataloader(
+                RunningStage.TESTING, model=pl_module
+            )
+
+    def reset_predict_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
+        """Resets the predict dataloader and determines the number of batches.
+
+        Args:
+            model: The ``LightningModule`` if called outside of the trainer scope.
+        """
+        source = self._data_connector._predict_dataloader_source
+        pl_module = self.lightning_module or model
+        if source.is_defined():
+            self.num_predict_batches, self.predict_dataloaders = self._data_connector._reset_eval_dataloader(
+                RunningStage.PREDICTING, model=pl_module
+            )
+
+    def reset_train_val_dataloaders(self, model: Optional["pl.LightningModule"] = None) -> None:
+        """Resets train and val dataloaders if none are attached to the trainer.
+
+        The val dataloader must be initialized before training loop starts, as the training loop
+        inspects the val dataloader to determine whether to run the evaluation loop.
+        Args:
+            model: The ``LightningModule`` if called outside of the trainer scope.
+        """
+        if self.train_dataloader is None:
+            self.reset_train_dataloader(model=model)
+        if self.val_dataloaders is None:
+            self.reset_val_dataloader(model=model)
+
 
 def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
     if 0 <= batches <= 1:

tests/deprecated_api/test_remove_1-8.py

@@ -31,11 +31,12 @@
 from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin
 from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
 from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
+from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.enums import DeviceType, DistributedType
 from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
-from tests.helpers.boring_model import BoringModel
+from tests.helpers.boring_model import BoringDataModule, BoringModel
 from tests.helpers.runif import RunIf
 from tests.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
 
@@ -271,6 +272,22 @@ def test_v1_8_0_deprecated_training_type_plugin_property():
         trainer.training_type_plugin
 
 
+def test_v1_8_0_deprecate_trainer_data_loading_mixin():
+    trainer = Trainer(max_epochs=1)
+    model = BoringModel()
+    dm = BoringDataModule()
+    trainer.fit(model, datamodule=dm)
+
+    with pytest.deprecated_call(
+        match=r"`TrainerDataLoadingMixin.prepare_dataloader` was deprecated in v1.6 and will be removed in v1.8.",
+    ):
+        trainer.prepare_dataloader(dataloader=model.train_dataloader, shuffle=False)
+    with pytest.deprecated_call(
+        match=r"`TrainerDataLoadingMixin.request_dataloader` was deprecated in v1.6 and will be removed in v1.8.",
+    ):
+        trainer.request_dataloader(stage=RunningStage.TRAINING)
+
+
 def test_v_1_8_0_deprecated_device_stats_monitor_prefix_metric_keys():
     from pytorch_lightning.callbacks.device_stats_monitor import prefix_metric_keys
 

tests/trainer/flags/test_limit_batches.py

@@ -62,7 +62,7 @@ def test_eval_limit_batches(stage, mode, limit_batches):
     trainer = Trainer(**{limit_eval_batches: limit_batches})
     model.trainer = trainer
     trainer._data_connector.attach_dataloaders(model)
-    loader_num_batches, dataloaders = trainer._reset_eval_dataloader(stage, model=model)
+    loader_num_batches, dataloaders = trainer._data_connector._reset_eval_dataloader(stage, model=model)
     expected_batches = int(limit_batches * len(eval_loader)) if isinstance(limit_batches, float) else limit_batches
     assert loader_num_batches[0] == expected_batches
     assert len(dataloaders[0]) == len(eval_loader)

tests/trainer/flags/test_overfit_batches.py

@@ -80,7 +80,7 @@ def test_overfit_batch_limits_eval(stage, mode, overfit_batches):
     model.trainer = trainer
     trainer._data_connector.attach_datamodule(model, datamodule=dm)
 
-    loader_num_batches, dataloaders = trainer._reset_eval_dataloader(stage, model=model)
+    loader_num_batches, dataloaders = trainer._data_connector._reset_eval_dataloader(stage, model=model)
     if stage == RunningStage.VALIDATING:
         assert loader_num_batches[0] == 0
     else:

tests/trainer/test_data_loading.py

@@ -248,17 +248,17 @@ def __init__(
     class CustomDummyObj:
         sampler = None
 
-    result = trainer.prepare_dataloader(CustomDummyObj(), shuffle=True)
+    result = trainer._data_connector._prepare_dataloader(CustomDummyObj(), shuffle=True)
     assert isinstance(result, CustomDummyObj), "Wrongly reinstantiated data loader"
 
     dataset = list(range(10))
-    result = trainer.prepare_dataloader(CustomDataLoader(dataset), shuffle=True)
+    result = trainer._data_connector._prepare_dataloader(CustomDataLoader(dataset), shuffle=True)
     assert isinstance(result, DataLoader)
     assert isinstance(result, CustomDataLoader)
     assert result.dummy_kwarg is None
 
     # Shuffled DataLoader should also work
-    result = trainer.prepare_dataloader(CustomDataLoader(dataset, shuffle=True), shuffle=True)
+    result = trainer._data_connector._prepare_dataloader(CustomDataLoader(dataset, shuffle=True), shuffle=True)
     assert isinstance(result, DataLoader)
     assert isinstance(result, CustomDataLoader)
     assert result.dummy_kwarg is None
@@ -269,7 +269,7 @@ class CustomSampler(Sampler):
     # Should raise an error if existing sampler is being replaced
     dataloader = CustomDataLoader(dataset, sampler=CustomSampler(dataset))
     with pytest.raises(MisconfigurationException, match="will be replaced by `DistributedSampler`"):
-        trainer.prepare_dataloader(dataloader, shuffle=True)
+        trainer._data_connector._prepare_dataloader(dataloader, shuffle=True)
 
 
 class LoaderTestModel(BoringModel):
@@ -351,7 +351,7 @@ def test_error_raised_with_float_limited_eval_batches():
         MisconfigurationException,
         match=fr"{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`",
     ):
-        trainer._reset_eval_dataloader(RunningStage.VALIDATING, model)
+        trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)
 
 
 @pytest.mark.parametrize(
@@ -375,4 +375,4 @@ def test_non_sequential_sampler_warning_is_raised_for_eval_dataloader(val_dl):
     model = BoringModel()
     trainer._data_connector.attach_data(model, val_dataloaders=val_dl)
     with pytest.warns(PossibleUserWarning, match="recommended .* turn this off for val/test/predict"):
-        trainer._reset_eval_dataloader(RunningStage.VALIDATING, model)
+        trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)

tests/trainer/test_dataloaders.py

@@ -563,7 +563,7 @@ def train_dataloader(self):
 @RunIf(skip_windows=True)
 @pytest.mark.parametrize("ckpt_path", (None, "best", "specific"))
 @pytest.mark.parametrize("stage", ("train", "test", "val"))
-@patch("pytorch_lightning.trainer.data_loading.multiprocessing.cpu_count", return_value=4)
+@patch("pytorch_lightning.trainer.connectors.data_connector.multiprocessing.cpu_count", return_value=4)
 def test_warning_with_few_workers(_, tmpdir, ckpt_path, stage):
     """Test that error is raised if dataloader with only a few workers is used."""
 
@@ -593,7 +593,7 @@ def test_warning_with_few_workers(_, tmpdir, ckpt_path, stage):
 @RunIf(skip_windows=True)
 @pytest.mark.parametrize("ckpt_path", (None, "best", "specific"))
 @pytest.mark.parametrize("stage", ("train", "test", "val"))
-@patch("pytorch_lightning.trainer.data_loading.multiprocessing.cpu_count", return_value=4)
+@patch("pytorch_lightning.trainer.connectors.data_connector.multiprocessing.cpu_count", return_value=4)
 def test_warning_with_few_workers_multi_loader(_, tmpdir, ckpt_path, stage):
     """Test that error is raised if dataloader with only a few workers is used."""