Remove the deprecated on_{train,val,test,predict}_dataloader hooks (#13033)

Co-authored-by: rohitgr7 <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>

Author: 3 people

CHANGELOG.md

@@ -115,6 +115,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed the deprecated `checkpoint_callback` argument from the `Trainer` constructor ([#13027](https://github.com/PyTorchLightning/pytorch-lightning/pull/13027))
 
 
+- Removed the deprecated `on_{train,val,test,predict}_dataloader` hooks from the `LightningModule` and `LightningDataModule` ([#13033](https://github.com/PyTorchLightning/pytorch-lightning/pull/13033))
+
+
 - Removed the deprecated `TestTubeLogger` ([#12859](https://github.com/PyTorchLightning/pytorch-lightning/pull/12859))
 
 

docs/source/common/lightning_module.rst

@@ -1609,30 +1609,6 @@ predict_dataloader
 .. automethod:: pytorch_lightning.core.lightning.LightningModule.predict_dataloader
     :noindex:
 
-on_train_dataloader
-~~~~~~~~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.lightning.LightningModule.on_train_dataloader
-    :noindex:
-
-on_val_dataloader
-~~~~~~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.lightning.LightningModule.on_val_dataloader
-    :noindex:
-
-on_test_dataloader
-~~~~~~~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.lightning.LightningModule.on_test_dataloader
-    :noindex:
-
-on_predict_dataloader
-~~~~~~~~~~~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.lightning.LightningModule.on_predict_dataloader
-    :noindex:
-
 transfer_batch_to_device
 ~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/data/datamodule.rst

@@ -360,30 +360,6 @@ state_dict
 .. automethod:: pytorch_lightning.core.datamodule.LightningDataModule.state_dict
     :noindex:
 
-on_train_dataloader
-===================
-
-.. automethod:: pytorch_lightning.core.datamodule.LightningDataModule.on_train_dataloader
-    :noindex:
-
-on_val_dataloader
-=================
-
-.. automethod:: pytorch_lightning.core.datamodule.LightningDataModule.on_val_dataloader
-    :noindex:
-
-on_test_dataloader
-==================
-
-.. automethod:: pytorch_lightning.core.datamodule.LightningDataModule.on_test_dataloader
-    :noindex:
-
-on_predict_dataloader
-=====================
-
-.. automethod:: pytorch_lightning.core.datamodule.LightningDataModule.on_predict_dataloader
-    :noindex:
-
 teardown
 ========
 

pytorch_lightning/core/hooks.py

@@ -628,38 +628,6 @@ def predict_dataloader(self) -> EVAL_DATALOADERS:
             "`predict_dataloader` must be implemented to be used with the Lightning Trainer"
         )
 
-    def on_train_dataloader(self) -> None:
-        """Called before requesting the train dataloader.
-
-        .. deprecated:: v1.5
-            :meth:`on_train_dataloader` is deprecated and will be removed in v1.7.0.
-            Please use :meth:`train_dataloader()` directly.
-        """
-
-    def on_val_dataloader(self) -> None:
-        """Called before requesting the val dataloader.
-
-        .. deprecated:: v1.5
-            :meth:`on_val_dataloader` is deprecated and will be removed in v1.7.0.
-            Please use :meth:`val_dataloader()` directly.
-        """
-
-    def on_test_dataloader(self) -> None:
-        """Called before requesting the test dataloader.
-
-        .. deprecated:: v1.5
-            :meth:`on_test_dataloader` is deprecated and will be removed in v1.7.0.
-            Please use :meth:`test_dataloader()` directly.
-        """
-
-    def on_predict_dataloader(self) -> None:
-        """Called before requesting the predict dataloader.
-
-        .. deprecated:: v1.5
-            :meth:`on_predict_dataloader` is deprecated and will be removed in v1.7.0.
-            Please use :meth:`predict_dataloader()` directly.
-        """
-
     def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any:
         """Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors wrapped in a custom
         data structure.

pytorch_lightning/trainer/configuration_validator.py

@@ -94,16 +94,6 @@ def __verify_train_val_loop_configuration(trainer: "pl.Trainer", model: "pl.Ligh
             " `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined."
         )
 
-    # ----------------------------------------------
-    # verify model does not have on_train_dataloader
-    # ----------------------------------------------
-    has_on_train_dataloader = is_overridden("on_train_dataloader", model)
-    if has_on_train_dataloader:
-        rank_zero_deprecation(
-            "Method `on_train_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-            " Please use `train_dataloader()` directly."
-        )
-
     trainer.overridden_optimizer_step = is_overridden("optimizer_step", model)
     trainer.overridden_optimizer_zero_grad = is_overridden("optimizer_zero_grad", model)
     automatic_optimization = model.automatic_optimization
@@ -129,16 +119,6 @@ def __verify_train_val_loop_configuration(trainer: "pl.Trainer", model: "pl.Ligh
     if has_val_step and not has_val_loader:
         rank_zero_warn("You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.")
 
-    # ----------------------------------------------
-    # verify model does not have on_val_dataloader
-    # ----------------------------------------------
-    has_on_val_dataloader = is_overridden("on_val_dataloader", model)
-    if has_on_val_dataloader:
-        rank_zero_deprecation(
-            "Method `on_val_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-            " Please use `val_dataloader()` directly."
-        )
-
 
 def _check_on_post_move_to_device(model: "pl.LightningModule") -> None:
     r"""
@@ -158,20 +138,9 @@ def __verify_eval_loop_configuration(trainer: "pl.Trainer", model: "pl.Lightning
     loader_name = f"{stage}_dataloader"
     step_name = "validation_step" if stage == "val" else f"{stage}_step"
     trainer_method = "validate" if stage == "val" else stage
-    on_eval_hook = f"on_{loader_name}"
 
     has_loader = getattr(trainer._data_connector, f"_{stage}_dataloader_source").is_defined()
     has_step = is_overridden(step_name, model)
-    has_on_eval_dataloader = is_overridden(on_eval_hook, model)
-
-    # ----------------------------------------------
-    # verify model does not have on_eval_dataloader
-    # ----------------------------------------------
-    if has_on_eval_dataloader:
-        rank_zero_deprecation(
-            f"Method `{on_eval_hook}` is deprecated in v1.5.0 and will"
-            f" be removed in v1.7.0. Please use `{loader_name}()` directly."
-        )
 
     # -----------------------------------
     # verify model has an eval_dataloader

pytorch_lightning/trainer/connectors/data_connector.py

@@ -348,7 +348,7 @@ def _reset_eval_dataloader(
         assert mode.evaluating or mode == RunningStage.PREDICTING
 
         # always get the loaders first so we can count how many there are
-        dataloaders = self._request_dataloader(mode, model=model)
+        dataloaders = self._request_dataloader(mode)
 
         if self.trainer.overfit_batches > 0:
             dataloaders = self._resolve_overfit_batches(dataloaders, mode)
@@ -423,18 +423,14 @@ def _reset_eval_dataloader(
 
         return loader_num_batches, dataloaders
 
-    def _request_dataloader(
-        self, stage: RunningStage, model: Optional["pl.LightningModule"] = None
-    ) -> Union[DataLoader, List[DataLoader]]:
+    def _request_dataloader(self, stage: RunningStage) -> Union[DataLoader, List[DataLoader]]:
         """Requests a dataloader from the given model by calling dataloader hooks corresponding to the given stage.
 
         Returns:
             The requested dataloader
         """
         source = getattr(self, f"_{stage.dataloader_prefix}_dataloader_source")
 
-        hook = f"{stage.dataloader_prefix}_dataloader"
-        self.trainer._call_lightning_module_hook("on_" + hook, pl_module=model)
         with _replace_dataloader_init_method():
             # under this context manager, the arguments passed to `DataLoader.__init__` will be captured and saved as
             # attributes on the instance in case the dataloader needs to be re-instantiated later by Lightning

pytorch_lightning/trainer/connectors/logger_connector/fx_validator.py

@@ -162,11 +162,8 @@ class _LogOptions(TypedDict):
             allowed_on_step=(False,), allowed_on_epoch=(True,), default_on_step=False, default_on_epoch=True
         ),
         "configure_optimizers": None,
-        "on_train_dataloader": None,
         "train_dataloader": None,
-        "on_val_dataloader": None,
         "val_dataloader": None,
-        "on_test_dataloader": None,
         "test_dataloader": None,
         "prepare_data": None,
         "configure_callbacks": None,

pytorch_lightning/trainer/data_loading.py

@@ -59,4 +59,4 @@ def request_dataloader(
         rank_zero_deprecation(
             "`TrainerDataLoadingMixin.request_dataloader` was deprecated in v1.6 and will be removed in v1.8."
         )
-        return self._data_connector._request_dataloader(stage, model)
+        return self._data_connector._request_dataloader(stage)

pytorch_lightning/trainer/trainer.py

@@ -1805,13 +1805,13 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
             model: The ``LightningModule`` if calling this outside of the trainer scope.
         """
         source = self._data_connector._train_dataloader_source
-        pl_module = self.lightning_module or model
+        pl_module = model or self.lightning_module
         has_step = is_overridden("training_step", pl_module)
         enable_training = self.limit_train_batches > 0
         if not (source.is_defined() and has_step and enable_training):
             return
 
-        self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING, model=model)
+        self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING)
 
         if self.overfit_batches > 0:
             self.train_dataloader = self._data_connector._resolve_overfit_batches(

tests/deprecated_api/test_remove_1-7.py

@@ -38,52 +38,6 @@
 from tests.plugins.environments.test_lsf_environment import _make_rankfile
 
 
-def test_v1_7_0_deprecated_on_task_dataloader(tmpdir):
-    class CustomBoringModel(BoringModel):
-        def on_train_dataloader(self):
-            print("on_train_dataloader")
-
-        def on_val_dataloader(self):
-            print("on_val_dataloader")
-
-        def on_test_dataloader(self):
-            print("on_test_dataloader")
-
-        def on_predict_dataloader(self):
-            print("on_predict_dataloader")
-
-    def _run(model, task="fit"):
-        trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
-        getattr(trainer, task)(model)
-
-    model = CustomBoringModel()
-
-    with pytest.deprecated_call(
-        match="Method `on_train_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-    ):
-        _run(model, "fit")
-
-    with pytest.deprecated_call(
-        match="Method `on_val_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-    ):
-        _run(model, "fit")
-
-    with pytest.deprecated_call(
-        match="Method `on_val_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-    ):
-        _run(model, "validate")
-
-    with pytest.deprecated_call(
-        match="Method `on_test_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-    ):
-        _run(model, "test")
-
-    with pytest.deprecated_call(
-        match="Method `on_predict_dataloader` is deprecated in v1.5.0 and will be removed in v1.7.0."
-    ):
-        _run(model, "predict")
-
-
 def test_v1_7_0_on_interrupt(tmpdir):
     class HandleInterruptCallback(Callback):
         def on_keyboard_interrupt(self, trainer, pl_module):