Do not mark LightningModule methods as abstract (#12381)

* do not mark LightningModule methods as abstract

* add concrete test

pytorch_lightning/core/hooks.py

@@ -19,6 +19,7 @@ import torch
 from torch.optim.optimizer import Optimizer
 
 from pytorch_lightning.utilities import move_data_to_device
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.types import EVAL_DATALOADERS, STEP_OUTPUT, TRAIN_DATALOADERS
 
 
@@ -490,7 +491,7 @@ class DataHooks:
                 # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
                 return {'mnist': mnist_loader, 'cifar': cifar_loader}
         """
-        raise NotImplementedError("`train_dataloader` must be implemented to be used with the Lightning Trainer")
+        raise MisconfigurationException("`train_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def test_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -544,7 +545,7 @@ class DataHooks:
             In the case where you return multiple test dataloaders, the :meth:`test_step`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
-        raise NotImplementedError("`test_dataloader` must be implemented to be used with the Lightning Trainer")
+        raise MisconfigurationException("`test_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def val_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -595,7 +596,7 @@ class DataHooks:
             In the case where you return multiple validation dataloaders, the :meth:`validation_step`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
-        raise NotImplementedError("`val_dataloader` must be implemented to be used with the Lightning Trainer")
+        raise MisconfigurationException("`val_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def predict_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -618,7 +619,9 @@ class DataHooks:
             In the case where you return multiple prediction dataloaders, the :meth:`predict_step`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
-        raise NotImplementedError("`predict_dataloader` must be implemented to be used with the Lightning Trainer")
+        raise MisconfigurationException(
+            "`predict_dataloader` must be implemented to be used with the Lightning Trainer"
+        )
 
     def on_train_dataloader(self) -> None:
         """Called before requesting the train dataloader.

tests/core/test_datamodules.py

@@ -377,9 +377,9 @@ def test_dm_init_from_datasets_dataloaders(iterable):
     with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
         dm.train_dataloader()
         dl_mock.assert_called_once_with(train_ds, batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True)
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(MisconfigurationException):
         _ = dm.val_dataloader()
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(MisconfigurationException):
         _ = dm.test_dataloader()
 
     train_ds_sequence = [ds(), ds()]
@@ -392,9 +392,9 @@ def test_dm_init_from_datasets_dataloaders(iterable):
                 call(train_ds_sequence[1], batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True),
             ]
         )
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(MisconfigurationException):
         _ = dm.val_dataloader()
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(MisconfigurationException):
         _ = dm.test_dataloader()
 
     valid_ds = ds()
@@ -405,7 +405,7 @@ def test_dm_init_from_datasets_dataloaders(iterable):
         dl_mock.assert_called_with(valid_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
         dm.test_dataloader()
         dl_mock.assert_called_with(test_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(MisconfigurationException):
         _ = dm.train_dataloader()
 
     valid_dss = [ds(), ds()]

tests/core/test_lightning_module.py

@@ -18,7 +18,7 @@ import torch
 from torch import nn
 from torch.optim import Adam, SGD
 
-from pytorch_lightning import Trainer
+from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11
@@ -26,6 +26,11 @@ from tests.helpers import BoringModel
 from tests.helpers.runif import RunIf
 
 
+def test_lightning_module_not_abstract():
+    """Test that the LightningModule can be instantiated (it is not an abstract class)."""
+    _ = LightningModule()
+
+
 def test_property_current_epoch():
     """Test that the current_epoch in LightningModule is accessible via the Trainer."""
     model = BoringModel()