Fix: Train loop config validation was run during trainer.predict

pytorch_lightning/trainer/configuration_validator.py

@@ -30,7 +30,9 @@ def verify_loop_configurations(self, model: LightningModule):
             model: The model to check the configuration.
 
         """
-        if not self.trainer.testing:
+        if self.trainer.predicting:
+            self.__verify_predict_loop_configuration(model)
+        elif not self.trainer.testing:
             self.__verify_train_loop_configuration(model)
             self.__verify_eval_loop_configuration(model, 'validation')
         else:
@@ -98,3 +100,9 @@ def __verify_eval_loop_configuration(self, model, eval_loop_name):
             rank_zero_warn(f'you passed in a {loader_name} but have no {step_name}. Skipping {eval_loop_name} loop')
         if has_step and not has_loader:
             rank_zero_warn(f'you defined a {step_name} but have no {loader_name}. Skipping {eval_loop_name} loop')
+
+    def __verify_predict_loop_configuration(self, model):
+
+        has_predict_dataloader = is_overridden('predict_dataloader', model)
+        if not has_predict_dataloader:
+            raise MisconfigurationException('Dataloader not found for `Trainer.predict`')

tests/trainer/test_trainer.py

@@ -1850,3 +1850,35 @@ def compare_optimizers():
     trainer.max_epochs = 2  # simulate multiple fit calls
     trainer.fit(model)
     compare_optimizers()
+
+
+def test_trainer_predict_verify_config(tmpdir):
+
+    class TestModel(LightningModule):
+
+        def __init__(self):
+            super().__init__()
+            self.layer = torch.nn.Linear(32, 2)
+
+        def forward(self, x):
+            return self.layer(x)
+
+    dataloaders = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))]
+
+    model = TestModel()
+    datamodule = TestLightningDataModule(dataloaders)
+
+    trainer = Trainer(default_root_dir=tmpdir)
+
+    if datamodule:
+        results = trainer.predict(model, datamodule=datamodule)
+    else:
+        results = trainer.predict(model, dataloaders=dataloaders)
+
+    assert len(results) == 2
+    assert results[0][0].shape == torch.Size([1, 2])
+
+    model.predict_dataloader = None
+
+    with pytest.raises(MisconfigurationException, match="Dataloader not found for `Trainer.predict`"):
+        trainer.predict(model)