Fix enforce_datamodule_dataloader_override() for iterable datasets

This function has the if statement `if (train_dataloader or val_dataloaders) and datamodule:`.


The issue is similar to that in Lightning-AI#1560. The problem is that the `if(dl)` translates to `if(bool(dl))`, but there's no dataloader.__bool__ so bool() uses dataloader.__len__ > 0. But... dataloader.__len__ uses IterableDataset.__len__ for IterableDatasets for which __len__ is undefined.

The fix is also the same, the `if dl` should be replaced by `if dl is not None`.

Author: SiddhantRanade

pytorch_lightning/trainer/configuration_validator.py

@@ -10,7 +10,7 @@ def __init__(self, trainer):
 
     def enforce_datamodule_dataloader_override(self, train_dataloader, val_dataloaders, datamodule):
         # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
-        if (train_dataloader or val_dataloaders) and datamodule:
+        if (train_dataloader is not None or val_dataloaders is not None) and datamodule is not None:
             raise MisconfigurationException(
                 'You cannot pass train_dataloader or val_dataloaders to trainer.fit if you supply a datamodule'
             )