add warning when getting checking len

pytorch_lightning/trainer/data_loading.py

@@ -41,19 +41,33 @@
     HOROVOD_AVAILABLE = True
 
 
+def _has_iterable_dataset(dataloader: DataLoader):
+    return ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset') \
+        and isinstance(dataloader.dataset, IterableDataset)
+
+
 def _has_len(dataloader: DataLoader) -> bool:
     """ Checks if a given Dataloader has __len__ method implemented i.e. if
-    it is a finite dataloader or infinite dataloader """
+    it is a finite dataloader or infinite dataloader. """
+
     try:
         # try getting the length
         if len(dataloader) == 0:
             raise ValueError('`Dataloader` returned 0 length.'
                              ' Please make sure that your Dataloader at least returns 1 batch')
-        return True
+        has_len = True
     except TypeError:
-        return False
+        has_len = False
     except NotImplementedError:  # e.g. raised by torchtext if a batch_size_fn is used
-        return False
+        has_len = False
+
+    if has_len and _has_iterable_dataset(dataloader):
+        rank_zero_warn(
+            'Your `IterableDataset` has `__len__` defined.'
+            ' In combination with multi-processing data loading (e.g. batch size > 1),'
+            ' this can lead to unintended side effects since the samples will be duplicated.'
+        )
+    return has_len
 
 
 class TrainerDataLoadingMixin(ABC):
@@ -131,9 +145,7 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
         # don't manipulate iterable datasets
         is_dataloader = isinstance(dataloader, DataLoader)
 
-        is_iterable_ds = False
-        if ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset'):
-            is_iterable_ds = isinstance(dataloader.dataset, IterableDataset)
+        is_iterable_ds = _has_iterable_dataset(dataloader)
 
         if not is_dataloader or is_iterable_ds:
             return dataloader