diff --git a/CHANGELOG.md b/CHANGELOG.md
index 81846809fbf85..c917ee94f8c5f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -197,6 +197,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed torch distributed not available in setup hook for DDP ([#6506](https://github.com/PyTorchLightning/pytorch-lightning/pull/6506))
 
 
+- Fixed an issue with `IterableDataset` when `__len__` is not defined ([#6828](https://github.com/PyTorchLightning/pytorch-lightning/pull/6828))
+
+
 ## [1.2.6] - 2021-03-30
 
 ### Changed
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index d565f0906e59e..27dcd6fe9aa0d 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -866,7 +866,7 @@ def validate(
         self.validating = True
 
         # If you supply a datamodule you can't supply val_dataloaders
-        if val_dataloaders and datamodule:
+        if val_dataloaders is not None and datamodule:
             raise MisconfigurationException(
                 'You cannot pass both `trainer.validate(val_dataloaders=..., datamodule=...)`'
             )
@@ -928,7 +928,7 @@ def test(
         self.testing = True
 
         # If you supply a datamodule you can't supply test_dataloaders
-        if test_dataloaders and datamodule:
+        if test_dataloaders is not None and datamodule:
             raise MisconfigurationException('You cannot pass both `trainer.test(test_dataloaders=..., datamodule=...)`')
 
         model_provided = model is not None
@@ -1024,7 +1024,7 @@ def predict(
         self.state = TrainerState.PREDICTING
         self.predicting = True
 
-        if dataloaders and datamodule:
+        if dataloaders is not None and datamodule:
             raise MisconfigurationException(
                 'You cannot pass dataloaders to trainer.predict if you supply a datamodule.'
             )
diff --git a/tests/trainer/test_dataloaders.py b/tests/trainer/test_dataloaders.py
index 505af173b7910..7f9cf6210ce7c 100644
--- a/tests/trainer/test_dataloaders.py
+++ b/tests/trainer/test_dataloaders.py
@@ -636,28 +636,42 @@ def test_warning_with_few_workers_multi_loader(_, tmpdir, ckpt_path, stage):
 
 def test_warning_with_iterable_dataset_and_len(tmpdir):
     """ Tests that a warning message is shown when an IterableDataset defines `__len__`. """
-    model = EvalModelTemplate()
+    model = BoringModel()
     original_dataset = model.train_dataloader().dataset
 
-    class IterableWithLen(IterableDataset):
+    class IterableWithoutLen(IterableDataset):
 
         def __iter__(self):
             return iter(original_dataset)
 
+    class IterableWithLen(IterableWithoutLen):
+
         def __len__(self):
             return len(original_dataset)
 
+    # with __len__ defined
     dataloader = DataLoader(IterableWithLen(), batch_size=16)
     assert has_len(dataloader)
     assert has_iterable_dataset(dataloader)
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_steps=3,
-    )
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=3)
+    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
+        trainer.validate(model, val_dataloaders=[dataloader])
     with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
         trainer.fit(model, train_dataloader=dataloader, val_dataloaders=[dataloader])
     with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
         trainer.test(model, test_dataloaders=[dataloader])
+    with pytest.warns(UserWarning, match='Your `IterableDataset` has `__len__` defined.'):
+        trainer.predict(model, dataloaders=[dataloader])
+
+    # without __len__ defined
+    dataloader = DataLoader(IterableWithoutLen(), batch_size=16)
+    assert not has_len(dataloader)
+    assert has_iterable_dataset(dataloader)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=3)
+    trainer.validate(model, val_dataloaders=dataloader)
+    trainer.fit(model, train_dataloader=dataloader, val_dataloaders=[dataloader])
+    trainer.test(model, test_dataloaders=dataloader)
+    trainer.predict(model, dataloaders=dataloader)
 
 
 @RunIf(min_gpus=2)