Add warning to trainstep output

pytorch_lightning/trainer/training_loop.py

@@ -16,7 +16,7 @@
 from contextlib import contextmanager, suppress
 from copy import copy
 from functools import partial, update_wrapper
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -269,6 +269,15 @@ def _check_training_step_output(self, training_step_output):
             if training_step_output.grad_fn is None:
                 # TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
                 raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor")
+        elif self.trainer.automatic_optimization:
+            if not (
+                isinstance(training_step_output, torch.Tensor) or
+                (isinstance(training_step_output, Mapping) and 'loss' in training_step_output)
+            ):
+                raise MisconfigurationException(
+                    "In automatic optimization, `training_step` must either return a Tensor, "
+                    "a dict with key 'loss' or None (where the step will be skipped)."
+                )
 
     def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
         # give the PL module a result for logging

tests/trainer/loops/test_training_loop.py

@@ -15,6 +15,7 @@
 import torch
 
 from pytorch_lightning import seed_everything, Trainer
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 
 
@@ -142,3 +143,22 @@ def validation_step(self, *args):
     assert trainer.current_epoch == 0
     assert trainer.global_step == 5
     assert model.validation_called_at == (0, 4)
+
+
+@pytest.mark.parametrize(['output'], [(5., ), ({'a': 5}, )])
+def test_warning_invalid_trainstep_output(tmpdir, output):
+
+    class TestModel(BoringModel):
+
+        def training_step(self, batch, batch_idx):
+            return output
+
+    model = TestModel()
+
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)
+    with pytest.raises(
+        MisconfigurationException,
+        match="In automatic optimization, `training_step` must either return a Tensor, "
+        "a dict with key 'loss' or None (where the step will be skipped)"
+    ):
+        trainer.fit(model)