fix retruning returns (#1431)

* returns

* changelog

CHANGELOG.md

@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed default `DistributedSampler` for DDP training ([#1425](https://github.com/PyTorchLightning/pytorch-lightning/pull/1425))
 - Fixed workers warning not on windows ([#1430](https://github.com/PyTorchLightning/pytorch-lightning/pull/1430))
+- Fixed returning tuple from `run_training_batch` ([#1431](https://github.com/PyTorchLightning/pytorch-lightning/pull/1431))
 
 ## [0.7.2] - 2020-04-07
 

pytorch_lightning/trainer/training_io.py

@@ -322,7 +322,7 @@ class TrainerIOMixin(ABC):
             checkpoint['hparams_type'] = 'namespace' if is_namespace else 'dict'
         else:
             rank_zero_warn(
-                "Did not find hyperparameters at model.hparams. Saving checkpoint without hyperparameters."
+                "Did not find hyperparameters at model hparams. Saving checkpoint without hyperparameters."
             )
 
         # give the model a chance to add a few things

pytorch_lightning/trainer/training_loop.py

@@ -532,7 +532,7 @@ class TrainerTrainLoopMixin(ABC):
         all_log_metrics = []
 
         if batch is None:
-            return 0, grad_norm_dic, {}
+            return 0, grad_norm_dic, {}, {}
 
         # Batch start events
         with self.profiler.profile('on_batch_start'):
@@ -542,7 +542,7 @@ class TrainerTrainLoopMixin(ABC):
             if self.is_function_implemented('on_batch_start'):
                 response = self.get_model().on_batch_start(batch)
                 if response == -1:
-                    return -1, grad_norm_dic, {}
+                    return -1, grad_norm_dic, {}, {}
 
         splits = [batch]
         if self.truncated_bptt_steps is not None: