diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index dc0fef9079..92725874df 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -1007,13 +1007,14 @@ class TrainerTrainLoopMixin(ABC):
         # FORWARD (TRAINING STEP + TRAIN STEP END)
         # ---------------------------
         with self.profiler.profile('model_forward'):
+            args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
             if self.amp_backend == AMPType.NATIVE and not self.use_tpu:
                 with torch.cuda.amp.autocast():
-                    training_step_output = self.training_forward(split_batch, batch_idx,
-                                                                 opt_idx, hiddens)
+                    training_step_output = self.accelerator_backend.training_step(args)
+                    training_step_output = self.call_hook('training_step_end', training_step_output)
             else:
-                training_step_output = self.training_forward(split_batch, batch_idx, opt_idx,
-                                                             hiddens)
+                training_step_output = self.accelerator_backend.training_step(args)
+                training_step_output = self.call_hook('training_step_end', training_step_output)
 
             # ----------------------------
             # PROCESS THE RESULT
@@ -1186,26 +1187,6 @@ class TrainerTrainLoopMixin(ABC):
 
         return args
 
-    def training_forward(self, batch, batch_idx, opt_idx, hiddens):
-        """
-        Handle forward for each training case (distributed, single gpu, etc...)
-        :param batch:
-        :param batch_idx:
-        :return:
-        """
-        # ---------------
-        # FORWARD
-        # ---------------
-        args = self.build_train_args(batch, batch_idx, opt_idx, hiddens)
-
-        # distributed forward
-        output = self.accelerator_backend.training_step(args)
-
-        # Training step end
-        output = self.call_hook('training_step_end', output)
-
-        return output
-
     def update_learning_rates(self, interval: str, monitor_metrics=None):
         """Update learning rates.