ref: added hook base method (#3127)

* added hook base method

* added hook base method

pytorch_lightning/core/lightning.py

@@ -272,12 +272,6 @@ class LightningModule(ABC, DeviceDtypeModuleMixin, GradInformation, ModelIO, Mod
         """
         rank_zero_warn('`training_step` must be implemented to be used with the Lightning Trainer')
 
-    def training_end(self, *args, **kwargs):
-        """
-        Warnings:
-            Deprecated in v0.7.0. Use  :meth:`training_step_end` instead.
-        """
-
     def training_step_end(self, *args, **kwargs):
         """
         Use this when training with dp or ddp2 because :meth:`training_step`

pytorch_lightning/trainer/trainer.py

@@ -1456,6 +1456,16 @@ class Trainer(
         self.amp_backend = None
         self._setup_amp_backend(amp_type)
 
+    def call_hook(self, hook_name, *args, **kwargs):
+        output = None
+        if self.is_overridden(hook_name):
+            model_ref = self.get_model()
+            with self.profiler.profile(hook_name):
+                hook_fx = getattr(model_ref, hook_name)
+                output = hook_fx(*args, **kwargs)
+
+        return output
+
 
 class _PatchDataLoader(object):
     r"""

pytorch_lightning/trainer/training_loop.py

@@ -323,6 +323,10 @@ class TrainerTrainLoopMixin(ABC):
     def reset_val_dataloader(self, model):
         """Warning: this is just empty shell for code implemented in other class."""
 
+    @abstractmethod
+    def call_hook(self, hook_name, *args, **kwargs):
+        """Warning: this is just empty shell for code implemented in other class."""
+
     @abstractmethod
     def has_arg(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
@@ -1202,24 +1206,10 @@ class TrainerTrainLoopMixin(ABC):
         # allow any mode to define training_step_end
         # do something will all the dp outputs (like softmax)
         if self.is_overridden('training_step_end'):
-            model_ref = self.get_model()
-            with self.profiler.profile('training_step_end'):
-                # TODO: modify when using result obj
-                output = model_ref.training_step_end(output)
-
+            output = self.call_hook('training_step_end', output)
         elif is_result_obj and (self.use_dp or self.use_ddp2):
             output.dp_reduce()
 
-        # allow any mode to define training_end
-        # TODO: remove in 1.0.0
-        if self.is_overridden('training_end'):
-            model_ref = self.get_model()
-            with self.profiler.profile('training_end'):
-                output = model_ref.training_end(output)
-
-            rank_zero_warn('`training_end` was deprecated in 0.7.0 and will be removed 1.0.0.'
-                           ' Use training_epoch_end instead', DeprecationWarning)
-
         return output
 
     def update_learning_rates(self, interval: str, monitor_metrics=None):