fixed hpc save, load. cleaned apu

pytorch_lightning/root_module/model_saving.py

@@ -7,7 +7,7 @@ from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistr
 
 class ModelIO(object):
 
-    def load_model_specific(self, checkpoint):
+    def on_load_checkpoint(self, checkpoint):
         """
         Do something with the checkpoint
         Gives model a chance to load something before state_dict is restored
@@ -16,25 +16,24 @@ class ModelIO(object):
         """
         pass
 
-    def get_save_dict(self):
+    def on_save_checkpoint(self, checkpoint):
         """
-        Return specific things for the model
-        Called before trainer requests the state_dict
-        :return:
+        Give the model a chance to add something to the checkpoint.
+        state_dict is already there
         """
         pass
 
     # -------------------------
     # OPTIONAL HOOKS
     # -------------------------
-    def on_hpc_save(self):
+    def on_hpc_save(self, checkpoint):
         """
         Hook to do whatever you need right before Slurm manager saves the model
         :return:
         """
         pass
 
-    def on_hpc_load(self):
+    def on_hpc_load(self, checkpoint):
         """
         Hook to do whatever you need right before Slurm manager loads the model
         :return:
@@ -78,13 +77,13 @@ class TrainerIO(object):
 
         checkpoint['optimizer_states'] = optimizer_states
 
-        # request what to save from the model
-        model = self.__get_model()
-        checkpoint_dict = model.get_save_dict()
-        checkpoint.update(checkpoint_dict)
-
         # add the state_dict from the model
-        checkpoint['state_dict'] = checkpoint_dict
+        model = self.__get_model()
+        checkpoint['state_dict'] = model.get_state_dict
+
+        # give the model a chance to add a few things
+        model.on_save_checkpoint(checkpoint)
+
         return checkpoint
 
     # --------------------
@@ -153,13 +152,12 @@ class TrainerIO(object):
 
         # give model a chance to do something on hpc_save
         model = self.__get_model()
-        model.on_hpc_save()
+        checkpoint = self.dump_checkpoint()
 
-        # request what to save from the model
-        checkpoint_dict = self.dump_checkpoint()
+        model.on_hpc_save(checkpoint)
 
         # do the actual save
-        torch.save(checkpoint_dict, filepath)
+        torch.save(checkpoint, filepath)
 
         return filepath
 
@@ -177,14 +175,11 @@ class TrainerIO(object):
         # load model state
         model = self.__get_model()
 
-        # give model a chance to load something
-        model.load_model_specific(checkpoint)
-
         # load the state_dict on the model automatically
         model.load_state_dict(checkpoint['state_dict'])
 
         # call model hook
-        model.on_hpc_load()
+        model.on_hpc_load(checkpoint)
 
 
     def max_ckpt_in_folder(self, path):