Fix load disparity between normal and hpc (#4526)

* Add missing load functionality in hpc

* Add general file load for hpc

* Add mark in CHANGELOG

* Fix Typo Li**hg**tning

Co-authored-by: Rohit Gupta <[email protected]>

* Refactor line separation

Co-authored-by: Rohit Gupta <[email protected]>

* Fix entangled fixation commit

* Fix naming of restore_model_states

* Fix amp restore place

Co-authored-by: Rohit Gupta <[email protected]>
Co-authored-by: chaton <[email protected]>

CHANGELOG.md

@@ -47,7 +47,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-
+- Fixed feature-lack in hpc load ([#4526](https://github.com/PyTorchLightning/pytorch-lightning/pull/4526))
 
 ## [1.0.5] - 2020-11-03
 

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -103,6 +103,20 @@ def restore(self, checkpoint_path: str, on_gpu: bool):
         # load model state
         model = self.trainer.get_model()
 
+        # restore model and datamodule state
+        self.restore_model_state(model, checkpoint)
+
+        if on_gpu:
+            model.cuda(self.trainer.root_gpu)
+
+        # restore training state
+        self.restore_training_state(checkpoint)
+
+    def restore_model_state(self, model: LightningModule, checkpoint) -> None:
+        """
+        Restore model states from a 'PyTorch-Lightning checkpoint' dictionary object
+        """
+
         # give the datamodule a chance to load something
         if self.trainer.datamodule is not None:
             self.trainer.datamodule.on_load_checkpoint(checkpoint)
@@ -113,18 +127,6 @@ def restore(self, checkpoint_path: str, on_gpu: bool):
         # restore the state_dict on the model
         model.load_state_dict(checkpoint['state_dict'])
 
-        if on_gpu:
-            model.cuda(self.trainer.root_gpu)
-
-        # restore amp scaling
-        if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
-            self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
-        elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
-            amp.load_state_dict(checkpoint['amp_scaling_state'])
-
-        # load training state (affects trainer only)
-        self.restore_training_state(checkpoint)
-
     def restore_training_state(self, checkpoint):
         """
         Restore trainer state.
@@ -147,6 +149,12 @@ def restore_training_state(self, checkpoint):
                 " where `model.ckpt` is your checkpoint file."
             )
 
+        # restore amp scaling
+        if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
+            self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
+        elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
+            amp.load_state_dict(checkpoint['amp_scaling_state'])
+
         # restore callback states
         self.trainer.on_load_checkpoint(checkpoint)
 
@@ -336,19 +344,13 @@ def hpc_load(self, folderpath, on_gpu):
         filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))
 
         # load on CPU first
-        checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
+        checkpoint = pl_load(filepath, map_location=lambda storage, loc: storage)
 
         # load model state
         model = self.trainer.get_model()
 
-        # load the state_dict on the model automatically
-        model.load_state_dict(checkpoint['state_dict'])
-
-        # restore amp scaling
-        if self.trainer.amp_backend == AMPType.NATIVE and 'native_amp_scaling_state' in checkpoint:
-            self.trainer.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])
-        elif self.trainer.amp_backend == AMPType.APEX and 'amp_scaling_state' in checkpoint:
-            amp.load_state_dict(checkpoint['amp_scaling_state'])
+        # restore states from 'PyTorch-Lightning checkpoint' dictionary object
+        self.restore_model_state(model, checkpoint)
 
         if self.trainer.root_gpu is not None:
             model.cuda(self.trainer.root_gpu)