Add CheckpointManager to keep avg checkpoint weights in memory to red…

…uce disk read when averaging + various checkpoint refactoring

Summary: Pull Request resolved: pytorch/translate#315

Reviewed By: akinh

Differential Revision: D13510446

fbshipit-source-id: 22a6594af9253130a93e638285a47183a974e0de

fairseq/trainer.py

@@ -119,7 +119,7 @@ def save_checkpoint(self, filename, extra_state):
         if distributed_utils.is_master(self.args):  # only save one checkpoint
             extra_state['train_meters'] = self.meters
             utils.save_state(
-                filename, self.args, self.get_model(), self.criterion, self.optimizer,
+                filename, self.args, self.get_model().state_dict(), self.criterion, self.optimizer,
                 self.lr_scheduler, self._num_updates, self._optim_history, extra_state,
             )
 

fairseq/utils.py

@@ -39,15 +39,15 @@ def convert_state_dict_type(state_dict, ttype=torch.FloatTensor):
         return state_dict
 
 
-def save_state(filename, args, model, criterion, optimizer, lr_scheduler,
+def save_state(filename, args, model_state_dict, criterion, optimizer, lr_scheduler,
                num_updates, optim_history=None, extra_state=None):
     if optim_history is None:
         optim_history = []
     if extra_state is None:
         extra_state = {}
     state_dict = {
         'args': args,
-        'model': model.state_dict() if model else {},
+        'model': model_state_dict if model_state_dict else {},
         'optimizer_history': optim_history + [
             {
                 'criterion_name': criterion.__class__.__name__,