single variable for single gpu model

train.py

@@ -128,8 +128,13 @@ def validate(model, criterion, valset, iteration, batch_size, n_gpus,
                                 pin_memory=False, collate_fn=collate_fn)
 
         val_loss = 0.0
+        if distributed_run or torch.cuda.device_count() > 1:
+            batch_parser = model.module.parse_batch
+        else:
+            batch_parser = model.parse_batch
+            
         for i, batch in enumerate(val_loader):
-            x, y = model.parse_batch(batch)
+            x, y = batch_parser(batch)
             y_pred = model(x)
             loss = criterion(y_pred, y)
             reduced_val_loss = reduce_tensor(loss.data, n_gpus)[0] \
@@ -157,6 +162,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
     if hparams.distributed_run:
         init_distributed(hparams, n_gpus, rank, group_name)
 
+
     torch.manual_seed(hparams.seed)
     torch.cuda.manual_seed(hparams.seed)
 
@@ -188,6 +194,10 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
             epoch_offset = max(0, int(iteration / len(train_loader)))
 
     model.train()
+    if distributed_run or torch.cuda.device_count() > 1:
+        batch_parser = model.module.parse_batch
+    else:
+        batch_parser = model.parse_batch
     # ================ MAIN TRAINNIG LOOP! ===================
     for epoch in range(epoch_offset, hparams.epochs):
         print("Epoch: {}".format(epoch))
@@ -197,7 +207,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
                 param_group['lr'] = learning_rate
 
             model.zero_grad()
-            x, y = model.parse_batch(batch)
+            x, y = batch_parser(batch)
             y_pred = model(x)
             loss = criterion(y_pred, y)
             reduced_loss = reduce_tensor(loss.data, n_gpus)[0] \