Fix `args.gradient_accumulation_steps` used before assigment.

run_classifier.py

@@ -404,6 +404,10 @@ def main():
                         type=int, 
                         default=42,
                         help="random seed for initialization")
+    parser.add_argument('--gradient_accumulation_steps',
+                        type=int,
+                        default=1,
+                        help="Number of updates steps to accumualte before performing a backward/update pass.")                       
     args = parser.parse_args()
 
     processors = {
@@ -469,7 +473,7 @@ def main():
 
     model = BertForSequenceClassification(bert_config, len(label_list))
     if args.init_checkpoint is not None:
-        model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
+        model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
     model.to(device)
 
     if args.local_rank != -1:

run_squad.py

@@ -739,7 +739,11 @@ def main():
                         type=int, 
                         default=42,
                         help="random seed for initialization")
-
+    parser.add_argument('--gradient_accumulation_steps',
+                        type=int,
+                        default=1,
+                        help="Number of updates steps to accumualte before performing a backward/update pass.")
+    
     args = parser.parse_args()
 
     if args.local_rank == -1 or args.no_cuda: