fix reloading model for evaluation in examples

examples/run_classifier.py

@@ -558,6 +558,9 @@ def main():
     # Load a trained model that you have fine-tuned
     model_state_dict = torch.load(output_model_file)
     model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    if args.fp16:
+        model.half()
+    model.to(device)
 
     if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = processor.get_dev_examples(args.data_dir)

examples/run_squad.py

@@ -923,6 +923,9 @@ def main():
     # Load a trained model that you have fine-tuned
     model_state_dict = torch.load(output_model_file)
     model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    if args.fp16:
+        model.half()
+    model.to(device)
 
     if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = read_squad_examples(

examples/run_swag.py

@@ -366,8 +366,7 @@ def main():
     # Prepare model
     model = BertForMultipleChoice.from_pretrained(args.bert_model,
         cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
-        num_choices = 4
-    )
+        num_choices=4)
     if args.fp16:
         model.half()
     model.to(device)
@@ -452,6 +451,9 @@ def main():
                     loss = loss * args.loss_scale
                 if args.gradient_accumulation_steps > 1:
                     loss = loss / args.gradient_accumulation_steps
+                tr_loss += loss.item()
+                nb_tr_examples += input_ids.size(0)
+                nb_tr_steps += 1
 
                 if args.fp16:
                     optimizer.backward(loss)
@@ -466,6 +468,20 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
+    # Save a trained model
+    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+    model_state_dict = torch.load(output_model_file)
+    model = BertForMultipleChoice.from_pretrained(args.bert_model,
+        state_dict=model_state_dict,
+        num_choices=4)
+    if args.fp16:
+        model.half()
+    model.to(device)
+
     if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
         eval_features = convert_examples_to_features(