updating run_classif

examples/run_classifier.py

@@ -50,15 +50,6 @@ else:
 logger = logging.getLogger(__name__)
 
 
-def average_distributed_scalar(scalar, args):
-    """ Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation. """
-    if args.local_rank == -1:
-        return scalar
-    scalar_t = torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size()
-    torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
-    return scalar_t.item()
-
-
 def main():
     parser = argparse.ArgumentParser()
 
@@ -368,7 +359,7 @@ def main():
         model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
         tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
     else:
-        model = BertForQuestionAnswering.from_pretrained(args.bert_model)
+        model = BertForSequenceClassification.from_pretrained(args.bert_model)
 
     model.to(device)
 
@@ -453,10 +444,6 @@ def main():
             preds = np.squeeze(preds)
         result = compute_metrics(task_name, preds, out_label_ids)
 
-        if args.local_rank != -1:
-            # Average over distributed nodes if needed
-            result = {key: average_distributed_scalar(value, args) for key, value in result.items()}
-
         loss = tr_loss/global_step if args.do_train else None
 
         result['eval_loss'] = eval_loss
@@ -530,10 +517,6 @@ def main():
             preds = np.argmax(preds, axis=1)
             result = compute_metrics(task_name, preds, out_label_ids)
 
-            if args.local_rank != -1:
-                # Average over distributed nodes if needed
-                result = {key: average_distributed_scalar(value, args) for key, value in result.items()}
-
             loss = tr_loss/global_step if args.do_train else None
 
             result['eval_loss'] = eval_loss