diff --git a/fastNLP/core/metrics.py b/fastNLP/core/metrics.py
index 6aca0b20..fa46df24 100644
--- a/fastNLP/core/metrics.py
+++ b/fastNLP/core/metrics.py
@@ -201,21 +201,6 @@ class MetricBase(object):
                     f"Parameter `{func_param}` is not in {_get_func_signature(self.evaluate)}. Please check the "
                     f"initialization parameters, or change its signature.")
 
-    def _fast_param_map(self, pred_dict, target_dict):
-        """Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map.
-            such as pred_dict has one element, target_dict has one element
-
-        :param pred_dict:
-        :param target_dict:
-        :return: dict, if dict is not {}, pass it to self.evaluate. Otherwise do mapping.
-        """
-        fast_param = {}
-        if len(self._param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
-            fast_param['pred'] = list(pred_dict.values())[0]
-            fast_param['target'] = list(target_dict.values())[0]
-            return fast_param
-        return fast_param
-
     def __call__(self, pred_dict, target_dict):
         """
         这个方法会调用self.evaluate 方法.
@@ -231,10 +216,6 @@ class MetricBase(object):
         :return:
         """
 
-        fast_param = self._fast_param_map(pred_dict, target_dict)
-        if fast_param:
-            self.evaluate(**fast_param)
-            return
 
         if not self._checked:
             if not callable(self.evaluate):
diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py
index e615ba64..3e2b98be 100644
--- a/fastNLP/embeddings/bert_embedding.py
+++ b/fastNLP/embeddings/bert_embedding.py
@@ -193,7 +193,7 @@ class BertWordPieceEncoder(nn.Module):
         with torch.no_grad():
             sep_mask = word_pieces.eq(self._sep_index)  # batch_size x max_len
             if token_type_ids is None:
-                sep_mask_cumsum = sep_mask.flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1])
+                sep_mask_cumsum = sep_mask.long().flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1])
                 token_type_ids = sep_mask_cumsum.fmod(2)
                 if token_type_ids[0, 0].item():  # 如果开头是奇数，则需要flip一下结果，因为需要保证开头为0
                     token_type_ids = token_type_ids.eq(0).long()