diff --git a/fastNLP/modules/decoder/crf.py b/fastNLP/modules/decoder/crf.py
index c83ef8be..b5ffa35d 100644
--- a/fastNLP/modules/decoder/crf.py
+++ b/fastNLP/modules/decoder/crf.py
@@ -293,6 +293,7 @@ class ConditionalRandomField(nn.Module):
         batch_size, seq_len, n_tags = logits.size()
         logits = logits.transpose(0, 1).data  # L, B, H
         mask = mask.transpose(0, 1).data.eq(True)  # L, B
+        flip_mask = mask.eq(False)
 
         # dp
         vpath = logits.new_zeros((seq_len, batch_size, n_tags), dtype=torch.long)
@@ -307,12 +308,11 @@ class ConditionalRandomField(nn.Module):
         trans_score = transitions[:n_tags, :n_tags].view(1, n_tags, n_tags).data
         for i in range(1, seq_len):
             prev_score = vscore.view(batch_size, n_tags, 1)
-            cur_score = logits[i].view(batch_size, 1, n_tags)
-            score = prev_score + trans_score + cur_score
+            cur_score = logits[i].view(batch_size, 1, n_tags) + trans_score
+            score = prev_score + cur_score.masked_fill(flip_mask[i].view(batch_size, 1, 1), 0)
             best_score, best_dst = score.max(1)
             vpath[i] = best_dst
-            vscore = best_score.masked_fill(mask[i].eq(False).view(batch_size, 1), 0) + \
-                     vscore.masked_fill(mask[i].view(batch_size, 1), 0)
+            vscore = best_score
 
         if self.include_start_end_trans:
             vscore += transitions[:n_tags, n_tags + 1].view(1, -1)
diff --git a/test/modules/decoder/test_CRF.py b/test/modules/decoder/test_CRF.py
index 94b4ab7a..85173669 100644
--- a/test/modules/decoder/test_CRF.py
+++ b/test/modules/decoder/test_CRF.py
@@ -220,3 +220,22 @@ class TestCRF(unittest.TestCase):
             if _%1000==0:
                 print(loss)
             self.assertGreater(loss.item(), 0, "CRF loss cannot be less than 0.")
+
+    def test_masking(self):
+        # 测试crf的pad masking正常运行
+        import torch
+        from fastNLP.modules.decoder.crf import ConditionalRandomField
+        max_len = 5
+        n_tags = 5
+        pad_len = 5
+
+        torch.manual_seed(4)
+        logit = torch.rand(1, max_len+pad_len, n_tags)
+        # logit[0, -1, :] = 0.0
+        mask = torch.ones(1, max_len+pad_len)
+        mask[0,-pad_len] = 0
+        model = ConditionalRandomField(n_tags)
+        pred, score = model.viterbi_decode(logit[:,:-pad_len], mask[:,:-pad_len])
+        mask_pred, mask_score = model.viterbi_decode(logit, mask)
+        self.assertEqual(pred[0].tolist(), mask_pred[0,:-pad_len].tolist())
+