diff --git a/fastNLP/loader/config_loader.py b/fastNLP/loader/config_loader.py
index 6391ecac..cf3ac1a9 100644
--- a/fastNLP/loader/config_loader.py
+++ b/fastNLP/loader/config_loader.py
@@ -8,9 +8,10 @@ from fastNLP.loader.base_loader import BaseLoader
 class ConfigLoader(BaseLoader):
     """loader for configuration files"""
 
-    def __init__(self, data_path):
+    def __init__(self, data_path=None):
         super(ConfigLoader, self).__init__()
-        self.config = self.parse(super(ConfigLoader, self).load(data_path))
+        if data_path is not None:
+            self.config = self.parse(super(ConfigLoader, self).load(data_path))
 
     @staticmethod
     def parse(string):
diff --git a/test/modules/test_variational_rnn.py b/test/modules/test_variational_rnn.py
index b182fa1a..c3806f60 100644
--- a/test/modules/test_variational_rnn.py
+++ b/test/modules/test_variational_rnn.py
@@ -3,35 +3,23 @@ import unittest
 import numpy as np
 import torch
 
-from fastNLP.modules.encoder.variational_rnn import VarMaskedFastLSTM
+from fastNLP.modules.encoder.variational_rnn import VarLSTM
 
 
 class TestMaskedRnn(unittest.TestCase):
     def test_case_1(self):
-        masked_rnn = VarMaskedFastLSTM(input_size=1, hidden_size=1, bidirectional=True, batch_first=True)
+        masked_rnn = VarLSTM(input_size=1, hidden_size=1, bidirectional=True, batch_first=True)
         x = torch.tensor([[[1.0], [2.0]]])
         print(x.size())
         y = masked_rnn(x)
-        mask = torch.tensor([[[1], [1]]])
-        y = masked_rnn(x, mask=mask)
-        mask = torch.tensor([[[1], [0]]])
-        y = masked_rnn(x, mask=mask)
+
 
     def test_case_2(self):
         input_size = 12
         batch = 16
         hidden = 10
-        masked_rnn = VarMaskedFastLSTM(input_size=input_size, hidden_size=hidden, bidirectional=False, batch_first=True)
-
-        x = torch.randn((batch, input_size))
-        output, _ = masked_rnn.step(x)
-        self.assertEqual(tuple(output.shape), (batch, hidden))
+        masked_rnn = VarLSTM(input_size=input_size, hidden_size=hidden, bidirectional=False, batch_first=True)
 
         xx = torch.randn((batch, 32, input_size))
         y, _ = masked_rnn(xx)
         self.assertEqual(tuple(y.shape), (batch, 32, hidden))
-
-        xx = torch.randn((batch, 32, input_size))
-        mask = torch.from_numpy(np.random.randint(0, 2, size=(batch, 32))).to(xx)
-        y, _ = masked_rnn(xx, mask=mask)
-        self.assertEqual(tuple(y.shape), (batch, 32, hidden))