diff --git a/fastNLP/modules/encoder/lstm.py b/fastNLP/modules/encoder/lstm.py
index 2966426a..1cc0dec1 100644
--- a/fastNLP/modules/encoder/lstm.py
+++ b/fastNLP/modules/encoder/lstm.py
@@ -19,7 +19,7 @@ class LSTM(nn.Module):
     别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
 
     LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
-        为1; 且可以应对DataParallel中LSTM的使用问题
+        为1; 且可以应对DataParallel中LSTM的使用问题。
 
     :param input_size:  输入 `x` 的特征维度
     :param hidden_size: 隐状态 `h` 的特征维度.
@@ -32,13 +32,12 @@ class LSTM(nn.Module):
     """
     
     def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True,
-                 bidirectional=False, bias=True, initial_method=None):
+                 bidirectional=False, bias=True):
         super(LSTM, self).__init__()
         self.batch_first = batch_first
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first,
                             dropout=dropout, bidirectional=bidirectional)
         self.init_param()
-        initial_parameter(self, initial_method)
 
     def init_param(self):
         for name, param in self.named_parameters():
@@ -81,9 +80,14 @@ class LSTM(nn.Module):
             else:
                 output = output[:, unsort_idx]
             #  解决LSTM无法在DataParallel下使用的问题问题https://github.com/pytorch/pytorch/issues/1591
-            if output.size(1) < max_len:
-                dummy_tensor = output.new_zeros(batch_size, max_len - output.size(1), output.size(-1))
-                output = torch.cat([output, dummy_tensor], 1)
+            if self.batch_first:
+                if output.size(1) < max_len:
+                    dummy_tensor = output.new_zeros(max_len - output.size(1), batch_size, output.size(-1))
+                    output = torch.cat([output, dummy_tensor], 0)
+            else:
+                if output.size(0) < max_len:
+                    dummy_tensor = output.new_zeros(batch_size, max_len - output.size(1), output.size(-1))
+                    output = torch.cat([output, dummy_tensor], 1)
         else:
             output, hx = self.lstm(x, hx)
         return output, hx