diff --git a/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..2741bf167b7c0de7e26b22f7cbe6c49e6eca9577
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+++ b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py
@@ -0,0 +1,154 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle.fluid.core as core
+from op_test import OpTest
+
+
+def lstm_naive(
+        input,
+        w, ):
+    seq_len, batch_size, hidden_size = input.shape
+
+    offset = 0
+    wi = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    wf = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    wc = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    wo = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    ri = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    rf = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    rc = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+    ro = w[offset:offset + hidden_size * hidden_size].reshape(
+        (hidden_size, hidden_size)).transpose()
+    offset += hidden_size * hidden_size
+
+    bi_1 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bf_1 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bc_1 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bo_1 = w[offset:offset + hidden_size]
+    offset += hidden_size
+
+    bi_2 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bf_2 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bc_2 = w[offset:offset + hidden_size]
+    offset += hidden_size
+    bo_2 = w[offset:offset + hidden_size]
+
+    def sigmoid(x):
+        return 1.0 / (1.0 + np.exp(-x))
+
+    def tanh(x):
+        return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))
+
+    output = []
+    pre_h = np.zeros((batch_size, hidden_size), dtype=input.dtype)
+    pre_c = np.zeros((batch_size, hidden_size), dtype=input.dtype)
+
+    for i in range(seq_len):
+        emb_1 = input[i]
+
+        input_gate = sigmoid(
+            np.matmul(emb_1, wi) + np.matmul(pre_h, ri) + bi_1 + bi_2)
+        forget_gate = sigmoid(
+            np.matmul(emb_1, wf) + np.matmul(pre_h, rf) + bf_1 + bf_2)
+        output_gate = sigmoid(
+            np.matmul(emb_1, wo) + np.matmul(pre_h, ro) + bo_1 + bo_2)
+        c_t_temp = tanh(
+            np.matmul(emb_1, wc) + np.matmul(pre_h, rc) + bc_1 + bc_2)
+        new_c = input_gate * c_t_temp + forget_gate * pre_c
+        new_h = output_gate * tanh(new_c)
+
+        pre_h = new_h
+        pre_c = new_c
+
+        output.append(new_h)
+
+    output = np.concatenate(output, -1)
+    output = output.reshape((batch_size, -1, hidden_size))
+
+    output = output.transpose((1, 0, 2))
+
+    return output
+
+
+class TestCUDNNLstmOp(OpTest):
+    def setUp(self):
+        self.op_type = "cudnn_lstm"
+        self.dtype = np.float32
+
+        num_steps = 50
+        batch_size = 20
+        hidden_size = 200
+
+        input_weight_size = (hidden_size * hidden_size) * 4
+        hidden_weight_size = (hidden_size * hidden_size) * 4
+        weight_size = input_weight_size + hidden_weight_size
+        weight_size += hidden_size * 8
+
+        input = np.random.random(
+            (num_steps, batch_size, hidden_size)).astype(self.dtype)
+        flat_w = np.random.random((weight_size)).astype(self.dtype)
+
+        output = lstm_naive(input, flat_w)
+
+        init_h = np.zeros((batch_size, hidden_size), dtype=np.float32)
+        init_c = np.zeros((batch_size, hidden_size), dtype=np.float32)
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'W': OpTest.np_dtype_to_fluid_dtype(flat_w),
+            'InitH': OpTest.np_dtype_to_fluid_dtype(init_h),
+            'InitC': OpTest.np_dtype_to_fluid_dtype(init_c),
+        }
+        self.attrs = {
+            'max_len': num_steps,
+            'dropout_prob': 0.0,
+            'is_bidirec': False,
+            'input_size': hidden_size,
+            'hidden_size': hidden_size,
+            'num_layers': 1,
+        }
+        self.outputs = {'Out': output}
+
+    def test_grad_with_place(self):
+        place = core.CUDAPlace(0)
+        self.check_grad_with_place(place, atol=1e-5)
+
+    def test_output_with_place(self):
+        place = core.CUDAPlace(0)
+        self.check_output_with_place(
+            place, atol=1e-5, no_check_set=['last_h', 'last_c'])