diff --git a/paddle/fluid/operators/sampling_id_op.cc b/paddle/fluid/operators/sampling_id_op.cc
index 9729537d1e4d7b4e4614dcd6778f0f0a72310f86..17f6461fcb816b23d100e8d5a61aac484c1a6ba3 100644
--- a/paddle/fluid/operators/sampling_id_op.cc
+++ b/paddle/fluid/operators/sampling_id_op.cc
@@ -25,9 +25,9 @@ class SamplingIdOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of RowConvOp should not be null.");
+                   "Input(X) of SamplingIdOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of RowConvOp should not be null.");
+                   "Output(Out) of SamplingIdOp should not be null.");
 
     auto input_dims = ctx->GetInputDim("X");
 
@@ -43,8 +43,7 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "The input tensor of softmax. "
              "2-D with shape [batch_size, input_feature_dimensions].");
-    AddOutput("Out", "Sliced data tensor.");
-
+    AddOutput("Out", "SamplingId data tensor.");
     AddComment(R"DOC(
 SamplingId Operator.
   @brief A layer for sampling id from multinomial distribution from the
diff --git a/paddle/fluid/operators/sampling_id_op.cu b/paddle/fluid/operators/sampling_id_op.cu
index e467165b6d24cead7926e61a33e4dfb746be01dd..c0bb9c916cc9e4cae193ce20e72431c173b16288 100644
--- a/paddle/fluid/operators/sampling_id_op.cu
+++ b/paddle/fluid/operators/sampling_id_op.cu
@@ -16,19 +16,6 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/operators/sampling_id_op.h"
 
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-class SamplingIdOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext *ctx) const override {}
-}
-}  // namespace operators
-}  // namespace paddle
-
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     sampling_id,
diff --git a/paddle/fluid/operators/sampling_id_op.h b/paddle/fluid/operators/sampling_id_op.h
index 5bb1991fc57250bb31a34d8a978204e9ac1298c0..4d962b4809f5440288feecb46135c2862c3b2523 100644
--- a/paddle/fluid/operators/sampling_id_op.h
+++ b/paddle/fluid/operators/sampling_id_op.h
@@ -50,7 +50,7 @@ class SamplingIdKernel : public framework::OpKernel<T> {
     std::vector<int64_t> out_dim;
     out_dim.push_back(static_cast<int64_t>(batch_size));
 
-    Tensor* output = context.Output<Tensor>("Output");
+    Tensor* output = context.Output<Tensor>("Out");
     output->Resize(framework::make_ddim(out_dim));
     output->mutable_data<T>(context.GetPlace());
     framework::TensorFromVector(ids, context.device_context(), output);
diff --git a/python/paddle/fluid/tests/unittests/test_sampling_id_op.py b/python/paddle/fluid/tests/unittests/test_sampling_id_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d86acfb521dc49ef7fb54c7bcc41a7e2ef0dc2
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_sampling_id_op.py
@@ -0,0 +1,45 @@
+#   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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+
+
+class TestSamplingIdOp(OpTest):
+    def setUp(self):
+        self.op_type = "sampling_id"
+        self.use_mkldnn = False
+        self.init_kernel_type()
+        X = np.random.random((3, 4)).astype('float32')
+        self.inputs = {"X": X}
+        Y = np.random.random(3).astype('float32')
+        self.outputs = {'Out': Y}
+        self.attrs = {'use_mkldnn': self.use_mkldnn}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+    def init_kernel_type(self):
+        pass
+
+
+if __name__ == "__main__":
+    unittest.main()