diff --git a/paddle/operators/multiplex_op.cc b/paddle/operators/multiplex_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..6e77b86b5698a263b850a973cd1b8644a0aa2201
--- /dev/null
+++ b/paddle/operators/multiplex_op.cc
@@ -0,0 +1,113 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#include "paddle/operators/multiplex_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+class MultiplexOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
+                   "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) shouldn't be null.");
+    auto ins = ctx.MultiInput<Tensor>("X");
+    auto *out = ctx.Output<LoDTensor>("Out");
+    auto num_ins = ins.size();
+    PADDLE_ENFORCE(num_ins > 2,
+                   "multiplex operator should have more than 2 inputs.");
+    PADDLE_ENFORCE_EQ(ins[0]->dims().size(), 1,
+                      "The first input must be a index vector.");
+    auto in_dim = ins[1]->dims();
+
+    for (size_t i = 2; i < num_ins; i++) {
+      auto dim = ins[i]->dims();
+      PADDLE_ENFORCE(
+          in_dim == dim,
+          "All the input tensors except the first one must have the same size");
+    }
+    out->Resize(in_dim);
+  }
+};
+
+class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MultiplexOpMaker(framework::OpProto *proto,
+                   framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input tensors of multiplex operator.").AsDuplicable();
+    AddOutput("Out", "The output tensor of multiplex operator.");
+    AddComment(R"DOC(Multiplex operator
+
+Multiplex multiple tensors according to the index provided by the first
+input tensor.
+
+ins[0]: the index tensor.
+ins[1:N]: the candidate output tensors.
+For each index i from 0 to batchSize - 1, the output is the i-th row of the
+the (index[i] + 1)-th tensor.
+
+For i-th row of the output tensor:
+
+y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
+
+where y is the output tensor. `x_{k}` is the k-th input tensor
+and `k = x{0}[i] + 1`.
+
+)DOC");
+  }
+};
+
+class MultiplexGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
+                   "Input(X) should not be null");
+    PADDLE_ENFORCE(!ctx.MultiOutputVar(framework::GradVarName("X")).empty(),
+                   "Output(X@Grad) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) shouldn't be null.");
+    auto d_ins = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));
+    auto ins = ctx.MultiInput<Tensor>("X");
+    // don't compute gradient for index (ins[0])
+    for (size_t i = 1; i < ins.size(); i++) {
+      if (d_ins[i]) {
+        d_ins[i]->Resize(ins[i]->dims());
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker, multiplex_grad,
+            ops::MultiplexGradOp);
+REGISTER_OP_CPU_KERNEL(
+    multiplex, ops::MultiplexCPUKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    multiplex_grad,
+    ops::MultiplexGradCPUKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/multiplex_op.cu b/paddle/operators/multiplex_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..4736f15bd594178168e3bcf799142d0fc18bff13
--- /dev/null
+++ b/paddle/operators/multiplex_op.cu
@@ -0,0 +1,95 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/multiplex_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class MultiplexGPUKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto rows = ins[1]->dims()[0];
+    auto cols = ins[1]->dims()[1];
+    // copy index to cpu
+    framework::Tensor index_t_cpu;
+    index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
+    auto* index = index_t_cpu.data<T>();
+    auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
+                      ctx.device_context())
+                      .stream();
+    Place place = boost::get<Place>(ctx.GetPlace());
+    for (auto i = 0; i < rows; i++) {
+      int k = (int)index[i] + 1;
+      PADDLE_ENFORCE_LT(k, ins.size(),
+                        "index exceeds the number of candidate tensors.");
+      memory::Copy(place, out->data<T>() + i * cols, place,
+                   ins[k]->data<T>() + i * cols, cols * sizeof(T), stream);
+    }
+  }
+};
+
+template <typename Place, typename T>
+class MultiplexGradGPUKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto d_ins =
+        ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+    for (size_t i = 1; i < d_ins.size(); i++) {
+      if (d_ins[i]) {
+        d_ins[i]->mutable_data<T>(ctx.GetPlace());
+        auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
+        t.device(ctx.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+      }
+    }
+
+    auto rows = ins[1]->dims()[0];
+    auto cols = ins[1]->dims()[1];
+    // copy index to cpu
+    framework::Tensor index_t_cpu;
+    index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
+    auto* index = index_t_cpu.data<T>();
+
+    auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
+                      ctx.device_context())
+                      .stream();
+    Place place = boost::get<Place>(ctx.GetPlace());
+    for (auto i = 0; i < rows; i++) {
+      int k = (int)index[i] + 1;
+      if (d_ins[k]) {
+        memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
+                     d_out->data<T>() + i * cols, cols * sizeof(T), stream);
+      }
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    multiplex, ops::MultiplexGPUKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    multiplex_grad,
+    ops::MultiplexGradGPUKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/multiplex_op.h b/paddle/operators/multiplex_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..44e8e0c1998014081b7e0aac603d573aba1f4a13
--- /dev/null
+++ b/paddle/operators/multiplex_op.h
@@ -0,0 +1,78 @@
+
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/memory/memcpy.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class MultiplexCPUKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto rows = ins[1]->dims()[0];
+    auto cols = ins[1]->dims()[1];
+    auto* index = ins[0]->data<T>();
+    Place place = boost::get<Place>(ctx.GetPlace());
+    for (auto i = 0; i < rows; i++) {
+      int k = (int)index[i] + 1;
+      PADDLE_ENFORCE_LT(k, ins.size(),
+                        "index exceeds the number of candidate tensors.");
+      memory::Copy(place, out->data<T>() + i * cols, place,
+                   ins[k]->data<T>() + i * cols, cols * sizeof(T));
+    }
+  }
+};
+
+template <typename Place, typename T>
+class MultiplexGradCPUKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto d_ins =
+        ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+    for (size_t i = 1; i < d_ins.size(); i++) {
+      if (d_ins[i]) {
+        d_ins[i]->mutable_data<T>(ctx.GetPlace());
+        auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
+        t.device(ctx.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+      }
+    }
+
+    auto rows = ins[1]->dims()[0];
+    auto cols = ins[1]->dims()[1];
+    auto* index = ins[0]->data<T>();
+    Place place = boost::get<Place>(ctx.GetPlace());
+    for (auto i = 0; i < rows; i++) {
+      int k = (int)index[i] + 1;
+      if (d_ins[k]) {
+        memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
+                     d_out->data<T>() + i * cols, cols * sizeof(T));
+      }
+    }
+  }
+};
+}
+}
diff --git a/python/paddle/v2/framework/tests/test_multiplex_op.py b/python/paddle/v2/framework/tests/test_multiplex_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b3881cde24c7fb96c3d7f9411352bc62d55077
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_multiplex_op.py
@@ -0,0 +1,43 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestMultiplexOp(OpTest):
+    def setUp(self):
+        self.op_type = "multiplex"
+        rows = 3
+        index = np.array([3, 1, 0])
+        ins1 = np.random.random((rows, 10)).astype("float32")
+        ins2 = np.random.random((rows, 10)).astype("float32")
+        ins3 = np.random.random((rows, 10)).astype("float32")
+        ins4 = np.random.random((rows, 10)).astype("float32")
+        self.inputs = {
+            'X': [('index', index), ('x1', ins1), ('x2', ins2), ('x3', ins3),
+                  ('x4', ins4)]
+        }
+        # multiplex output
+        output = np.zeros_like(ins1)
+        for i in range(0, rows):
+            k = index[i] + 1
+            output[i] = self.inputs['X'][k][1][i]
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['x1', 'x2', 'x3', 'x4'], 'Out')
+
+    def test_check_grad_ignore_x1(self):
+        self.check_grad(['x2', 'x3', 'x4'], 'Out', no_grad_set=set('x1'))
+
+    def test_check_grad_ignore_x1_x2(self):
+        self.check_grad(['x3', 'x4'], 'Out', no_grad_set=set(['x1', 'x2']))
+
+    def test_check_grad_ignore_x3(self):
+        self.check_grad(['x1', 'x2', 'x4'], 'Out', no_grad_set=set('x3'))
+
+
+if __name__ == '__main__':
+    unittest.main()