diff --git a/paddle/fluid/operators/sequence_pad_op.cc b/paddle/fluid/operators/sequence_pad_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..183d38fcc9ac09d58127d88385cb77e0eb2ae810
--- /dev/null
+++ b/paddle/fluid/operators/sequence_pad_op.cc
@@ -0,0 +1,131 @@
+/* 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. */
+
+#include "paddle/fluid/operators/sequence_pad_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SequencePadOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SequencePadOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SequencePadOp should not be null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2,
+                      "Only support 2-D tensor, rank of Input(X) should be 2.");
+
+    auto out_dims = x_dims;
+
+    if (ctx->IsRuntime()) {
+      framework::Variable* x_var =
+          boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
+
+      auto& x_lod = x_var->Get<LoDTensor>().lod();
+
+      PADDLE_ENFORCE_GE(x_lod.size(), 1,
+                        "Input(X) should be sequences containing lod.");
+
+      auto last_level_lod = x_lod[x_lod.size() - 1];
+      size_t max_len = 0;
+
+      for (size_t i = 1; i < last_level_lod.size(); ++i) {
+        auto seq_len = last_level_lod[i] - last_level_lod[i - 1];
+        max_len = max_len < seq_len ? seq_len : max_len;
+      }
+
+      out_dims[0] = max_len * (last_level_lod.size() - 1);
+    } else {
+      framework::VarDesc* x_desc =
+          boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
+      PADDLE_ENFORCE_GE(x_desc->GetLoDLevel(), 1,
+                        "Input(X) should be sequences containing lod.");
+      out_dims[0] = -1;
+    }
+
+    ctx->SetOutputDim("Out", out_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
+class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SequencePadOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(LoDTensor, default LoDTensor<float>) Input variable which "
+             "should contain lod information. Length of each sequence would "
+             "be computed from the most bottom level lod.");
+    AddOutput("Out",
+              "(Tensor) Output variable which would be a common tensor "
+              "without lod. Each sequence would be padded to the maximum "
+              "length.");
+    AddAttr<float>("pad_value",
+                   "(float, default 0.0) Value to be padded "
+                   "to the end of each sequence.");
+    AddComment(R"DOC(
+
+    )DOC");
+  }
+};
+
+class SequencePadGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SequencePadGradOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) of SequencePadGradOp should not be null.");
+
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+      ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(sequence_pad, ops::SequencePadOp, ops::SequencePadOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(sequence_pad_grad, ops::SequencePadGradOp);
+REGISTER_OP_CPU_KERNEL(
+    sequence_pad,
+    ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    sequence_pad_grad,
+    ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
diff --git a/paddle/fluid/operators/sequence_pad_op.cu b/paddle/fluid/operators/sequence_pad_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..a2fa62957eaf423f58d8d380ba9d0c2f540a1902
--- /dev/null
+++ b/paddle/fluid/operators/sequence_pad_op.cu
@@ -0,0 +1,23 @@
+/* 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. */
+
+#include "paddle/fluid/operators/sequence_pad_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    sequence_pad,
+    ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, float>);
+REGISTER_OP_CUDA_KERNEL(
+    sequence_pad_grad,
+    ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, float>);
diff --git a/paddle/fluid/operators/sequence_pad_op.h b/paddle/fluid/operators/sequence_pad_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..b36465d8e717a57ff6445201632f3f9c05cc28dc
--- /dev/null
+++ b/paddle/fluid/operators/sequence_pad_op.h
@@ -0,0 +1,97 @@
+/* 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. */
+
+#pragma once
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
+
+// @TODO clean code
+template <typename DeviceContext, typename T>
+class SequencePadOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x_ptr = ctx.Input<LoDTensor>("X");
+    auto* out_ptr = ctx.Output<LoDTensor>("Out");
+
+    out_ptr->mutable_data<T>(ctx.GetPlace());
+
+    T pad_value = static_cast<T>(ctx.Attr<float>("pad_value"));
+
+    math::SetConstant<DeviceContext, T> set_func;
+    set_func(ctx.template device_context<DeviceContext>(), out_ptr, pad_value);
+
+    auto& x_lod = x_ptr->lod();
+    auto& x_last_level_lod = x_lod[x_lod.size() - 1];
+    auto seq_num = x_last_level_lod.size() - 1;
+    auto max_len = out_ptr->dims()[0] / seq_num;
+
+    PADDLE_ENFORCE_EQ(max_len * seq_num, out_ptr->dims()[0],
+                      "First dimension of `Out` should be equal to "
+                      "maximum length mulplied by sequence number.");
+
+    for (size_t i = 1; i < x_last_level_lod.size(); ++i) {
+      auto x_start = x_last_level_lod[i - 1];
+      auto x_end = x_last_level_lod[i];
+      auto out_start = (i - 1) * max_len;
+      auto out_end = out_start + (x_end - x_start);
+      auto x_sub_tensor = x_ptr->Slice(x_start, x_end);
+      auto out_sub_tensor = out_ptr->Slice(out_start, out_end);
+      framework::TensorCopy(x_sub_tensor, ctx.GetPlace(), &out_sub_tensor);
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class SequencePadGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x_ptr = ctx.Input<LoDTensor>("X");
+    auto* g_out_ptr = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
+    auto* g_x_ptr = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+
+    math::SetConstant<DeviceContext, T> set_func;
+    set_func(ctx.template device_context<DeviceContext>(), g_x_ptr,
+             static_cast<T>(0));
+
+    auto& x_lod = x_ptr->lod();
+    auto& x_last_level_lod = x_lod[x_lod.size() - 1];
+    auto seq_num = x_last_level_lod.size() - 1;
+    int64_t max_len = g_out_ptr->dims()[0] / seq_num;
+
+    PADDLE_ENFORCE_EQ(max_len * seq_num, g_out_ptr->dims()[0],
+                      "First dimension of `Out` should be equal to "
+                      "maximum length mulplied by sequence number.");
+
+    for (size_t i = 1; i < x_last_level_lod.size(); ++i) {
+      auto x_start = x_last_level_lod[i - 1];
+      auto x_end = x_last_level_lod[i];
+      auto out_start = (i - 1) * max_len;
+      auto out_end = out_start + (x_end - x_start);
+
+      auto g_out_sub = g_out_ptr->Slice(out_start, out_end);
+      auto g_x_sub = g_x_ptr->Slice(x_start, x_end);
+      framework::TensorCopy(g_x_sub, ctx.GetPlace(), &g_out_sub);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle