diff --git a/paddle/fluid/operators/sequence_expand_op.cu b/paddle/fluid/operators/sequence_expand_op.cu
index 26622d23afa1c703e237628bcb11db8f1da73210..6477af89f110a51fe2d494f1b54f4002fae3560d 100644
--- a/paddle/fluid/operators/sequence_expand_op.cu
+++ b/paddle/fluid/operators/sequence_expand_op.cu
@@ -15,6 +15,58 @@ limitations under the License. */
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/sequence_expand_op.h"
 
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+__global__ sequence_expand_kernel(const T* x_data, T* out_data, size_t* lod,
+                                  size_t element_len) {
+  int BLOCK_SIZE = 1024;
+  __shared__ T shm_lod[BLOCK_SIZE];
+  for (int idx = threadIdx.x; idx < BLOCK_SIZE; ++idx) {
+    shm_lod[idx] = lod[idx];
+  }
+  for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < lod.size();
+       idx += blockDim.x * gridDim.x) {
+    int scale = lod[i]
+  }
+}
+
+template <typename T>
+void SequenceExpandFunctor<platform::CPUDeviceContext, T>::operator()(
+    const platform::CPUDeviceContext& context, const LoDTensor& x,
+    LoDTensor* out) {
+  x_dims = x.dims();
+  size_t element_len = framework::product(x_dims) / x_dims[0];
+  T* out_data = out->mutable_data<T>(context.GetPlace());
+  auto out_starts = out->lod().back();
+
+  const int kThreadsPerBlock = 1024;
+  int block_cols = kThreadsPerBlock;
+  if (out_cols < kThreadsPerBlock) {  // block_cols is aligned by 32.
+    block_cols = ((out_cols + 31) >> 5) << 5;
+  }
+  int block_rows = kThreadsPerBlock / block_cols;
+  dim3 block_size = dim3(block_cols, block_rows, 1);
+
+  int max_threads = context.GetMaxPhysicalThreadCount();
+  int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
+
+  int grid_cols =
+      std::min((out_cols + block_cols - 1) / block_cols, max_blocks);
+  int grid_rows =
+      std::min(max_blocks / grid_cols, std::max(out_rows / block_rows, 1));
+  dim3 grid_size = dim3(grid_cols, grid_rows, 1);
+  sequence_expand_kernel<<<grid_size, block_size, 0, context.stream()>>>(
+      x.data<T>(), out->mutable_data<T>(context.GetPlace()),
+      out_starts.CUDAData(context.GetPlace()), element_len);
+}
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     sequence_expand,
diff --git a/paddle/fluid/operators/sequence_expand_op.h b/paddle/fluid/operators/sequence_expand_op.h
index 76dde976db2d19e307ae7406be8280f9b4987187..12e4018b95bbf6b9d995e4d2933c6e81b7841d2b 100644
--- a/paddle/fluid/operators/sequence_expand_op.h
+++ b/paddle/fluid/operators/sequence_expand_op.h
@@ -16,13 +16,44 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
-#include "unsupported/Eigen/CXX11/Tensor"
+#include "paddle/fluid/platform/device_context.h"
 
 namespace paddle {
 namespace operators {
 
 using LoDTensor = framework::LoDTensor;
 
+template <typename DeviceContext, typename T>
+struct SequenceExpandFunctor {
+  void operator()(const DeviceContext& ctx, const LoDTensor& x, LoDTensor* out);
+};
+
+// template <typename DeviceContext, typename T>
+// struct SequenceExpandGradFunctor {};
+
+template <typename T>
+void SequenceExpandFunctor<platform::CPUDeviceContext, T>::operator()(
+    const platform::CPUDeviceContext& context, const LoDTensor& x,
+    LoDTensor* out) {
+  x_dims = x.dims();
+  size_t element_len = framework::product(x_dims) / x_dims[0];
+  T* out_data = out->mutable_data<T>(context.GetPlace());
+  auto out_starts = out->lod().back();
+
+  for (size_t i = 0; i < out_starts.size() - 1; i++) {
+    int scale = out_starts[i + 1] - out_starts[i];
+    Eigen::TensorMap<
+        Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
+        x_t(x_data, 1, element_len);
+    Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
+        out_t(out_data, scale, element_len);
+    Eigen::array<int, 2> cast({{scale, 1}});
+    out_t.device(*context.eigen_device()) = x_t.broadcast(cast);
+    x_data += element_len;
+    out_data += element_len * scale;
+  }
+}
+
 template <typename DeviceContext, typename T>
 class SequenceExpandKernel : public framework::OpKernel<T> {
  public:
@@ -38,24 +69,8 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
                       "The size of last lod level in Input(Y)"
                       "must be equal to dims[0] of Input(X).");
     out->set_lod(y->lod());
-    auto* place =
-        context.template device_context<DeviceContext>().eigen_device();
-    size_t element_len = framework::product(x_dims) / x_dims[0];
-    T* out_data = out->mutable_data<T>(context.GetPlace());
-    auto out_starts = out->lod().back();
-
-    for (size_t i = 0; i < out_starts.size() - 1; i++) {
-      int scale = out_starts[i + 1] - out_starts[i];
-      Eigen::TensorMap<
-          Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-          x_t(x_data, 1, element_len);
-      Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-          out_t(out_data, scale, element_len);
-      Eigen::array<int, 2> cast({{scale, 1}});
-      out_t.device(*place) = x_t.broadcast(cast);
-      x_data += element_len;
-      out_data += element_len * scale;
-    }
+    SequenceExpandFunctor<DeviceContext, T> functor;
+    functor(context.template device_context<DeviceContext>(), *x, out);
   }
 };