diff --git a/paddle/operators/math/selected_rows_functor.cc b/paddle/operators/math/selected_rows_functor.cc
index ab758d1e7fd8ab361948b28e8cb735b9a742a339..21418ba4b0201e50302edea66d016e9464891f36 100644
--- a/paddle/operators/math/selected_rows_functor.cc
+++ b/paddle/operators/math/selected_rows_functor.cc
@@ -179,6 +179,53 @@ template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, double>;
 template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, int>;
 template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, int64_t>;
 
+// This is a separated namespace for manipulate SelectedRows typed
+// data. Like merge duplicated rows, adding two SelectedRows etc.
+//
+// Another group of functors is called "scatter updates", which means
+// use SelectedRows to update a dense tensor with different Ops, like
+// add or mul.
+namespace scatter {
+
+size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
+  return std::find(rows.begin(), rows.end(), value) - rows.begin();
+}
+
+template <typename T>
+struct MergeAdd<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::SelectedRows& input,
+                  framework::SelectedRows* out) {
+    auto input_rows = input.rows();
+    std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
+    std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
+
+    auto input_width = input.value().dims()[1];
+    // std::unique_ptr<framework::SelectedRows> out{
+    //     new framework::SelectedRows()};
+    out->set_rows(merge_rows);
+    out->set_height(input.height());
+    out->mutable_value()->mutable_data<T>(
+        framework::make_ddim(
+            {static_cast<int64_t>(merge_rows.size()), input_width}),
+        context.GetPlace());
+
+    math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
+    constant_functor(context, out->mutable_value(), 0.0);
+
+    auto* out_data = out->mutable_value()->data<T>();
+    auto* input_data = input.value().data<T>();
+
+    for (size_t i = 0; i < input_rows.size(); i++) {
+      size_t out_i = FindPos(merge_rows, input_rows[i]);
+      for (int64_t j = 0; j < input_width; j++) {
+        out_data[out_i * input_width + j] += input_data[i * input_width + j];
+      }
+    }
+  }
+};
+
+}  // namespace scatter
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/selected_rows_functor.cu b/paddle/operators/math/selected_rows_functor.cu
index 9fddd97a36f7fdb6628d6eeb192cb216fdae3e5b..b2c0fe7bc3da725a9175930657bb4fc8f6da3764 100644
--- a/paddle/operators/math/selected_rows_functor.cu
+++ b/paddle/operators/math/selected_rows_functor.cu
@@ -222,6 +222,73 @@ template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, float>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, double>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int>;
 template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int64_t>;
+
+namespace scatter {
+
+template <typename T, int block_size>
+__global__ void MergeAddKernel(const T* input, const int64_t* input_rows,
+                               T* out, const int64_t* out_rows,
+                               size_t out_rows_size, int64_t row_numel) {
+  const int ty = blockIdx.y;
+  int tid = threadIdx.x;
+  __shared__ size_t out_idx;
+
+  if (tid == 0) {
+    for (size_t i = 0; i < out_rows_size; i++) {
+      if (input_rows[ty] == out_rows[i]) {
+        out_idx = i;
+      }
+    }
+  }
+
+  __syncthreads();
+
+  input += ty * row_numel;
+  out += out_idx * row_numel;
+  for (int index = tid; index < row_numel; index += block_size) {
+    paddle::platform::CudaAtomicAdd(out + index, input[index]);
+  }
+}
+
+template <typename T>
+struct MergeAdd<platform::GPUDeviceContext, T> {
+  void operator()(const platform::GPUDeviceContext& context,
+                  const framework::SelectedRows& input,
+                  framework::SelectedRows* out) {
+    auto input_rows = input.rows();
+    std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
+    std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
+
+    auto input_width = input.value().dims()[1];
+    // std::unique_ptr<framework::SelectedRows> out{
+    //     new framework::SelectedRows()};
+    out->set_rows(merge_rows);
+    out->set_height(input.height());
+    out->mutable_value()->mutable_data<T>(
+        framework::make_ddim(
+            {static_cast<int64_t>(merge_rows.size()), input_width}),
+        context.GetPlace());
+
+    math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
+    constant_functor(context, out->mutable_value(), 0.0);
+
+    auto* out_data = out->mutable_value()->data<T>();
+    auto* input_data = input.value().data<T>();
+
+    const int block_size = 256;
+    dim3 threads(block_size, 1);
+    dim3 grid1(1, input_rows.size());
+
+    MergeAddKernel<
+        T, 256><<<grid1, threads, 0,
+                  reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                      .stream()>>>(input_data, input.rows().data(), out_data,
+                                   out->rows().data(), out->rows().size(),
+                                   input_width);
+  }
+};
+
+}  // namespace scatter
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/selected_rows_functor.h b/paddle/operators/math/selected_rows_functor.h
index 1149075abf16547a120ac8928c45b4972409fc72..8adfca77f6930734b9c5dac43caa979d8705ed2a 100644
--- a/paddle/operators/math/selected_rows_functor.h
+++ b/paddle/operators/math/selected_rows_functor.h
@@ -52,6 +52,53 @@ struct SelectedRowsAddToTensor {
                   framework::Tensor* input2);
 };
 
+namespace scatter {
+// functors for manuplating SelectedRows data
+
+template <typename DeviceContext, typename T>
+struct MergeAdd {
+  // unary functor, merge by adding duplicated rows in
+  // the input SelectedRows object.
+  void operator()(const DeviceContext& context,
+                  const framework::SelectedRows& input,
+                  framework::SelectedRows* out);
+};
+
+template <typename DeviceContext, typename T>
+struct Add {
+  void operator()(const DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::SelectedRows& input2,
+                  framework::SelectedRows* out) {
+    out->set_rows(input1->rows());
+    out->set_height(input1->height());
+    out->mutable_value()->mutable_data<T>(input1->value().dims(),
+                                          context.GetPlace());
+    auto e_out = framework::EigenVector<T>::Flatten(*(out->mutable_value()));
+    auto e_in1 = framework::EigenVector<T>::Flatten(input1->value());
+    auto e_in2 = framework::EigenVector<T>::Flatten(input2->value());
+    e_out.device(*context.eigen_device()) = e_in1 + e_in2;
+  }
+};
+
+template <typename DeviceContext, typename T>
+struct Mul {
+  void operator()(const DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::SelectedRows& input2,
+                  framework::SelectedRows* out) {
+    out->set_rows(input1->rows());
+    out->set_height(input1->height());
+    out->mutable_value()->mutable_data<T>(input1->value().dims(),
+                                          context.GetPlace());
+    auto e_out = framework::EigenVector<T>::Flatten(*(out->mutable_value()));
+    auto e_in1 = framework::EigenVector<T>::Flatten(input1->value());
+    auto e_in2 = framework::EigenVector<T>::Flatten(input2->value());
+    e_out.device(*context.eigen_device()) = e_in1 * e_in2;
+  }
+};
+
+}  // namespace scatter
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle