Merge remote-tracking branch 'baidu/develop' into pybind_selected_rows

doc/design/executor.md

+# Executor Design Doc
+
+## Motivation
+
+We use executor to do the runtime evaluation of a `ProgramDesc`.
+
+## Overview
+
+An executor takes a `ProgramDesc`, a `block_id` and a `Scope`.  The `ProgramDesc` is a list of blocks and each block contains the protobuf definition of all the parameters and operators. The `block_id` specifies the entrance block. And the `Scope` is the container of all the variable instance, which is persistent throughout different runs.
+
+### What does executor do?
+
+It evaluates all the operators in the `block_id`th block of a `ProgramDesc`.
+
+### What does executor NOT do?
+
+It does not do runtime optimization, meaning intelligently parse the dependency of each op a choose which one to be run and in which order they should be run.
+
+It does not do graph partitioning, meaning dividing the `ProgramDesc` into several small pieces and executing them on different devices.
+
+## Implementation
+
+`Executor` evaluates a `ProgramDesc`. Essentially, it instantiates Variables and Operators, then run all the operators in sequence. [[code]](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.cc)

paddle/framework/op_registry.h

@@ -82,16 +82,6 @@ class OpRegistry {
   static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
 };
 
-template <typename OpType, typename ProtoMakerType, typename GradOpType>
-class OpRegistrar : public Registrar {
- public:
-  explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
-  OpRegistrar(const char* op_type, const char* grad_op_type) {
-    OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
-                                                               grad_op_type);
-  }
-};
-
 template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
 struct OpKernelRegistrarFunctor;
 

paddle/framework/selected_rows.h

@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/tensor.h"
 
 namespace paddle {
@@ -34,9 +35,9 @@ class SelectedRows {
 
   void set_height(int64_t height) { height_ = height; }
 
-  const std::vector<int64_t>& rows() const { return rows_; }
+  const Vector<int64_t>& rows() const { return rows_; }
 
-  void set_rows(const std::vector<int64_t>& rows) { rows_ = rows; }
+  void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
 
   DDim GetCompleteDims() const {
     std::vector<int64_t> dims = vectorize(value_->dims());
@@ -45,7 +46,10 @@ class SelectedRows {
   }
 
  private:
-  std::vector<int64_t> rows_;
+  // Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
+  // SelectedRows are simplely concated when adding together. Until a
+  // SelectedRows add a Tensor, will the duplicate rows be handled.
+  Vector<int64_t> rows_;
   std::unique_ptr<Tensor> value_{nullptr};
   int64_t height_;
 };

paddle/operators/cross_entropy_op.cu

@@ -91,7 +91,8 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
                               .stream()>>>(dx_data, dy_data, x_data, label_data,
                                            batch_size, class_num);
     } else {
-      math::SetConstant<platform::GPUPlace, T>(ctx.device_context(), dx, 0);
+      math::SetConstant<platform::GPUPlace, T> functor;
+      functor(ctx.device_context(), dx, 0);
       auto* label_data = label->data<int>();
       grid = (batch_size + block - 1) / block;
       CrossEntropyGradientKernel<T><<<

paddle/operators/cross_entropy_op.h

@@ -70,7 +70,8 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
       const T* x_data = x->data<T>();
       const int* label_data = label->data<int>();
 
-      math::SetConstant<platform::CPUPlace, T>(ctx.device_context(), dx, 0);
+      math::SetConstant<platform::CPUPlace, T> functor;
+      functor(ctx.device_context(), dx, 0);
 
       for (int i = 0; i < batch_size; ++i) {
         PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);

paddle/operators/math/CMakeLists.txt

 if(WITH_GPU)
     nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator)
-    nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
+    nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function tensor)
+    nv_library(selected_rows_functor SRCS selected_rows_functor.cc selected_rows_functor.cu DEPS selected_rows math_function)
+    nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor)
     nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
     nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
     nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
-    cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
+    cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
     cc_library(softmax SRCS softmax.cc DEPS operator)
     cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
     cc_library(pooling SRCS pooling.cc DEPS device_context)
     cc_library(vol2col SRCS vol2col.cc DEPS device_context)
 endif()
 
+cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
+cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selected_rows_functor)
 cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)
 cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col tensor)

paddle/operators/math/math_function.cc

@@ -130,6 +130,8 @@ void matmul<platform::CPUPlace, double>(
       matrix_b.data<double>(), beta, matrix_out->data<double>());
 }
 
+template struct SetConstant<platform::CPUPlace, float>;
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.cu

@@ -155,6 +155,8 @@ void matmul<platform::GPUPlace, double>(
       matrix_b.data<double>(), beta, matrix_out->data<double>());
 }
 
+template struct SetConstant<platform::GPUPlace, float>;
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.h

@@ -86,11 +86,14 @@ void matmul(const platform::DeviceContext& context,
             framework::Tensor* matrix_out, T beta);
 
 template <typename Place, typename T>
-void SetConstant(const platform::DeviceContext& context,
+struct SetConstant {
+  void operator()(const platform::DeviceContext& context,
                   framework::Tensor* tensor, T num) {
     auto t = framework::EigenVector<T>::Flatten(*tensor);
-  t.device(*context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(num));
-}
+    t.device(*context.GetEigenDevice<Place>()) =
+        t.constant(static_cast<T>(num));
+  }
+};
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/math_function_test.cc

 #include "paddle/operators/math/math_function.h"
 #include "gtest/gtest.h"
 
-#ifdef PADDLE_WITH_CUDA
-TEST(math_function, notrans_mul_trans) {
-  paddle::framework::Tensor input1;
-  paddle::framework::Tensor input1_gpu;
-  paddle::framework::Tensor input2_gpu;
-  paddle::framework::Tensor out_gpu;
-  paddle::framework::Tensor out;
-
-  auto* cpu_place = new paddle::platform::CPUPlace();
-  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
-  float arr[6] = {0, 1, 2, 3, 4, 5};
-  memcpy(input1_ptr, arr, 6 * sizeof(float));
-
-  auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::CUDADeviceContext context(*gpu_place);
-
-  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
-  input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
-
-  out_gpu.mutable_data<float>({2, 2}, *gpu_place);
-
-  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
-      context, input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0);
-
-  out.CopyFrom<float>(out_gpu, *cpu_place, context);
-
-  float* out_ptr = out.data<float>();
-  context.Wait();
-  EXPECT_EQ(out_ptr[0], 5);
-  EXPECT_EQ(out_ptr[1], 14);
-  EXPECT_EQ(out_ptr[2], 14);
-  EXPECT_EQ(out_ptr[3], 50);
-  delete gpu_place;
-}
-
-TEST(math_function, trans_mul_notrans) {
-  paddle::framework::Tensor input1;
-  paddle::framework::Tensor input1_gpu;
-  paddle::framework::Tensor input2_gpu;
-  paddle::framework::Tensor out_gpu;
-  paddle::framework::Tensor out;
-
-  auto* cpu_place = new paddle::platform::CPUPlace();
-  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
-  float arr[6] = {0, 1, 2, 3, 4, 5};
-  memcpy(input1_ptr, arr, 6 * sizeof(float));
-
-  auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::CUDADeviceContext context(*gpu_place);
-
-  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
-  input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
-
-  out_gpu.mutable_data<float>({3, 3}, *gpu_place);
-
-  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
-      context, input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0);
-
-  out.CopyFrom<float>(out_gpu, *cpu_place, context);
-
-  float* out_ptr = out.data<float>();
-  context.Wait();
-  EXPECT_EQ(out_ptr[0], 9);
-  EXPECT_EQ(out_ptr[1], 12);
-  EXPECT_EQ(out_ptr[2], 15);
-  EXPECT_EQ(out_ptr[3], 12);
-  EXPECT_EQ(out_ptr[4], 17);
-  EXPECT_EQ(out_ptr[5], 22);
-  EXPECT_EQ(out_ptr[6], 15);
-  EXPECT_EQ(out_ptr[7], 22);
-  EXPECT_EQ(out_ptr[8], 29);
-  delete gpu_place;
-}
-
-TEST(math_function, gemm_notrans_cublas) {
-  paddle::framework::Tensor input1;
-  paddle::framework::Tensor input2;
-  paddle::framework::Tensor input3;
-  paddle::framework::Tensor input1_gpu;
-  paddle::framework::Tensor input2_gpu;
-  paddle::framework::Tensor input3_gpu;
-
-  int m = 2;
-  int n = 3;
-  int k = 3;
-  auto* cpu_place = new paddle::platform::CPUPlace();
-  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
-  float arr1[6] = {0, 1, 2, 3, 4, 5};
-  memcpy(input1_ptr, arr1, 6 * sizeof(float));
-  float* input2_ptr = input2.mutable_data<float>({3, 4}, *cpu_place);
-  float arr2[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-  memcpy(input2_ptr, arr2, 12 * sizeof(float));
-  float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
-  float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
-  memcpy(input3_ptr, arr3, 8 * sizeof(float));
-
-  auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::CUDADeviceContext context(*gpu_place);
-
-  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
-  input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
-  input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
-  float* a = input1_gpu.data<float>();
-  float* b = input2_gpu.data<float>();
-  float* c = input3_gpu.mutable_data<float>(*gpu_place);
-
-  paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
-      context, false, false, m, n, k, 1, a, 3, b + 1, 4, 1, c + 1, 4);
-
-  input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
-
-  // numpy code:
-  // a = np.arange(6).reshape(2, 3)
-  // b = np.arange(12).reshape(3, 4)[:, 1:]
-  // c = np.arange(8).reshape(2, 4)[:, 1:]
-  // out = np.arange(8).reshape(2, 4)
-  // out[:, 1:] = np.dot(a, b) + c
-  context.Wait();
-  EXPECT_EQ(input3_ptr[0], 0);
-  EXPECT_EQ(input3_ptr[1], 24);
-  EXPECT_EQ(input3_ptr[2], 28);
-  EXPECT_EQ(input3_ptr[3], 32);
-  EXPECT_EQ(input3_ptr[4], 4);
-  EXPECT_EQ(input3_ptr[5], 73);
-  EXPECT_EQ(input3_ptr[6], 86);
-  EXPECT_EQ(input3_ptr[7], 99);
-  delete gpu_place;
-}
-
-TEST(math_function, gemm_trans_cublas) {
-  paddle::framework::Tensor input1;
-  paddle::framework::Tensor input2;
-  paddle::framework::Tensor input3;
-  paddle::framework::Tensor input1_gpu;
-  paddle::framework::Tensor input2_gpu;
-  paddle::framework::Tensor input3_gpu;
-
-  int m = 2;
-  int n = 3;
-  int k = 3;
-  auto* cpu_place = new paddle::platform::CPUPlace();
-  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
-  float arr1[6] = {0, 1, 2, 3, 4, 5};
-  memcpy(input1_ptr, arr1, 6 * sizeof(float));
-  float* input2_ptr = input2.mutable_data<float>({4, 3}, *cpu_place);
-  float arr2[12] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-  memcpy(input2_ptr, arr2, 12 * sizeof(float));
-  float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
-  float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
-  memcpy(input3_ptr, arr3, 8 * sizeof(float));
-
-  auto* gpu_place = new paddle::platform::GPUPlace(0);
-  paddle::platform::CUDADeviceContext context(*gpu_place);
-
-  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
-  input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
-  input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
-  float* a = input1_gpu.data<float>();
-  float* b = input2_gpu.data<float>();
-  float* c = input3_gpu.mutable_data<float>(*gpu_place);
-
-  paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
-      context, false, true, m, n, k, 1, a, 3, b + 3, 3, 1, c + 1, 4);
-
-  input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
-  context.Wait();
-
-  EXPECT_EQ(input3_ptr[0], 0);
-  EXPECT_EQ(input3_ptr[1], 24);
-  EXPECT_EQ(input3_ptr[2], 28);
-  EXPECT_EQ(input3_ptr[3], 32);
-  EXPECT_EQ(input3_ptr[4], 4);
-  EXPECT_EQ(input3_ptr[5], 73);
-  EXPECT_EQ(input3_ptr[6], 86);
-  EXPECT_EQ(input3_ptr[7], 99);
-  delete gpu_place;
-}
-#endif
-
 TEST(math_function, gemm_notrans_cblas) {
   paddle::framework::Tensor input1;
   paddle::framework::Tensor input2;
@@ -253,15 +74,15 @@ TEST(math_function, zero) {
   auto* cpu_place = new paddle::platform::CPUPlace();
   float* t = tensor.mutable_data<float>({2, 2}, *cpu_place);
   paddle::platform::CPUDeviceContext context(*cpu_place);
-  paddle::operators::math::SetConstant<paddle::platform::CPUPlace, float>(
-      context, &tensor, 0);
+  paddle::operators::math::SetConstant<paddle::platform::CPUPlace, float>
+      functor;
+  functor(context, &tensor, 0);
   EXPECT_EQ(t[0], 0);
   EXPECT_EQ(t[1], 0);
   EXPECT_EQ(t[2], 0);
   EXPECT_EQ(t[3], 0);
 
-  paddle::operators::math::SetConstant<paddle::platform::CPUPlace, float>(
-      context, &tensor, 1);
+  functor(context, &tensor, 1);
 
   EXPECT_EQ(t[0], 1);
   EXPECT_EQ(t[1], 1);

paddle/operators/math/math_function_test.cu

+#include "gtest/gtest.h"
+#include "paddle/operators/math/math_function.h"
+
+TEST(math_function, notrans_mul_trans) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor out_gpu;
+  paddle::framework::Tensor out;
+
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr, 6 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
+  input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
+
+  out_gpu.mutable_data<float>({2, 2}, *gpu_place);
+
+  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
+      context, input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0);
+
+  out.CopyFrom<float>(out_gpu, *cpu_place, context);
+
+  float* out_ptr = out.data<float>();
+  context.Wait();
+  EXPECT_EQ(out_ptr[0], 5);
+  EXPECT_EQ(out_ptr[1], 14);
+  EXPECT_EQ(out_ptr[2], 14);
+  EXPECT_EQ(out_ptr[3], 50);
+  delete gpu_place;
+}
+
+TEST(math_function, trans_mul_notrans) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor out_gpu;
+  paddle::framework::Tensor out;
+
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr, 6 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
+  input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
+
+  out_gpu.mutable_data<float>({3, 3}, *gpu_place);
+
+  paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
+      context, input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0);
+
+  out.CopyFrom<float>(out_gpu, *cpu_place, context);
+
+  float* out_ptr = out.data<float>();
+  context.Wait();
+  EXPECT_EQ(out_ptr[0], 9);
+  EXPECT_EQ(out_ptr[1], 12);
+  EXPECT_EQ(out_ptr[2], 15);
+  EXPECT_EQ(out_ptr[3], 12);
+  EXPECT_EQ(out_ptr[4], 17);
+  EXPECT_EQ(out_ptr[5], 22);
+  EXPECT_EQ(out_ptr[6], 15);
+  EXPECT_EQ(out_ptr[7], 22);
+  EXPECT_EQ(out_ptr[8], 29);
+  delete gpu_place;
+}
+
+TEST(math_function, gemm_notrans_cublas) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input2;
+  paddle::framework::Tensor input3;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor input3_gpu;
+
+  int m = 2;
+  int n = 3;
+  int k = 3;
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr1[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr1, 6 * sizeof(float));
+  float* input2_ptr = input2.mutable_data<float>({3, 4}, *cpu_place);
+  float arr2[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+  memcpy(input2_ptr, arr2, 12 * sizeof(float));
+  float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
+  float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
+  memcpy(input3_ptr, arr3, 8 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
+  input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
+  input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
+  float* a = input1_gpu.data<float>();
+  float* b = input2_gpu.data<float>();
+  float* c = input3_gpu.mutable_data<float>(*gpu_place);
+
+  paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
+      context, false, false, m, n, k, 1, a, 3, b + 1, 4, 1, c + 1, 4);
+
+  input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
+
+  // numpy code:
+  // a = np.arange(6).reshape(2, 3)
+  // b = np.arange(12).reshape(3, 4)[:, 1:]
+  // c = np.arange(8).reshape(2, 4)[:, 1:]
+  // out = np.arange(8).reshape(2, 4)
+  // out[:, 1:] = np.dot(a, b) + c
+  context.Wait();
+  EXPECT_EQ(input3_ptr[0], 0);
+  EXPECT_EQ(input3_ptr[1], 24);
+  EXPECT_EQ(input3_ptr[2], 28);
+  EXPECT_EQ(input3_ptr[3], 32);
+  EXPECT_EQ(input3_ptr[4], 4);
+  EXPECT_EQ(input3_ptr[5], 73);
+  EXPECT_EQ(input3_ptr[6], 86);
+  EXPECT_EQ(input3_ptr[7], 99);
+  delete gpu_place;
+}
+
+TEST(math_function, gemm_trans_cublas) {
+  paddle::framework::Tensor input1;
+  paddle::framework::Tensor input2;
+  paddle::framework::Tensor input3;
+  paddle::framework::Tensor input1_gpu;
+  paddle::framework::Tensor input2_gpu;
+  paddle::framework::Tensor input3_gpu;
+
+  int m = 2;
+  int n = 3;
+  int k = 3;
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
+  float arr1[6] = {0, 1, 2, 3, 4, 5};
+  memcpy(input1_ptr, arr1, 6 * sizeof(float));
+  float* input2_ptr = input2.mutable_data<float>({4, 3}, *cpu_place);
+  float arr2[12] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+  memcpy(input2_ptr, arr2, 12 * sizeof(float));
+  float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
+  float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
+  memcpy(input3_ptr, arr3, 8 * sizeof(float));
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::platform::CUDADeviceContext context(*gpu_place);
+
+  input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
+  input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
+  input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
+  float* a = input1_gpu.data<float>();
+  float* b = input2_gpu.data<float>();
+  float* c = input3_gpu.mutable_data<float>(*gpu_place);
+
+  paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
+      context, false, true, m, n, k, 1, a, 3, b + 3, 3, 1, c + 1, 4);
+
+  input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
+  context.Wait();
+
+  EXPECT_EQ(input3_ptr[0], 0);
+  EXPECT_EQ(input3_ptr[1], 24);
+  EXPECT_EQ(input3_ptr[2], 28);
+  EXPECT_EQ(input3_ptr[3], 32);
+  EXPECT_EQ(input3_ptr[4], 4);
+  EXPECT_EQ(input3_ptr[5], 73);
+  EXPECT_EQ(input3_ptr[6], 86);
+  EXPECT_EQ(input3_ptr[7], 99);
+  delete gpu_place;
+}

paddle/operators/math/selected_rows_functor.cc

+/* 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/math/selected_rows_functor.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+template <typename T>
+struct SelectedRowsAdd<platform::CPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::SelectedRows& input2,
+                  framework::SelectedRows* output) {
+    auto in1_height = input1.height();
+    PADDLE_ENFORCE_EQ(in1_height, input2.height());
+    output->set_height(in1_height);
+
+    auto& in1_rows = input1.rows();
+    auto& in2_rows = input2.rows();
+    std::vector<int64_t> out_rows;
+    out_rows.reserve(in1_rows.size() + in2_rows.size());
+
+    // concat rows
+    out_rows.insert(out_rows.end(), in1_rows.begin(), in1_rows.end());
+    out_rows.insert(out_rows.end(), in2_rows.begin(), in2_rows.end());
+    output->set_rows(out_rows);
+
+    auto* out_value = output->mutable_value();
+    auto& in1_value = input1.value();
+    auto& in2_value = input2.value();
+
+    auto in1_row_numel = in1_value.numel() / in1_rows.size();
+    PADDLE_ENFORCE_EQ(in1_row_numel, in2_value.numel() / in2_rows.size());
+    PADDLE_ENFORCE_EQ(in1_row_numel, out_value->numel() / out_rows.size());
+
+    auto in1_place = input1.place();
+    PADDLE_ENFORCE(platform::is_cpu_place(in1_place));
+    auto in2_place = input2.place();
+    PADDLE_ENFORCE(platform::is_cpu_place(in2_place));
+    auto out_place = context.GetPlace();
+    PADDLE_ENFORCE(platform::is_cpu_place(out_place));
+
+    auto* out_data = out_value->data<T>();
+    auto* in1_data = in1_value.data<T>();
+    memory::Copy(boost::get<platform::CPUPlace>(out_place), out_data,
+                 boost::get<platform::CPUPlace>(in1_place), in1_data,
+                 in1_value.numel() * sizeof(T));
+
+    auto* in2_data = in2_value.data<T>();
+    memory::Copy(boost::get<platform::CPUPlace>(out_place),
+                 out_data + in1_value.numel(),
+                 boost::get<platform::CPUPlace>(in2_place), in2_data,
+                 in2_value.numel() * sizeof(T));
+  }
+};
+
+template struct SelectedRowsAdd<platform::CPUPlace, float>;
+
+template <typename T>
+struct SelectedRowsAddTensor<platform::CPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::Tensor& input2, framework::Tensor* output) {
+    auto in1_height = input1.height();
+    auto in2_dims = input2.dims();
+    auto out_dims = output->dims();
+    PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
+    PADDLE_ENFORCE_EQ(in1_height, out_dims[0]);
+
+    auto& in1_value = input1.value();
+    auto& in1_rows = input1.rows();
+
+    int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
+    PADDLE_ENFORCE_EQ(in1_row_numel, input2.numel() / in1_height);
+    PADDLE_ENFORCE_EQ(in1_row_numel, output->numel() / in1_height);
+
+    SetConstant<platform::CPUPlace, T> functor;
+    functor(context, output, 0.0);
+
+    auto* in1_data = in1_value.data<T>();
+    auto* out_data = output->data<T>();
+
+    for (size_t i = 0; i < in1_rows.size(); i++) {
+      for (int64_t j = 0; j < in1_row_numel; j++) {
+        out_data[in1_rows[i] * in1_row_numel + j] +=
+            in1_data[i * in1_row_numel + j];
+      }
+    }
+
+    auto out_eigen = framework::EigenVector<T>::Flatten(*output);
+    auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
+    out_eigen.device(*context.GetEigenDevice<platform::CPUPlace>()) =
+        out_eigen + in2_eigen;
+  }
+};
+
+template struct SelectedRowsAddTensor<platform::CPUPlace, float>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/selected_rows_functor.cu

+/* 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/math/math_function.h"
+#include "paddle/operators/math/selected_rows_functor.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+template <typename T>
+struct SelectedRowsAdd<platform::GPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::SelectedRows& input2,
+                  framework::SelectedRows* output) {
+    auto in1_height = input1.height();
+    PADDLE_ENFORCE_EQ(in1_height, input2.height());
+    output->set_height(in1_height);
+
+    auto& in1_rows = input1.rows();
+    auto& in2_rows = input2.rows();
+    std::vector<int64_t> out_rows;
+    out_rows.reserve(in1_rows.size() + in2_rows.size());
+
+    // concat rows
+    out_rows.insert(out_rows.end(), in1_rows.begin(), in1_rows.end());
+    out_rows.insert(out_rows.end(), in2_rows.begin(), in2_rows.end());
+    output->set_rows(out_rows);
+
+    auto* out_value = output->mutable_value();
+    auto& in1_value = input1.value();
+    auto& in2_value = input2.value();
+
+    auto in1_row_numel = in1_value.numel() / in1_rows.size();
+    PADDLE_ENFORCE_EQ(in1_row_numel, in2_value.numel() / in2_rows.size());
+    PADDLE_ENFORCE_EQ(in1_row_numel, out_value->numel() / out_rows.size());
+
+    auto* out_data = out_value->data<T>();
+    auto* in1_data = in1_value.data<T>();
+
+    auto in1_place = input1.place();
+    PADDLE_ENFORCE(platform::is_gpu_place(in1_place));
+    auto in2_place = input2.place();
+    PADDLE_ENFORCE(platform::is_gpu_place(in2_place));
+    auto out_place = context.GetPlace();
+    PADDLE_ENFORCE(platform::is_gpu_place(out_place));
+
+    memory::Copy(
+        boost::get<platform::GPUPlace>(out_place), out_data,
+        boost::get<platform::GPUPlace>(in1_place), in1_data,
+        in1_value.numel() * sizeof(T),
+        reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
+
+    auto* in2_data = in2_value.data<T>();
+    memory::Copy(
+        boost::get<platform::GPUPlace>(out_place), out_data + in1_value.numel(),
+        boost::get<platform::GPUPlace>(in2_place), in2_data,
+        in2_value.numel() * sizeof(T),
+        reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
+  }
+};
+
+template struct SelectedRowsAdd<platform::GPUPlace, float>;
+
+namespace {
+template <typename T>
+__global__ void SelectedRowsAddTensorKernel(const T* selected_rows,
+                                            const int64_t* rows, T* tensor_out,
+                                            int64_t row_numel, int block_size) {
+  const int ty = blockIdx.y;
+  int tid = threadIdx.x;
+
+  selected_rows += ty * row_numel;
+  tensor_out += rows[ty] * row_numel;
+
+  for (int index = tid; index < row_numel; index += block_size) {
+    // Since index in rows of SelectedRows can be duplicate, we can not use
+    // tensor_out[index] += selected_rows[index]; Instead, we have to use
+    // AtomicAdd to avoid concurrent write error.
+    paddle::platform::CudaAtomicAdd(tensor_out + index, selected_rows[index]);
+  }
+}
+}  // namespace
+
+template <typename T>
+struct SelectedRowsAddTensor<platform::GPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::Tensor& input2, framework::Tensor* output) {
+    auto in1_height = input1.height();
+    auto in2_dims = input2.dims();
+    auto out_dims = output->dims();
+    PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
+    PADDLE_ENFORCE_EQ(in1_height, out_dims[0]);
+
+    auto& in1_value = input1.value();
+    auto& in1_rows = input1.rows();
+
+    int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
+    PADDLE_ENFORCE_EQ(in1_row_numel, input2.numel() / in1_height);
+    PADDLE_ENFORCE_EQ(in1_row_numel, output->numel() / in1_height);
+
+    auto* in1_data = in1_value.data<T>();
+    auto* in2_data = input2.data<T>();
+    auto* out_data = output->data<T>();
+
+    SetConstant<platform::GPUPlace, T> functor;
+    functor(context, output, 0.0);
+
+    int block_size = 256;
+    dim3 threads(block_size, 1);
+    dim3 grid(1, in1_rows.size());
+    SelectedRowsAddTensorKernel<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(in1_data, in1_rows.data(), out_data,
+                              in1_row_numel, block_size);
+
+    auto out_eigen = framework::EigenVector<T>::Flatten(*output);
+    auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
+    out_eigen.device(*context.GetEigenDevice<platform::GPUPlace>()) =
+        out_eigen + in2_eigen;
+  }
+};
+
+template struct SelectedRowsAddTensor<platform::GPUPlace, float>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/selected_rows_functor.h

+/* 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/selected_rows.h"
+#include "paddle/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// SelectedRows + SelectedRows will simplely concat value and rows.
+// The real computation happens in dealing with LoDTensor.
+template <typename Place, typename T>
+struct SelectedRowsAdd {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::SelectedRows& input2,
+                  framework::SelectedRows* output);
+};
+
+template <typename Place, typename T>
+struct SelectedRowsAddTensor {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input1,
+                  const framework::Tensor& input2, framework::Tensor* output);
+};
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/selected_rows_functor_test.cc

+/* 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/math/selected_rows_functor.h"
+#include "gtest/gtest.h"
+#include "paddle/operators/math/math_function.h"
+
+TEST(selected_rows_functor, cpu_add) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+  using namespace paddle::operators::math;
+
+  CPUPlace cpu_place;
+  CPUDeviceContext ctx(cpu_place);
+  SetConstant<CPUPlace, float> functor;
+  int64_t height = 10;
+  int64_t row_numel = 10;
+
+  std::vector<int64_t> rows1{0, 4, 7};
+  std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), cpu_place);
+  functor(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{0, 5, 7, 9};
+  std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), cpu_place);
+  functor(ctx, in2_value, 2.0);
+
+  std::unique_ptr<SelectedRows> output{new SelectedRows()};
+  auto* out_value = output->mutable_value();
+
+  // simplely concat two SelectedRows
+  out_value->mutable_data<float>(make_ddim({7, 10}), cpu_place);
+
+  SelectedRowsAdd<CPUPlace, float> add_functor;
+  add_functor(ctx, *selected_rows1, *selected_rows2, output.get());
+
+  auto out_height = output->height();
+  EXPECT_EQ(out_height, height);
+
+  auto& out_rows = output->rows();
+
+  // input1 rows
+  EXPECT_EQ(out_rows[0], 0);
+  EXPECT_EQ(out_rows[1], 4);
+  EXPECT_EQ(out_rows[2], 7);
+  // input2 rows
+  EXPECT_EQ(out_rows[3], 0);
+  EXPECT_EQ(out_rows[4], 5);
+  EXPECT_EQ(out_rows[5], 7);
+  EXPECT_EQ(out_rows[6], 9);
+
+  auto* out_data = output->value().data<float>();
+  // input1 value
+  EXPECT_EQ(out_data[0 * row_numel + 0], 1.0);
+  EXPECT_EQ(out_data[0 * row_numel + 8], 1.0);
+  EXPECT_EQ(out_data[1 * row_numel + 1], 1.0);
+  EXPECT_EQ(out_data[2 * row_numel + 6], 1.0);
+  // input2 value
+  EXPECT_EQ(out_data[3 * row_numel + 3], 2.0);
+  EXPECT_EQ(out_data[3 * row_numel + 8], 2.0);
+  EXPECT_EQ(out_data[4 * row_numel + 4], 2.0);
+  EXPECT_EQ(out_data[5 * row_numel + 7], 2.0);
+  EXPECT_EQ(out_data[6 * row_numel + 9], 2.0);
+
+  std::unique_ptr<Tensor> tensor1{new Tensor()};
+  tensor1->mutable_data<float>(make_ddim({height, row_numel}), cpu_place);
+  functor(ctx, tensor1.get(), 3.0);
+
+  std::unique_ptr<Tensor> tensor2{new Tensor()};
+  tensor2->mutable_data<float>(make_ddim({height, row_numel}), cpu_place);
+
+  SelectedRowsAddTensor<CPUPlace, float> add_tensor_functor;
+  add_tensor_functor(ctx, *output, *tensor1, tensor2.get());
+
+  auto* tensor2_data = tensor2->data<float>();
+  // row0: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor2_data[0 * row_numel + 0], 6.0);
+  // row1: 3.0
+  EXPECT_EQ(tensor2_data[1 * row_numel + 1], 3.0);
+  // row4 : 1.0 + 3.0
+  EXPECT_EQ(tensor2_data[4 * row_numel + 6], 4.0);
+  // row5: 2.0 + 3.0
+  EXPECT_EQ(tensor2_data[5 * row_numel + 7], 5.0);
+  // row6: 3.0
+  EXPECT_EQ(tensor2_data[6 * row_numel + 1], 3.0);
+  // row7: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor2_data[7 * row_numel + 3], 6.0);
+  // row9: 2.0 + 3.0
+  EXPECT_EQ(tensor2_data[9 * row_numel + 6], 5.0);
+}

paddle/operators/math/selected_rows_functor_test.cu

+/* 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 "gtest/gtest.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/selected_rows_functor.h"
+
+TEST(selected_rows_functor, gpu_add) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+  using namespace paddle::operators::math;
+
+  GPUPlace gpu_place(0);
+  CPUPlace cpu_place;
+  CUDADeviceContext ctx(gpu_place);
+  SetConstant<GPUPlace, float> functor;
+  int64_t height = 10;
+  int64_t row_numel = 10;
+
+  std::vector<int64_t> rows1{0, 4, 7};
+  std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), gpu_place);
+  functor(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{0, 5, 7, 9};
+  std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), gpu_place);
+  functor(ctx, in2_value, 2.0);
+
+  std::unique_ptr<SelectedRows> output{new SelectedRows()};
+  auto* out_value = output->mutable_value();
+
+  // simplely concat two SelectedRows
+  out_value->mutable_data<float>(make_ddim({7, 10}), gpu_place);
+
+  SelectedRowsAdd<GPUPlace, float> add_functor;
+  add_functor(ctx, *selected_rows1, *selected_rows2, output.get());
+
+  auto out_height = output->height();
+  EXPECT_EQ(out_height, height);
+
+  auto& out_rows = output->rows();
+
+  // input1 rows
+  EXPECT_EQ(out_rows[0], 0);
+  EXPECT_EQ(out_rows[1], 4);
+  EXPECT_EQ(out_rows[2], 7);
+  // input2 rows
+  EXPECT_EQ(out_rows[3], 0);
+  EXPECT_EQ(out_rows[4], 5);
+  EXPECT_EQ(out_rows[5], 7);
+  EXPECT_EQ(out_rows[6], 9);
+
+  Tensor out_cpu;
+  out_cpu.CopyFrom<float>(*out_value, cpu_place, ctx);
+  ctx.Wait();
+
+  auto* out_cpu_data = out_cpu.data<float>();
+  // input1 value
+  EXPECT_EQ(out_cpu_data[0 * row_numel + 0], 1.0);
+  EXPECT_EQ(out_cpu_data[0 * row_numel + 8], 1.0);
+  EXPECT_EQ(out_cpu_data[1 * row_numel + 1], 1.0);
+  EXPECT_EQ(out_cpu_data[2 * row_numel + 6], 1.0);
+  // input2 value
+  EXPECT_EQ(out_cpu_data[3 * row_numel + 3], 2.0);
+  EXPECT_EQ(out_cpu_data[3 * row_numel + 8], 2.0);
+  EXPECT_EQ(out_cpu_data[4 * row_numel + 4], 2.0);
+  EXPECT_EQ(out_cpu_data[5 * row_numel + 7], 2.0);
+  EXPECT_EQ(out_cpu_data[6 * row_numel + 9], 2.0);
+
+  std::unique_ptr<Tensor> tensor1{new Tensor()};
+  tensor1->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
+  functor(ctx, tensor1.get(), 3.0);
+
+  std::unique_ptr<Tensor> tensor2{new Tensor()};
+  tensor2->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
+
+  SelectedRowsAddTensor<GPUPlace, float> add_tensor_functor;
+  add_tensor_functor(ctx, *output, *tensor1, tensor2.get());
+
+  Tensor tensor2_cpu;
+  tensor2_cpu.CopyFrom<float>(*tensor2, cpu_place, ctx);
+  ctx.Wait();
+
+  auto* tensor2_cpu_data = tensor2_cpu.data<float>();
+  // row0: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor2_cpu_data[0 * row_numel + 0], 6.0);
+  // row1: 3.0
+  EXPECT_EQ(tensor2_cpu_data[1 * row_numel + 1], 3.0);
+  // row4 : 1.0 + 3.0
+  EXPECT_EQ(tensor2_cpu_data[4 * row_numel + 6], 4.0);
+  // row5: 2.0 + 3.0
+  EXPECT_EQ(tensor2_cpu_data[5 * row_numel + 7], 5.0);
+  // row6: 3.0
+  EXPECT_EQ(tensor2_cpu_data[6 * row_numel + 1], 3.0);
+  // row7: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor2_cpu_data[7 * row_numel + 3], 6.0);
+  // row9: 2.0 + 3.0
+  EXPECT_EQ(tensor2_cpu_data[9 * row_numel + 6], 5.0);
+}

paddle/operators/sequence_pool_op.h

@@ -111,7 +111,8 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
     in_g->mutable_data<T>(context.GetPlace());
     if (strategy == LAST || strategy == FIRST) {
       // set X@Grad be zero at first when strategy is LAST/FIRST
-      math::SetConstant<Place, T>(context.device_context(), in_g, 0);
+      math::SetConstant<Place, T> functor;
+      functor(context.device_context(), in_g, 0);
     }
     auto place = context.GetEigenDevice<Place>();
     for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {