/* Copyright (c) 2016 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/phi/kernels/funcs/selected_rows_functor.h"

#include "gtest/gtest.h"
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/phi/kernels/funcs/math_function.h"

TEST(selected_rows_functor, cpu_add) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> functor;
  int64_t height = 10;
  int64_t row_numel = 10;

  std::vector<int64_t> rows1{0, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows1{
      new phi::SelectedRows(rows1, height)};
  auto* in1_value = selected_rows1->mutable_value();
  in1_value->mutable_data<float>(
      phi::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<phi::SelectedRows> selected_rows2{
      new phi::SelectedRows(rows2, height)};
  auto* in2_value = selected_rows2->mutable_value();
  in2_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows2.size()), row_numel}),
      cpu_place);
  functor(ctx, in2_value, 2.0);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};
  auto* out_value = output->mutable_value();

  // simplely concat two SelectedRows
  out_value->mutable_data<float>(phi::make_ddim({7, 10}), cpu_place);

  phi::funcs::SelectedRowsAdd<phi::CPUContext, 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<phi::DenseTensor> tensor1{new phi::DenseTensor()};
  tensor1->mutable_data<float>(phi::make_ddim({height, row_numel}), cpu_place);
  functor(ctx, tensor1.get(), 3.0);

  std::unique_ptr<phi::DenseTensor> tensor2{new phi::DenseTensor()};
  tensor2->mutable_data<float>(phi::make_ddim({height, row_numel}), cpu_place);

  phi::funcs::SelectedRowsAddTensor<phi::CPUContext, 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);
}

TEST(selected_rows_functor, cpu_add_to) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> functor;
  int64_t height = 10;
  int64_t row_numel = 10;

  std::vector<int64_t> rows1{0, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows1{
      new phi::SelectedRows(rows1, height)};
  auto* in1_value = selected_rows1->mutable_value();
  in1_value->mutable_data<float>(
      phi::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<phi::SelectedRows> selected_rows2{
      new phi::SelectedRows(rows2, height)};
  auto* in2_value = selected_rows2->mutable_value();
  in2_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows2.size()), row_numel}),
      cpu_place);
  functor(ctx, in2_value, 2.0);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};
  output->set_height(height);
  auto* out_value = output->mutable_value();

  // simplely concat two SelectedRows
  out_value->mutable_data<float>(phi::make_ddim({7, 10}), cpu_place);

  phi::funcs::SelectedRowsAddTo<phi::CPUContext, float> add_to_functor;
  add_to_functor(ctx, *selected_rows1, 0, output.get());
  add_to_functor(ctx, *selected_rows2, in1_value->numel(), 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<phi::DenseTensor> tensor1{new phi::DenseTensor()};
  tensor1->mutable_data<float>(phi::make_ddim({height, row_numel}), cpu_place);
  functor(ctx, tensor1.get(), 3.0);

  phi::funcs::SelectedRowsAddToTensor<phi::CPUContext, float>
      add_to_tensor_functor;
  add_to_tensor_functor(ctx, *output, tensor1.get());

  auto* tensor1_data = tensor1->data<float>();
  // row0: 1.0 + 2.0 + 3.0
  EXPECT_EQ(tensor1_data[0 * row_numel + 0], 6.0);
  // row1: 3.0
  EXPECT_EQ(tensor1_data[1 * row_numel + 1], 3.0);
  // row4 : 1.0 + 3.0
  EXPECT_EQ(tensor1_data[4 * row_numel + 6], 4.0);
  // row5: 2.0 + 3.0
  EXPECT_EQ(tensor1_data[5 * row_numel + 7], 5.0);
  // row6: 3.0
  EXPECT_EQ(tensor1_data[6 * row_numel + 1], 3.0);
  // row7: 1.0 + 2.0 + 3.0
  EXPECT_EQ(tensor1_data[7 * row_numel + 3], 6.0);
  // row9: 2.0 + 3.0
  EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
}

TEST(selected_rows_functor, cpu_merge_average_float) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> functor;
  int64_t height = 10;
  int64_t row_numel = 10;

  std::vector<int64_t> rows{0, 4, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows{
      new phi::SelectedRows(rows, height)};
  auto* in_value = selected_rows->mutable_value();
  in_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows.size()), row_numel}),
      cpu_place);
  functor(ctx, in_value, 1.0);

  phi::funcs::scatter::MergeAverage<phi::CPUContext, float>
      merge_average_functor;
  phi::SelectedRows output = merge_average_functor(ctx, *selected_rows);

  auto out_height = output.height();
  EXPECT_EQ(out_height, height);

  auto& out_rows = output.rows();
  EXPECT_EQ(out_rows[0], 0);
  EXPECT_EQ(out_rows[1], 4);
  EXPECT_EQ(out_rows[2], 7);

  auto* out_data = output.value().data<float>();

  EXPECT_EQ(out_data[0 * row_numel], 1.0);
  EXPECT_EQ(out_data[1 * row_numel], 2.0);
  EXPECT_EQ(out_data[2 * row_numel], 1.0);
}

TEST(selected_rows_functor, cpu_merge_add_float) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> functor;
  int64_t height = 10;
  int64_t row_numel = 10;

  std::vector<int64_t> rows{0, 4, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows{
      new phi::SelectedRows(rows, height)};
  auto* in_value = selected_rows->mutable_value();
  in_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows.size()), row_numel}),
      cpu_place);
  functor(ctx, in_value, 1.0);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};

  phi::funcs::scatter::MergeAdd<phi::CPUContext, float> merge_add_functor;
  merge_add_functor(ctx, *selected_rows, output.get());

  auto out_height = output->height();
  EXPECT_EQ(out_height, height);

  auto& out_rows = output->rows();
  EXPECT_EQ(out_rows[0], 0);
  EXPECT_EQ(out_rows[1], 4);
  EXPECT_EQ(out_rows[2], 7);

  auto* out_data = output->value().data<float>();

  EXPECT_EQ(out_data[0 * row_numel], 1.0);
  EXPECT_EQ(out_data[1 * row_numel], 2.0);
  EXPECT_EQ(out_data[2 * row_numel], 1.0);
}

TEST(selected_rows_functor, cpu_merge_add_int) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, int> functor;
  int64_t height = 10;
  int64_t row_numel = 10;

  std::vector<int64_t> rows{0, 4, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows{
      new phi::SelectedRows(rows, height)};
  auto* in_value = selected_rows->mutable_value();
  in_value->mutable_data<int>(
      phi::make_ddim({static_cast<int64_t>(rows.size()), row_numel}),
      cpu_place);
  functor(ctx, in_value, 1);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};

  phi::funcs::scatter::MergeAdd<phi::CPUContext, int> merge_add_functor;
  merge_add_functor(ctx, *selected_rows, output.get());

  auto out_height = output->height();
  EXPECT_EQ(out_height, height);

  auto& out_rows = output->rows();
  EXPECT_EQ(out_rows[0], 0);
  EXPECT_EQ(out_rows[1], 4);
  EXPECT_EQ(out_rows[2], 7);

  auto* out_data = output->value().data<int>();

  EXPECT_EQ(out_data[0 * row_numel], 1);
  EXPECT_EQ(out_data[1 * row_numel], 2);
  EXPECT_EQ(out_data[2 * row_numel], 1);
}

TEST(selected_rows_functor, cpu_merge_add_multi) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> set_const;

  int64_t height = 10;
  int64_t row_numel = 8;

  std::vector<int64_t> rows1{5, 2, 5, 3, 5};
  std::unique_ptr<phi::SelectedRows> selected_rows1{
      new phi::SelectedRows(rows1, height)};
  auto* in1_value = selected_rows1->mutable_value();
  in1_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows1.size()), row_numel}),
      cpu_place);
  set_const(ctx, in1_value, 1.0);

  std::vector<int64_t> rows2{2, 5, 3, 5, 3};
  std::unique_ptr<phi::SelectedRows> selected_rows2{
      new phi::SelectedRows(rows2, height)};
  auto* in2_value = selected_rows2->mutable_value();
  in2_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows2.size()), row_numel}),
      cpu_place);
  set_const(ctx, in2_value, 1.0);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};
  output->set_height(height);
  phi::funcs::scatter::MergeAdd<phi::CPUContext, float> merge_add_functor;

  std::vector<const phi::SelectedRows*> inputs;
  inputs.push_back(selected_rows1.get());
  inputs.push_back(selected_rows2.get());
  merge_add_functor(ctx, inputs, output.get());

  EXPECT_EQ(output->height(), height);
  EXPECT_EQ(output->value().dims(), phi::make_ddim({3, row_numel}));

  std::vector<int64_t> ret_rows{2, 3, 5};
  EXPECT_EQ(output->rows(), ret_rows);

  auto* out_data = output->value().data<float>();
  for (size_t i = 0; i < ret_rows.size(); ++i) {
    for (size_t j = 0; j < static_cast<size_t>(row_numel); ++j) {
      EXPECT_EQ(out_data[i * row_numel + j], ret_rows[i]);
    }
  }
}

TEST(selected_rows_functor, cpu_merge_add_multi_noduplicated) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> set_const;

  int64_t height = 10;
  int64_t row_numel = 8;

  std::vector<int64_t> rows1{1, 3, 5, 7, 9};
  std::unique_ptr<phi::SelectedRows> selected_rows1{
      new phi::SelectedRows(rows1, height)};
  auto* in1_value = selected_rows1->mutable_value();
  in1_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows1.size()), row_numel}),
      cpu_place);
  set_const(ctx, in1_value, 1.0);

  std::vector<int64_t> rows2{0, 2, 4, 6, 8};
  std::unique_ptr<phi::SelectedRows> selected_rows2{
      new phi::SelectedRows(rows2, height)};
  auto* in2_value = selected_rows2->mutable_value();
  in2_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows2.size()), row_numel}),
      cpu_place);
  set_const(ctx, in2_value, 2.0);

  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};
  output->set_height(height);
  phi::funcs::scatter::MergeAdd<phi::CPUContext, float> merge_add_functor;

  std::vector<const phi::SelectedRows*> inputs;
  inputs.push_back(selected_rows1.get());
  inputs.push_back(selected_rows2.get());
  merge_add_functor(ctx, inputs, output.get());

  EXPECT_EQ(output->height(), height);
  EXPECT_EQ(output->value().dims(), phi::make_ddim({10, row_numel}));

  std::vector<int64_t> ret_rows{1, 3, 5, 7, 9, 0, 2, 4, 6, 8};
  EXPECT_EQ(output->rows(), ret_rows);

  auto* out_data = output->value().data<float>();
  for (size_t i = 0; i < ret_rows.size(); ++i) {
    float data_value = 0;
    if (i < 5) {
      data_value = 1.0;
    } else {
      data_value = 2.0;
    }
    for (size_t j = 0; j < static_cast<size_t>(row_numel); ++j) {
      EXPECT_EQ(out_data[i * row_numel + j], data_value);
    }
  }
}

TEST(selected_rows_functor, cpu_sum_to) {
  paddle::platform::CPUPlace cpu_place;
  phi::CPUContext ctx(cpu_place);
  ctx.SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
                       .GetAllocator(cpu_place)
                       .get());
  phi::funcs::SetConstant<phi::CPUContext, float> functor;
  int64_t height = 10;
  int64_t row_numel = 10;
  std::vector<int64_t> rows1{0, 4, 7};
  std::unique_ptr<phi::SelectedRows> selected_rows1{
      new phi::SelectedRows(rows1, height)};
  auto* in1_value = selected_rows1->mutable_value();
  in1_value->mutable_data<float>(
      phi::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<phi::SelectedRows> selected_rows2{
      new phi::SelectedRows(rows2, height)};
  auto* in2_value = selected_rows2->mutable_value();
  in2_value->mutable_data<float>(
      phi::make_ddim({static_cast<int64_t>(rows2.size()), row_numel}),
      cpu_place);

  functor(ctx, in2_value, 2.0);
  std::unique_ptr<phi::SelectedRows> output{new phi::SelectedRows()};
  output->set_height(height);
  auto* out_value = output->mutable_value();
  // simplely concat two SelectedRows
  out_value->mutable_data<float>(phi::make_ddim({7, 10}), cpu_place);
  phi::funcs::SelectedRowsSumTo<phi::CPUContext, float> sum_to_functor;
  sum_to_functor(ctx,
                 std::vector<phi::SelectedRows*>(
                     {selected_rows1.get(), selected_rows2.get()}),
                 std::vector<int64_t>({0, in1_value->numel()}),
                 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<phi::DenseTensor> tensor1{new phi::DenseTensor()};
  tensor1->mutable_data<float>(phi::make_ddim({height, row_numel}), cpu_place);
  functor(ctx, tensor1.get(), 3.0);
  phi::funcs::SelectedRowsAddToTensor<phi::CPUContext, float>
      add_to_tensor_functor;
  add_to_tensor_functor(ctx, *output, tensor1.get());
  auto* tensor1_data = tensor1->data<float>();
  // row0: 1.0 + 2.0 + 3.0
  EXPECT_EQ(tensor1_data[0 * row_numel + 0], 6.0);
  // row1: 3.0
  EXPECT_EQ(tensor1_data[1 * row_numel + 1], 3.0);
  // row4 : 1.0 + 3.0
  EXPECT_EQ(tensor1_data[4 * row_numel + 6], 4.0);
  // row5: 2.0 + 3.0
  EXPECT_EQ(tensor1_data[5 * row_numel + 7], 5.0);
  // row6: 3.0
  EXPECT_EQ(tensor1_data[6 * row_numel + 1], 3.0);
  // row7: 1.0 + 2.0 + 3.0
  EXPECT_EQ(tensor1_data[7 * row_numel + 3], 6.0);
  // row9: 2.0 + 3.0
  EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
}
