//   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/framework/details/broadcast_op_handle.h"
#include "gtest/gtest.h"

#include "paddle/fluid/platform/device_context.h"

namespace paddle {
namespace framework {
namespace details {

namespace f = paddle::framework;
namespace p = paddle::platform;

// test data amount
const f::DDim kDims = {20, 20};

struct TestBroadcastOpHandle {
  std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
  std::vector<Scope*> local_scopes_;
  Scope g_scope_;
  std::unique_ptr<OpHandleBase> op_handle_;
  std::vector<std::unique_ptr<VarHandleBase>> vars_;
  std::vector<p::Place> gpu_list_;

  void WaitAll() {
    for (size_t j = 0; j < ctxs_.size(); ++j) {
      ctxs_[j]->Wait();
    }
  }

  void InitCtxOnGpu(bool use_gpu) {
    if (use_gpu) {
#ifdef PADDLE_WITH_CUDA
      int count = p::GetCUDADeviceCount();
      if (count <= 1) {
        LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
                        "device count is "
                     << count;
        exit(0);
      }
      for (int i = 0; i < count; ++i) {
        auto p = p::CUDAPlace(i);
        gpu_list_.push_back(p);
        ctxs_.emplace_back(new p::CUDADeviceContext(p));
      }
#else
      PADDLE_THROW("CUDA is not support.");
#endif
    } else {
      int count = 8;
      for (int i = 0; i < count; ++i) {
        auto p = p::CPUPlace();
        gpu_list_.push_back(p);
        ctxs_.emplace_back(new p::CPUDeviceContext(p));
      }
    }
  }

  void InitBroadcastOp(size_t input_scope_idx) {
    for (size_t j = 0; j < gpu_list_.size(); ++j) {
      local_scopes_.push_back(&(g_scope_.NewScope()));
      local_scopes_[j]->Var("out");
    }
    local_scopes_[input_scope_idx]->Var("input");

    op_handle_.reset(new BroadcastOpHandle(local_scopes_, gpu_list_));
    auto* in_var_handle =
        new VarHandle(1, input_scope_idx, "input", gpu_list_[input_scope_idx]);
    vars_.emplace_back(in_var_handle);
    op_handle_->AddInput(in_var_handle);

    // add dummy var
    vars_.emplace_back(new DummyVarHandle());
    DummyVarHandle* dummy_var_handle =
        static_cast<DummyVarHandle*>(vars_.back().get());
    dummy_var_handle->generated_op_ = nullptr;
    op_handle_->AddInput(dummy_var_handle);

    for (size_t j = 0; j < gpu_list_.size(); ++j) {
      op_handle_->SetDeviceContext(gpu_list_[j], ctxs_[j].get());
      VarHandle* out_var_handle = new VarHandle(2, j, "out", gpu_list_[j]);
      vars_.emplace_back(out_var_handle);
      op_handle_->AddOutput(out_var_handle);
    }

    // add dummy var
    vars_.emplace_back(new DummyVarHandle());
    DummyVarHandle* out_dummy_var_handle =
        static_cast<DummyVarHandle*>(vars_.back().get());
    out_dummy_var_handle->generated_op_ = nullptr;
    op_handle_->AddOutput(out_dummy_var_handle);
  }

  void TestBroadcastLodTensor(size_t input_scope_idx) {
    auto in_var = local_scopes_[input_scope_idx]->Var("input");
    auto in_lod_tensor = in_var->GetMutable<f::LoDTensor>();
    in_lod_tensor->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);

    std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
    for (size_t k = 0; k < send_vector.size(); ++k) {
      send_vector[k] = k;
    }
    f::LoD lod{{0, 10, 20}};
    paddle::framework::TensorFromVector<float>(
        send_vector, *(ctxs_[input_scope_idx]), in_lod_tensor);
    in_lod_tensor->set_lod(lod);

    op_handle_->Run(false);

    WaitAll();

    p::CPUPlace cpu_place;
    for (size_t j = 0; j < gpu_list_.size(); ++j) {
      auto out_var = local_scopes_[j]->Var("out");
      auto out_tensor = out_var->Get<f::LoDTensor>();
      PADDLE_ENFORCE_EQ(out_tensor.lod(), lod, "lod is not equal.");

      f::Tensor result_tensor;
      f::TensorCopy(out_tensor, cpu_place, *(ctxs_[j]), &result_tensor);
      float* ct = result_tensor.mutable_data<float>(cpu_place);

      for (int64_t i = 0; i < f::product(kDims); ++i) {
        ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
      }
    }
  }

  void TestBroadcastSelectedRows(size_t input_scope_idx) {
    auto in_var = local_scopes_[input_scope_idx]->Var("input");
    auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
    auto value = in_selected_rows->mutable_value();
    value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
    int height = static_cast<int>(kDims[0]) * 2;
    std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
                              2, 4, 6, 3, 1, 1, 1,  1, 3, 7};
    in_selected_rows->set_height(height);
    in_selected_rows->set_rows(rows);

    std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
    for (size_t k = 0; k < send_vector.size(); ++k) {
      send_vector[k] = k;
    }
    paddle::framework::TensorFromVector<float>(
        send_vector, *(ctxs_[input_scope_idx]), value);

    op_handle_->Run(false);

    WaitAll();

    p::CPUPlace cpu_place;
    for (size_t j = 0; j < gpu_list_.size(); ++j) {
      auto out_var = local_scopes_[j]->Var("out");
      auto& out_select_rows = out_var->Get<f::SelectedRows>();
      auto rt = out_select_rows.value();

      PADDLE_ENFORCE_EQ(out_select_rows.height(), height,
                        "height is not equal.");
      for (size_t k = 0; k < out_select_rows.rows().size(); ++k) {
        PADDLE_ENFORCE_EQ(out_select_rows.rows()[k], rows[k]);
      }

      f::Tensor result_tensor;
      f::TensorCopy(rt, cpu_place, *(ctxs_[j]), &result_tensor);
      float* ct = result_tensor.data<float>();

      for (int64_t i = 0; i < f::product(kDims); ++i) {
        ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
      }
    }
  }
};

TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
  TestBroadcastOpHandle test_op;
  size_t input_scope_idx = 0;
  test_op.InitCtxOnGpu(false);
  test_op.InitBroadcastOp(input_scope_idx);
  test_op.TestBroadcastLodTensor(input_scope_idx);
}

TEST(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
  TestBroadcastOpHandle test_op;
  size_t input_scope_idx = 0;
  test_op.InitCtxOnGpu(false);
  test_op.InitBroadcastOp(input_scope_idx);
  test_op.TestBroadcastSelectedRows(input_scope_idx);
}

#ifdef PADDLE_WITH_CUDA
TEST(BroadcastTester, TestGPUBroadcastTestLodTensor) {
  TestBroadcastOpHandle test_op;
  size_t input_scope_idx = 0;
  test_op.InitCtxOnGpu(true);
  test_op.InitBroadcastOp(input_scope_idx);
  test_op.TestBroadcastLodTensor(input_scope_idx);
}

TEST(BroadcastTester, TestGPUBroadcastTestSelectedRows) {
  TestBroadcastOpHandle test_op;
  size_t input_scope_idx = 0;
  test_op.InitCtxOnGpu(true);
  test_op.InitBroadcastOp(input_scope_idx);
  test_op.TestBroadcastSelectedRows(input_scope_idx);
}
#endif

}  // namespace details
}  // namespace framework
}  // namespace paddle