/* Copyright (c) 2023 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 <glog/logging.h>
#include <gtest/gtest.h>
#include <fstream>
#include <iostream>
#include <unordered_map>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/platform/enforce.h"

using std::pair;
using std::string;
using std::unordered_map;

PD_DEFINE_bool(enable_mkldnn, true, "Enable MKLDNN");

namespace paddle {
namespace pass {

using VarQuantScale =
    std::unordered_map<std::string, std::pair<bool, phi::DenseTensor>>;

static float const SCALE = 2.f;
const std::vector<std::string> PreGraphPasses({
    "conv_activation_mkldnn_fuse_pass",
    "cpu_quantize_placement_pass",
    "cpu_quantize_pass",
});

TEST(cpuQuantizePass, ConvReLU6) {
  paddle::framework::ProgramDesc prog;
  auto* block = prog.MutableBlock(0);

  auto* conv2d_op = block->AppendOp();
  conv2d_op->SetType("conv2d");
  conv2d_op->SetInput("Input", {"conv2d-X"});
  conv2d_op->SetInput("Filter", {"conv2d-Y"});
  conv2d_op->SetOutput("Output", {"conv2d-Out"});

  const std::vector<int> strides({1, 1});
  const std::vector<int> paddings({1, 1});
  const std::vector<int> dilations({1, 1});
  const int groups = 1;

  conv2d_op->SetAttr("strides", strides);
  conv2d_op->SetAttr("paddings", paddings);
  conv2d_op->SetAttr("dilations", dilations);
  conv2d_op->SetAttr("groups", groups);

  auto* relu6_op = block->AppendOp();
  relu6_op->SetType("relu6");
  relu6_op->SetAttr("threshold", 6.f);
  relu6_op->SetInput("X", {"conv2d-Out"});
  relu6_op->SetOutput("Out", {"relu-Out"});

  auto place = phi::CPUPlace();
  VarQuantScale* scales = new VarQuantScale();
  phi::DenseTensor scale_input_tensor;
  phi::DenseTensor scale_weight_tensor;
  scale_input_tensor.Resize({1});
  scale_weight_tensor.Resize({1});
  auto* ptr_scale_input = scale_input_tensor.mutable_data<double>(place);
  auto* ptr_scale_weight = scale_weight_tensor.mutable_data<double>(place);
  ptr_scale_input[0] = SCALE;
  ptr_scale_weight[0] = SCALE;

  (*scales)["conv2d-X"] = std::make_pair(false, std::move(scale_input_tensor));
  (*scales)["conv2d-Y"] = std::make_pair(false, std::move(scale_weight_tensor));

  paddle::framework::Scope scope;

  std::unique_ptr<paddle::framework::ir::Graph> graph(
      new paddle::framework::ir::Graph(prog));
  (graph)->SetNotOwned(paddle::framework::ir::kParamScopeAttr, &scope);

  for (const auto& pass : PreGraphPasses) {
    auto pass_ = paddle::framework::ir::PassRegistry::Instance().Get(pass);
    if (pass == "cpu_quantize_pass") {
      pass_->Set("quant_var_scales", scales);
    }
    graph.reset(pass_->Apply(graph.release()));
  }
  int fused_conv2d_num = 0;
  for (auto* node : graph->Nodes()) {
    if (node->IsOp() && node->Op() && node->Op()->Type() == "fused_conv2d") {
      CHECK_EQ(node->Op()->GetAttrIfExists<float>("fuse_beta"), 6)
          << "Attr fuse_beta must equal to 6.";
      fused_conv2d_num++;
    }
  }
  CHECK_GT(fused_conv2d_num, 0) << "Graph must contain fused_conv2d";
}

}  // namespace pass
}  // namespace paddle

USE_PASS(conv_activation_mkldnn_fuse_pass);
USE_PASS(cpu_quantize_placement_pass);
USE_PASS(cpu_quantize_pass);