// Copyright (c) 2019 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/ir/mkldnn/cpu_quantize_pass.h"
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/string/pretty_log.h"

namespace paddle {
namespace framework {
namespace ir {

namespace {

void UnlinkNodes(ir::Node* a, ir::Node* b) {
  a->outputs.erase(std::remove(a->outputs.begin(), a->outputs.end(), b),
                   a->outputs.end());
  b->inputs.erase(std::remove(b->inputs.begin(), b->inputs.end(), a),
                  b->inputs.end());
}

}  // namespace

enum { U8_MAX = 255, S8_MAX = 127 };

using EigenVectorArrayMap = Eigen::Map<Eigen::Array<double, Eigen::Dynamic, 1>>;
using string::PrettyLogDetail;

void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
                                    std::string input_name, double scale_to_one,
                                    bool is_unsigned,
                                    std::string scale_attr_name) const {
  unsigned max = is_unsigned ? U8_MAX : S8_MAX;
  float scale = scale_to_one * max;

  // Create quantize output variable
  VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
  auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);

  // create a quantize op node
  OpDesc q_desc;
  q_desc.SetType("quantize");
  q_desc.SetInput("Input", std::vector<std::string>({input->Name()}));
  q_desc.SetOutput("Output",
                   std::vector<std::string>({quantize_out_node->Name()}));
  q_desc.SetAttr("Scale", scale);
  q_desc.SetAttr("is_negative_input", !is_unsigned);
  auto quantize_op = g->CreateOpNode(&q_desc);  // OpDesc will be copied.

  // update op's input
  op->Op()->SetInput(input_name,
                     std::vector<std::string>({quantize_out_node->Name()}));

  // link quantize op
  UnlinkNodes(input, op);
  IR_NODE_LINK_TO(input, quantize_op);
  IR_NODE_LINK_TO(quantize_op, quantize_out_node);
  IR_NODE_LINK_TO(quantize_out_node, op);

  if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
}

void CPUQuantizePass::DequantizeOutput(Graph* g, Node* op, Node* output,
                                       std::string output_name,
                                       double scale_to_one, bool is_unsigned,
                                       std::string scale_attr_name) const {
  unsigned max = is_unsigned ? U8_MAX : S8_MAX;
  float scale = scale_to_one * max;

  // Create dequantize input variable
  VarDesc dequantize_in_desc(patterns::PDNodeName("dequantize", "in"));
  auto* dequantize_in_node = g->CreateVarNode(&dequantize_in_desc);

  // create a dequantize op node for output.
  OpDesc deq_desc;
  deq_desc.SetType("dequantize");
  deq_desc.SetInput("Input",
                    std::vector<std::string>({dequantize_in_node->Name()}));
  deq_desc.SetOutput("Output", std::vector<std::string>({output->Name()}));
  deq_desc.SetAttr("Scale", scale);
  auto dequantize_op = g->CreateOpNode(&deq_desc);  // OpDesc will be copied.

  // update op's output
  op->Op()->SetOutput(output_name,
                      std::vector<std::string>({dequantize_in_node->Name()}));

  // link dequantize op
  UnlinkNodes(op, output);
  IR_NODE_LINK_TO(op, dequantize_in_node);
  IR_NODE_LINK_TO(dequantize_in_node, dequantize_op);
  IR_NODE_LINK_TO(dequantize_op, output);

  if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
}

void CPUQuantizePass::QuantizeConv(Graph* graph,
                                   bool with_residual_data) const {
  GraphPatternDetector gpd;
  auto pattern = gpd.mutable_pattern();
  patterns::ConvResidual conv_pattern{pattern, name_scope_};
  conv_pattern(with_residual_data);

  int quantize_conv_count = 0;
  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                     Graph* g) {
    VLOG(4) << "Quantize conv2d op";
    GET_IR_NODE_FROM_SUBGRAPH(conv_op, conv_op, conv_pattern);
    auto* conv_op_desc = conv_op->Op();

    // skip if should not be quantized
    if (!conv_op_desc->HasAttr("use_quantizer") ||
        !boost::get<bool>(conv_op_desc->GetAttr("use_quantizer")))
      return;

    GET_IR_NODE_FROM_SUBGRAPH(conv_filter, conv_filter, conv_pattern);
    GET_IR_NODE_FROM_SUBGRAPH(conv_input, conv_input, conv_pattern);
    GET_IR_NODE_FROM_SUBGRAPH(conv_output, conv_output, conv_pattern);

    // get scales calculated after warmup, they scale variables to MAX=1.0
    auto scales = Get<VarQuantScale>("quant_var_scales");

    auto input_scale = scales[conv_input->Name()].second.data<double>()[0];
    bool is_input_unsigned = scales[conv_input->Name()].first;
    QuantizeInput(g, conv_op, conv_input, "Input", input_scale,
                  is_input_unsigned, "Scale_in");

    auto filter_scale_tensor = scales[conv_filter->Name()].second;
    EigenVectorArrayMap eigen_tensor{filter_scale_tensor.data<double>(),
                                     filter_scale_tensor.numel(), 1};
    eigen_tensor *= static_cast<double>(S8_MAX);
    std::vector<float> filter_scale{
        filter_scale_tensor.data<double>(),
        filter_scale_tensor.data<double>() + filter_scale_tensor.numel()};

    conv_op->Op()->SetAttr("Scale_weights", filter_scale);

    if (with_residual_data) {
      GET_IR_NODE_FROM_SUBGRAPH(conv_residual_data, conv_residual_data,
                                conv_pattern);
      auto residual_scale =
          scales[conv_residual_data->Name()].second.data<double>()[0];
      bool is_residual_unsigned = scales[conv_residual_data->Name()].first;

      QuantizeInput(g, conv_op, conv_residual_data, "ResidualData",
                    residual_scale, is_residual_unsigned, "Scale_in_eltwise");
    }

    auto output_scale = scales[conv_output->Name()].second.data<double>()[0];
    bool is_output_unsigned = scales[conv_output->Name()].first;
    DequantizeOutput(g, conv_op, conv_output, "Output", output_scale,
                     is_output_unsigned, "Scale_out");

    ++quantize_conv_count;
  };

  gpd(graph, handler);
  AddStatis(quantize_conv_count);

  std::stringstream msg_ss;
  msg_ss << "---    quantized " << quantize_conv_count << " conv2d ops";
  if (with_residual_data) msg_ss << " with residual connection";
  PrettyLogDetail(msg_ss.str().c_str());
}

void CPUQuantizePass::QuantizePool(Graph* graph) const {
  GraphPatternDetector gpd;
  auto pattern = gpd.mutable_pattern();
  patterns::Pool pool_pattern{pattern, name_scope_};
  pool_pattern();

  int quantize_pool_count = 0;
  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                     Graph* g) {
    VLOG(4) << "Quantize pool2d op";
    GET_IR_NODE_FROM_SUBGRAPH(pool_op, pool_op, pool_pattern);
    auto* pool_op_desc = pool_op->Op();

    // skip if should not be quantized
    if (!pool_op_desc->HasAttr("use_quantizer") ||
        !boost::get<bool>(pool_op_desc->GetAttr("use_quantizer")))
      return;

    GET_IR_NODE_FROM_SUBGRAPH(pool_input, pool_input, pool_pattern);
    GET_IR_NODE_FROM_SUBGRAPH(pool_output, pool_output, pool_pattern);

    // get scales calculated after warmup, they scale variables to MAX=1.0
    auto scales = Get<VarQuantScale>("quant_var_scales");

    auto input_scale = scales[pool_input->Name()].second.data<double>()[0];
    bool is_input_unsigned = scales[pool_input->Name()].first;
    QuantizeInput(g, pool_op, pool_input, "X", input_scale, is_input_unsigned);

    auto output_scale = scales[pool_output->Name()].second.data<double>()[0];
    bool is_output_unsigned = scales[pool_output->Name()].first;
    DequantizeOutput(g, pool_op, pool_output, "Out", output_scale,
                     is_output_unsigned);

    ++quantize_pool_count;
  };

  gpd(graph, handler);
  AddStatis(quantize_pool_count);

  PrettyLogDetail("---    quantized %d pool2d ops", quantize_pool_count);
}

void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
  VLOG(3) << "Quantizing the graph.";
  PADDLE_ENFORCE(graph);
  FusePassBase::Init(name_scope_, graph);

  PADDLE_ENFORCE(param_scope());

  QuantizeConv(graph, false /* with_residual_data */);
  QuantizeConv(graph, true /* with_residual_data */);
  QuantizePool(graph);
}

}  // namespace ir
}  // namespace framework
}  // namespace paddle

REGISTER_PASS(cpu_quantize_pass, paddle::framework::ir::CPUQuantizePass)
    .RequirePassAttr("quant_var_scales");
