// 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 "lite/api/light_api.h"
#include <algorithm>
#include <unordered_map>
#include "paddle_use_kernels.h"  // NOLINT
#include "paddle_use_ops.h"      // NOLINT

namespace paddle {
namespace lite {

void LightPredictor::Build(const std::string& lite_model_file,
                           bool model_from_memory) {
  if (model_from_memory) {
    LoadModelNaiveFromMemory(lite_model_file, scope_.get(), &cpp_program_desc_);
  } else {
    LoadModelNaiveFromFile(lite_model_file, scope_.get(), &cpp_program_desc_);
  }

  // For weight quantization of post training, load the int8/16 weights
  // for optimized model, and dequant it to fp32.
  DequantizeWeight();

  BuildRuntimeProgram(cpp_program_desc_);
  PrepareFeedFetch();
}

void LightPredictor::Build(const std::string& model_dir,
                           const std::string& model_buffer,
                           const std::string& param_buffer,
                           lite_api::LiteModelType model_type,
                           bool model_from_memory) {
  switch (model_type) {
#ifndef LITE_ON_TINY_PUBLISH
    case lite_api::LiteModelType::kProtobuf:
      LoadModelPb(model_dir, "", "", scope_.get(), &cpp_program_desc_);
      break;
#endif
    case lite_api::LiteModelType::kNaiveBuffer: {
      if (model_from_memory) {
        LoadModelNaiveFromMemory(
            model_buffer, param_buffer, scope_.get(), &cpp_program_desc_);
      } else {
        LoadModelNaive(model_dir, scope_.get(), &cpp_program_desc_);
      }
      break;
    }
    default:
      LOG(FATAL) << "Unknown model type";
  }

  DequantizeWeight();
  BuildRuntimeProgram(cpp_program_desc_);
  PrepareFeedFetch();
}

Tensor* LightPredictor::GetInput(size_t offset) {
  CHECK(input_names_.size() > offset)
      << "The network has " << input_names_.size() << " inputs"
      << ", the offset should be less than this.";
  auto* in_var = program_->exec_scope()->FindVar(input_names_[offset]);
  CHECK(in_var) << "no fatch variable " << input_names_[offset]
                << " in exec_scope";
  return in_var->GetMutable<lite::Tensor>();
}

// get input by name
Tensor* LightPredictor::GetInputByName(const std::string& name) {
  auto element = std::find(input_names_.begin(), input_names_.end(), name);
  if (element == input_names_.end()) {
    LOG(ERROR) << "Model do not have input named with: [" << name
               << "], model's inputs include:";
    for (int i = 0; i < input_names_.size(); i++) {
      LOG(ERROR) << "[" << input_names_[i] << "]";
    }
    return nullptr;
  } else {
    int position = std::distance(input_names_.begin(), element);
    return GetInput(position);
  }
}

const Tensor* LightPredictor::GetOutput(size_t offset) {
  CHECK(output_names_.size() > offset)
      << "The network has " << output_names_.size() << " outputs"
      << ", the offset should be less than this.";
  auto* out_var = program_->exec_scope()->FindVar(output_names_.at(offset));
  CHECK(out_var) << "no fatch variable " << output_names_.at(offset)
                 << " in exec_scope";
  return out_var->GetMutable<lite::Tensor>();
}
// get inputs names
std::vector<std::string> LightPredictor::GetInputNames() {
  return input_names_;
}
// get outputnames
std::vector<std::string> LightPredictor::GetOutputNames() {
  return output_names_;
}
// append the names of inputs and outputs into input_names_ and output_names_
void LightPredictor::PrepareFeedFetch() {
  auto current_block = cpp_program_desc_.GetBlock<cpp::BlockDesc>(0);
  std::vector<cpp::OpDesc*> feeds;
  std::vector<cpp::OpDesc*> fetchs;
  for (int i = 0; i < current_block->OpsSize(); i++) {
    auto op = current_block->GetOp<cpp::OpDesc>(i);
    if (op->Type() == "feed") {
      feeds.push_back(op);
    } else if (op->Type() == "fetch") {
      fetchs.push_back(op);
    }
  }
  input_names_.resize(feeds.size());
  output_names_.resize(fetchs.size());
  for (int i = 0; i < feeds.size(); i++) {
    input_names_[feeds[i]->GetAttr<int>("col")] =
        feeds[i]->Output("Out").front();
  }
  for (int i = 0; i < fetchs.size(); i++) {
    output_names_[fetchs[i]->GetAttr<int>("col")] =
        fetchs[i]->Input("X").front();
  }
}

void LightPredictor::BuildRuntimeProgram(const cpp::ProgramDesc& prog) {
  std::vector<Instruction> insts;
  // 1. Create op first
  Program program(prog, scope_, {});

// 2. Create Instructs
#ifdef LITE_WITH_OPENCL
  using WaitListType =
      std::unordered_map<decltype(static_cast<const void*>(nullptr)),
                         std::shared_ptr<cl::Event>>;
  using OpenCLContext = Context<TargetType::kOpenCL>;
  std::unique_ptr<KernelContext> local_ctx(new KernelContext());
  local_ctx->As<OpenCLContext>().InitOnce();
#endif

  // Create the kernels of the target places, and filter out the specific
  // kernel with the target alias.
  for (auto& op : program.ops()) {
    auto kernel_type = op->op_info()->GetAttr<std::string>(kKernelTypeAttr);
    std::string op_type, alias;
    Place place;
    KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place);
    auto kernels = op->CreateKernels({place});
    // filter out a kernel
    auto it = std::find_if(
        kernels.begin(), kernels.end(), [&](std::unique_ptr<KernelBase>& it) {
          return it->alias() == alias;
        });
    CHECK(it != kernels.end());

#ifdef LITE_WITH_OPENCL
    if ((*it)->target() == TARGET(kOpenCL)) {
      std::unique_ptr<KernelContext> ctx(new KernelContext());
      (*local_ctx).As<OpenCLContext>().CopySharedTo(&ctx->As<OpenCLContext>());
      (*it)->SetContext(std::move(ctx));
    } else {
      (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
    }
#else
    (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
#endif

    insts.emplace_back(op, std::move(*it));
  }
  program_.reset(new RuntimeProgram(std::move(insts)));

  CHECK(program.exec_scope());
  program_->set_exec_scope(program.exec_scope());
}

void LightPredictor::DequantizeWeight() {
#define PROCESS_CONV2D_DATA()                                             \
  for (int64_t i = 0; i < ch; ++i) {                                      \
    for (int64_t j = 0; j < offset; ++j) {                                \
      fp_data[i * offset + j] = scale_list[i] * int_data[i * offset + j]; \
    }                                                                     \
  }

#define PROCESS_FC_DATA()                                               \
  for (int64_t i = 0; i < chin; i++) {                                  \
    for (int64_t j = 0; j < chout; j++) {                               \
      fp_data[i * chout + j] = scale_list[j] * int_data[i * chout + j]; \
    }                                                                   \
  }

  auto is_weight_quantized_op = [](const cpp::OpDesc* op_desc) {
    bool result = false;
    if (op_desc->HasAttr("quantization_type")) {
      std::string type = op_desc->GetAttr<std::string>("quantization_type");
      result = (type == "post_weight_abs_max") ||
               (type == "post_weight_channel_wise_abs_max");
    } else {
      result = op_desc->HasAttr("quantize_weight_bits");
    }
    return result;
  };

  Tensor tmp_tensor;
  for (size_t i = 0; i < cpp_program_desc_.BlocksSize(); i++) {
    auto* block = cpp_program_desc_.GetBlock<cpp::BlockDesc>(i);
    for (size_t k = 0; k < block->OpsSize(); ++k) {
      auto* op_desc = block->GetOp<cpp::OpDesc>(k);
      if (is_weight_quantized_op(op_desc)) {
        auto input_names = op_desc->input_vars();
        for (auto& input_name : input_names) {
          std::string input_scale_name = input_name + "_quant_scale";
          if (op_desc->HasAttr(input_scale_name)) {  // the input is quantized
            auto input_tensor =
                scope_->FindVar(input_name)->GetMutable<lite::Tensor>();
            tmp_tensor.CopyDataFrom(*input_tensor);
            auto scale_list =
                op_desc->GetAttr<std::vector<float>>(input_scale_name);

            int quantize_weight_bits =
                op_desc->GetAttr<int>("quantize_weight_bits");
            CHECK(quantize_weight_bits == 8 || quantize_weight_bits == 16);
            float* fp_data = input_tensor->mutable_data<float>();

            std::string op_type = op_desc->Type();
            if (op_type == "conv2d" || op_type == "depthwise_conv2d") {
              int64_t ch = input_tensor->dims()[0];
              int64_t offset = input_tensor->numel() / ch;
              CHECK_EQ(scale_list.size(), ch);
              if (quantize_weight_bits == 8) {
                const int8_t* int_data = tmp_tensor.data<int8_t>();
                PROCESS_CONV2D_DATA()
              } else {
                const int16_t* int_data = tmp_tensor.data<int16_t>();
                PROCESS_CONV2D_DATA()
              }
            } else if (op_type == "fc" || op_type == "mul") {
              int64_t chin = input_tensor->dims()[0];
              int64_t chout = input_tensor->dims()[1];
              CHECK_EQ(scale_list.size(), chout);
              if (quantize_weight_bits == 8) {
                const int8_t* int_data = tmp_tensor.data<int8_t>();
                PROCESS_FC_DATA()
              } else {
                const int16_t* int_data = tmp_tensor.data<int16_t>();
                PROCESS_FC_DATA()
              }
            }
          }
        }
      }
    }
  }

#undef PROCESS_CONV2D_DATA
#undef PROCESS_FC_DATA
}

}  // namespace lite
}  // namespace paddle