// 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/paddle_place.h"
#include "lite/core/kernel.h"
#include "lite/core/op_registry.h"
#include "lite/core/target_wrapper.h"
#include "lite/core/type_system.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace fpga {

using float16 = zynqmp::float16;

void CopyFromHostSync(void* target, const void* source, size_t size) {
  TargetWrapper<TARGET(kFPGA)>::MemcpySync(
      target, source, size, IoDirection::HtoD);
}

void CopyToHostSync(void* target, const void* source, size_t size) {
  TargetWrapper<TARGET(kFPGA)>::MemcpySync(
      target, source, size, IoDirection::DtoH);
}

/*
 * This kernel copies a tensor from host to FPGA space.
 */
class IoCopyHostToFpgaCompute
    : public KernelLite<TARGET(kFPGA), PRECISION(kAny), DATALAYOUT(kAny)> {
 public:
  void Run() override {
    auto& param = Param<operators::IoCopyParam>();
    CHECK(param.x->target() == TARGET(kHost) ||
          param.x->target() == TARGET(kFPGA));
    param.y->mutable_data<float16>();
    if (param.x->ZynqTensor()->aligned() &&
        param.x->ZynqTensor()->shape().shouldAlign()) {
      zynqmp::Tensor tempTensor;
      tempTensor.mutableData<float16>(zynqmp::FP16,
                                      param.x->ZynqTensor()->shape());
      tempTensor.copyFrom(param.x->ZynqTensor());
      tempTensor.setAligned(true);
      tempTensor.unalignImage();
      param.y->ZynqTensor()->copyFrom(&tempTensor);
    } else {
      param.y->ZynqTensor()->copyFrom(param.x->ZynqTensor());
    }
    param.y->ZynqTensor()->invalidate();
    param.y->ZynqTensor()->copyScaleFrom(param.x->ZynqTensor());
    auto out_lod = param.y->mutable_lod();
    *out_lod = param.x->lod();
  }

  std::unique_ptr<type_infer_handler_t> GetTypeInferHandler() override {
    std::unique_ptr<type_infer_handler_t> res(new type_infer_handler_t);
    *res = [](const std::map<std::string, const Type*>& inputs,
              const std::string& out) -> const Type* {
      CHECK(!inputs.empty());
      auto* type = inputs.at("Input");
      CHECK(type->target() == TARGET(kHost));

      auto out_place = type->place();
      out_place.target = TARGET(kFPGA);
      auto* out_type = Type::Get(type->id(),
                                 out_place.target,
                                 out_place.precision,
                                 out_place.layout,
                                 out_place.device);
      return out_type;
    };
    return res;
  }

  std::string doc() const override { return "Copy IO from HOST to FPGA"; }
};

/*
 * This kernel copies a tensor from FPGA to host space.
 */
class IoCopyFpgaToHostCompute
    : public KernelLite<TARGET(kFPGA), PRECISION(kAny), DATALAYOUT(kAny)> {
 public:
  void Run() override {
    auto& param = Param<operators::IoCopyParam>();
    CHECK(param.x->target() == TARGET(kHost) ||
          param.x->target() == TARGET(kFPGA));

    param.y->mutable_data<float>();
    param.y->ZynqTensor()->setDataType(zynqmp::FP32);
    param.x->ZynqTensor()->syncToDevice();

    if (param.x->ZynqTensor()->aligned() &&
        param.x->ZynqTensor()->shape().shouldAlign()) {
      zynqmp::Tensor tempTensor;
      tempTensor.mutableData<float16>(zynqmp::FP16,
                                      param.x->ZynqTensor()->shape());
      tempTensor.copyFrom(param.x->ZynqTensor());
      tempTensor.setAligned(true);
      tempTensor.unalignImage();
      param.y->ZynqTensor()->copyFrom(&tempTensor);
    } else {
      param.y->ZynqTensor()->copyFrom(param.x->ZynqTensor());
    }
    param.y->ZynqTensor()->copyScaleFrom(param.x->ZynqTensor());
    param.y->ZynqTensor()->flush();
    auto out_lod = param.y->mutable_lod();
    *out_lod = param.x->lod();
  }
  std::string doc() const override { return "Copy IO from FPGA to HOST"; }
};

void hwc_to_chw(float* chw_data,
                float* hwc_data,
                int num,
                int channel,
                int height,
                int width) {
  int chw = channel * height * width;
  int wc = width * channel;
  int wh = width * height;
  int index = 0;
  for (int n = 0; n < num; n++) {
    for (int h = 0; h < height; h++) {
      for (int w = 0; w < width; w++) {
        for (int c = 0; c < channel; c++) {
          chw_data[n * chw + c * wh + h * width + w] = hwc_data[index];
          index++;
        }
      }
    }
  }
}

class IoCopyFpgaToHostCHWCompute
    : public KernelLite<TARGET(kFPGA), PRECISION(kAny), DATALAYOUT(kAny)> {
 public:
  void Run() override {
    auto& param = Param<operators::IoCopyParam>();
    CHECK(param.x->target() == TARGET(kHost) ||
          param.x->target() == TARGET(kFPGA));

    Tensor hwc;
    hwc.Resize(param.y->dims());
    float* hwc_data = hwc.mutable_data<float>();

    float* chw_data = param.y->mutable_data<float>();
    param.y->ZynqTensor()->setDataType(zynqmp::FP32);
    param.x->ZynqTensor()->syncToDevice();

    if (param.x->ZynqTensor()->aligned() &&
        param.x->ZynqTensor()->shape().shouldAlign()) {
      zynqmp::Tensor tempTensor;
      tempTensor.mutableData<float16>(zynqmp::FP16,
                                      param.x->ZynqTensor()->shape());
      tempTensor.copyFrom(param.x->ZynqTensor());
      tempTensor.setAligned(true);
      tempTensor.unalignImage();
      hwc.ZynqTensor()->copyFrom(&tempTensor);
    } else {
      hwc.ZynqTensor()->copyFrom(param.x->ZynqTensor());
    }

    int num = 1;
    int channel = 1;
    int height = 1;
    int width = 1;

    auto dims = param.y->ZynqTensor()->shape();

    hwc_to_chw(chw_data,
               hwc_data,
               dims.num(),
               dims.channel(),
               dims.height(),
               dims.width());

    param.y->ZynqTensor()->copyScaleFrom(param.x->ZynqTensor());
    param.y->ZynqTensor()->flush();
    auto out_lod = param.y->mutable_lod();
    *out_lod = param.x->lod();
    // param.x->ZynqTensor()->saveToFile("io_x", true);
    // param.y->ZynqTensor()->saveToFile("io_y", true);
  }
  std::string doc() const override { return "Copy IO from FPGA to HOST"; }
};

}  // namespace fpga
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(io_copy,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyHostToFpgaCompute,
                     host_to_device)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kAny),
                                       DATALAYOUT(kAny))})
    .Finalize();

REGISTER_LITE_KERNEL(io_copy,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyHostToFpgaCompute,
                     host_to_device_any_any)
    .BindInput("Input",
               {LiteType::GetTensorTy(
                   TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(io_copy,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyFpgaToHostCompute,
                     device_to_host)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kHost),
                                       PRECISION(kFloat),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(io_copy,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyFpgaToHostCHWCompute,
                     device_to_host_chw)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kARM),
                                       PRECISION(kFloat),
                                       DATALAYOUT(kNCHW))})
    .Finalize();

REGISTER_LITE_KERNEL(io_copy_once,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyHostToFpgaCompute,
                     host_to_device_once)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kAny),
                                       DATALAYOUT(kAny))})
    .Finalize();

REGISTER_LITE_KERNEL(io_copy_once,
                     kFPGA,
                     kAny,
                     kAny,
                     paddle::lite::kernels::fpga::IoCopyFpgaToHostCompute,
                     device_to_host_once)
    .BindInput("Input",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kHost),
                                       PRECISION(kAny),
                                       DATALAYOUT(kAny))})
    .Finalize();