// 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/inference/tensorrt/plugin/pool_op_plugin.h"
#include "paddle/phi/kernels/funcs/pooling.h"

namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {

nvinfer1::Dims PoolPlugin::getOutputDimensions(int index,
                                               const nvinfer1::Dims *inputDims,
                                               int nbInputs) TRT_NOEXCEPT {
  assert(nbInputs == 1);
  assert(index == 0);
  assert(inputDims[0].nbDims == 3);
  nvinfer1::Dims const &input_dims = inputDims[0];

  nvinfer1::Dims output_dims = input_dims;

  output_dims.d[1] = output_shape_[1];
  output_dims.d[2] = output_shape_[2];
  return output_dims;
}

size_t PoolPlugin::getSerializationSize() const TRT_NOEXCEPT {
  return getBaseSerializationSize() + SerializedSize(ceil_mode_) +
         SerializedSize(pool_type_) + SerializedSize(adaptive_) +
         SerializedSize(exclusive_) + SerializedSize(ksize_) +
         SerializedSize(strides_) + SerializedSize(paddings_) +
         SerializedSize(real_paddings_) + SerializedSize(input_shape_) +
         SerializedSize(output_shape_);
}

// TRT will call this func when we need to serialize the configuration of
// tensorrt.
void PoolPlugin::serialize(void *buffer) const TRT_NOEXCEPT {
  serializeBase(buffer);
  SerializeValue(&buffer, ceil_mode_);
  SerializeValue(&buffer, pool_type_);
  SerializeValue(&buffer, adaptive_);
  SerializeValue(&buffer, exclusive_);
  SerializeValue(&buffer, ksize_);
  SerializeValue(&buffer, strides_);
  SerializeValue(&buffer, paddings_);
  SerializeValue(&buffer, real_paddings_);
  SerializeValue(&buffer, input_shape_);
  SerializeValue(&buffer, output_shape_);
}

PoolPlugin *PoolPlugin::clone() const TRT_NOEXCEPT {
  return new PoolPlugin(ceil_mode_,
                        pool_type_,
                        adaptive_,
                        exclusive_,
                        ksize_,
                        strides_,
                        paddings_,
                        input_shape_,
                        real_paddings_);
}

int PoolPlugin::enqueue(int batchSize,
                        const void *const *inputs,
#if IS_TRT_VERSION_LT(8000)
                        void **outputs,
                        void *workspace,
                        cudaStream_t stream) TRT_NOEXCEPT {
#else
                        void *const *outputs,
                        void *workspace,
                        cudaStream_t stream) TRT_NOEXCEPT {
#endif
  auto const &input_dims = this->getInputDims(0);
  int input_size = 0;
  float const *idata = reinterpret_cast<float const *>(inputs[0]);
  float *const *odatas = reinterpret_cast<float *const *>(outputs);

  std::vector<int> input_shape = input_shape_;
  std::vector<int> output_shape = output_shape_;
  input_shape.insert(input_shape.begin(), batchSize);
  output_shape.insert(output_shape.begin(), batchSize);

  if (pool_type_ == PoolType::max) {
    phi::funcs::MaxPool<float> pool_process;
    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
        pool2d_forward;
    pool2d_forward(idata,
                   input_shape,
                   output_shape,
                   ksize_,
                   strides_,
                   paddings_,
                   true,
                   false,
                   odatas[0],
                   stream,
                   pool_process);
  } else if (pool_type_ == PoolType::avg) {
    phi::funcs::AvgPool<float> pool_process;
    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
        pool2d_forward;
    pool2d_forward(idata,
                   input_shape,
                   output_shape,
                   ksize_,
                   strides_,
                   paddings_,
                   exclusive_,
                   adaptive_,
                   odatas[0],
                   stream,
                   pool_process);
  }

  return cudaGetLastError() != cudaSuccess;
}

// Dynamic Plugin below.
#if IS_TRT_VERSION_GE(6000)

PoolPluginDynamic::PoolPluginDynamic(void const *serialData,
                                     size_t serialLength) {
  DeserializeValue(&serialData, &serialLength, &ceil_mode_);
  const char *pool_type;
  DeserializeValue(&serialData, &serialLength, &pool_type);
  pool_type_ = std::string(pool_type);
  DeserializeValue(&serialData, &serialLength, &adaptive_);
  DeserializeValue(&serialData, &serialLength, &exclusive_);
  DeserializeValue(&serialData, &serialLength, &ksize_);
  DeserializeValue(&serialData, &serialLength, &strides_);
  DeserializeValue(&serialData, &serialLength, &paddings_);
  DeserializeValue(&serialData, &serialLength, &is_global_);
}

size_t PoolPluginDynamic::getSerializationSize() const TRT_NOEXCEPT {
  return SerializedSize(ceil_mode_) + SerializedSize(pool_type_.c_str()) +
         SerializedSize(adaptive_) + SerializedSize(exclusive_) +
         SerializedSize(ksize_) + SerializedSize(strides_) +
         SerializedSize(paddings_) + SerializedSize(is_global_);
}

void PoolPluginDynamic::serialize(void *buffer) const TRT_NOEXCEPT {
  SerializeValue(&buffer, ceil_mode_);
  SerializeValue(&buffer, pool_type_.c_str());
  SerializeValue(&buffer, adaptive_);
  SerializeValue(&buffer, exclusive_);
  SerializeValue(&buffer, ksize_);
  SerializeValue(&buffer, strides_);
  SerializeValue(&buffer, paddings_);
  SerializeValue(&buffer, is_global_);
}

nvinfer1::IPluginV2DynamicExt *PoolPluginDynamic::clone() const TRT_NOEXCEPT {
  return new PoolPluginDynamic(ceil_mode_,
                               pool_type_,
                               adaptive_,
                               exclusive_,
                               ksize_,
                               strides_,
                               paddings_,
                               is_global_);
}

nvinfer1::DimsExprs PoolPluginDynamic::getOutputDimensions(
    int output_index,
    const nvinfer1::DimsExprs *inputs,
    int nb_inputs,
    nvinfer1::IExprBuilder &expr_builder) TRT_NOEXCEPT {
  PADDLE_ENFORCE_EQ(nb_inputs,
                    1,
                    platform::errors::InvalidArgument(
                        "The Split plugin should be only one input."));

  nvinfer1::DimsExprs output(inputs[0]);
  if (is_global_ && !adaptive_) {
    output.d[2] = expr_builder.constant(1);
    output.d[3] = expr_builder.constant(1);
    return output;
  }
  if (is_global_ && adaptive_) {
    return inputs[0];
  }
  if (adaptive_) {
    output.d[2] = expr_builder.constant(ksize_[0]);
    output.d[3] = expr_builder.constant(ksize_[1]);
    return output;
  }

  auto stri_0 = expr_builder.constant(strides_[0]);
  auto stri_1 = expr_builder.constant(strides_[1]);
  auto one_value = expr_builder.constant(1);

  auto v0_tmp = expr_builder.constant(-ksize_[0] + 2 * paddings_[0]);
  auto v1_tmp = expr_builder.constant(-ksize_[1] + 2 * paddings_[1]);

  auto ceil_tmp =
      expr_builder.constant(-ksize_[0] + 2 * paddings_[0] + strides_[0] - 1);
  auto ceil1_tmp =
      expr_builder.constant(-ksize_[1] + 2 * paddings_[1] + strides_[1] - 1);

  if (!ceil_mode_) {
    output.d[2] = expr_builder.operation(
        nvinfer1::DimensionOperation::kSUM,
        *expr_builder.operation(
            nvinfer1::DimensionOperation::kFLOOR_DIV,
            *expr_builder.operation(
                nvinfer1::DimensionOperation::kSUM, *inputs[0].d[2], *v0_tmp),
            *stri_0),
        *one_value);
    output.d[3] = expr_builder.operation(
        nvinfer1::DimensionOperation::kSUM,
        *expr_builder.operation(
            nvinfer1::DimensionOperation::kFLOOR_DIV,
            *expr_builder.operation(
                nvinfer1::DimensionOperation::kSUM, *inputs[0].d[3], *v1_tmp),
            *stri_1),
        *one_value);

  } else {
    output.d[2] = expr_builder.operation(
        nvinfer1::DimensionOperation::kSUM,
        *expr_builder.operation(
            nvinfer1::DimensionOperation::kFLOOR_DIV,
            *expr_builder.operation(
                nvinfer1::DimensionOperation::kSUM, *inputs[0].d[2], *ceil_tmp),
            *stri_0),
        *one_value);
    output.d[3] = expr_builder.operation(
        nvinfer1::DimensionOperation::kSUM,
        *expr_builder.operation(
            nvinfer1::DimensionOperation::kFLOOR_DIV,
            *expr_builder.operation(nvinfer1::DimensionOperation::kSUM,
                                    *inputs[0].d[3],
                                    *ceil1_tmp),
            *stri_1),
        *one_value);
  }

  return output;
}

bool PoolPluginDynamic::supportsFormatCombination(
    int pos,
    const nvinfer1::PluginTensorDesc *in_out,
    int nb_inputs,
    int nb_outputs) TRT_NOEXCEPT {
  PADDLE_ENFORCE_NOT_NULL(
      in_out,
      platform::errors::InvalidArgument(
          "The input of swish plugin shoule not be nullptr."));

  PADDLE_ENFORCE_LT(
      pos,
      nb_inputs + nb_outputs,
      platform::errors::InvalidArgument("The pos(%d) should be less than the "
                                        "num(%d) of the input and the output.",
                                        pos,
                                        nb_inputs + nb_outputs));
  (in_out && pos < (nb_inputs + nb_outputs));

  return ((in_out[pos].type == nvinfer1::DataType::kFLOAT) &&
          in_out[pos].format == nvinfer1::PluginFormat::kLINEAR);
}

nvinfer1::DataType PoolPluginDynamic::getOutputDataType(
    int index,
    const nvinfer1::DataType *input_types,
    int nb_inputs) const TRT_NOEXCEPT {
  PADDLE_ENFORCE_EQ(index,
                    0,
                    platform::errors::InvalidArgument(
                        "The Pool Plugin only has one input, so the "
                        "index value should be 0, but get %d.",
                        index));
  PADDLE_ENFORCE_EQ((input_types[0] == nvinfer1::DataType::kFLOAT),
                    true,
                    platform::errors::InvalidArgument(
                        "The input type should be half or float"));
  return input_types[0];
}

int PoolPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
                               const nvinfer1::PluginTensorDesc *output_desc,
                               const void *const *inputs,
                               void *const *outputs,
                               void *workspace,
                               cudaStream_t stream) TRT_NOEXCEPT {
  auto input_dims = input_desc[0].dims;
  int n = input_dims.d[0];
  int c = input_dims.d[1];
  int h = input_dims.d[2];
  int w = input_dims.d[3];

  const float *input = static_cast<const float *>(inputs[0]);
  float *output = static_cast<float *>(outputs[0]);

  std::vector<int> input_shape, output_shape;
  for (int i = 0; i < input_dims.nbDims; i++)
    input_shape.push_back(input_dims.d[i]);
  output_shape = input_shape;

  std::vector<int> ksize = ksize_;
  std::vector<int> paddings = paddings_;
  if (is_global_) {
    ksize[0] = h;
    ksize[1] = w;
    paddings[0] = 0;
    paddings[1] = 0;
    output_shape[2] = 1;
    output_shape[3] = 1;
    if (adaptive_) {
      output_shape[2] = h;
      output_shape[3] = w;
    }
  } else {
    auto data_dim = CalcOutputSize(
        {h, w}, ceil_mode_, adaptive_, ksize_, strides_, paddings_);
    output_shape[2] = data_dim[0];
    output_shape[3] = data_dim[1];
  }

  if (pool_type_ == "max") {
    phi::funcs::MaxPool<float> pool_process;
    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
        pool2d_forward;
    pool2d_forward(input,
                   input_shape,
                   output_shape,
                   ksize,
                   strides_,
                   paddings,
                   true,
                   false,
                   output,
                   stream,
                   pool_process);
  } else if (pool_type_ == "avg") {
    phi::funcs::AvgPool<float> pool_process;
    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
        pool2d_forward;
    pool2d_forward(input,
                   input_shape,
                   output_shape,
                   ksize,
                   strides_,
                   paddings,
                   exclusive_,
                   adaptive_,
                   output,
                   stream,
                   pool_process);
  }

  return cudaGetLastError() != cudaSuccess;
}
#endif

}  // namespace plugin
}  // namespace tensorrt
}  // namespace inference
}  // namespace paddle
