// 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/anakin/convert/conv2d_fusion.h"
#include <algorithm>
#include <memory>
#include <vector>

using anakin::graph::GraphGlobalMem;
using anakin::AK_FLOAT;
using anakin::saber::NV;
using anakin::saber::Shape;
using anakin::PTuple;

namespace paddle {
namespace inference {
namespace anakin {

void Conv2dFusionOpConverter::operator()(const framework::proto::OpDesc &op,
                                         const framework::BlockDesc &block_desc,
                                         const framework::Scope &scope,
                                         bool test_mode) {
  framework::OpDesc op_desc(op, nullptr);
  PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1UL);
  PADDLE_ENFORCE_EQ(op_desc.Input("Filter").size(), 1UL);
  PADDLE_ENFORCE_EQ(op_desc.Input("Bias").size(), 1UL);
  PADDLE_ENFORCE_EQ(op_desc.Output("Output").size(), 1UL);

  auto input_name = op_desc.Input("Input").front();
  auto output_name = op_desc.Output("Output").front();
  auto op_name = op_desc.Type() + ":" + op_desc.Output("Output").front();
  engine_->AddOp(op_name, "Convolution", {input_name}, {output_name});

  auto *filter_v = scope.FindVar(op_desc.Input("Filter").front());
  PADDLE_ENFORCE_NOT_NULL(filter_v);
  auto *filter_t = filter_v->GetMutable<framework::LoDTensor>();

  auto *b_v = scope.FindVar(op_desc.Input("Bias").front());
  PADDLE_ENFORCE_NOT_NULL(b_v);
  auto *b_t = b_v->GetMutable<framework::LoDTensor>();

  std::unique_ptr<framework::LoDTensor> weight_tensor(
      new framework::LoDTensor());
  weight_tensor->Resize(filter_t->dims());
  TensorCopySync((*filter_t), platform::CPUPlace(), weight_tensor.get());

  PADDLE_ENFORCE_EQ(weight_tensor->dims().size(), 4UL);

  // const int n_output = weight_tensor->dims()[0];
  // const int n_input = weight_tensor->dims()[1];
  const int filter_h = weight_tensor->dims()[2];
  const int filter_w = weight_tensor->dims()[3];
  // auto filter_num = n_input * filter_h * filter_w ;
  auto filter_num = weight_tensor->dims()[0];
  engine_->AddOpAttr<int>(op_name, "filter_num", filter_num);
  engine_->AddOpAttr<PTuple<int>>(op_name, "kernel_size", {filter_h, filter_w});
  auto strides = boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
  engine_->AddOpAttr<PTuple<int>>(op_name, "strides", strides);
  auto paddings = boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
  engine_->AddOpAttr<PTuple<int>>(op_name, "padding", paddings);
  auto dilations = boost::get<std::vector<int>>(op_desc.GetAttr("dilations"));
  engine_->AddOpAttr<PTuple<int>>(op_name, "dilation_rate", dilations);
  const int groups = boost::get<int>(op_desc.GetAttr("groups"));
  engine_->AddOpAttr(op_name, "group", groups);
  engine_->AddOpAttr(op_name, "axis", 1);
  engine_->AddOpAttr(op_name, "bias_term", true);

  auto weight_shape = framework::vectorize2int(filter_t->dims());
  Shape anakin_shape(weight_shape);
  auto *weight1 =
      GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(anakin_shape);
  float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
  std::copy_n(weight_tensor->data<float>(), weight_tensor->numel(), cpu_data);
  weight1->d_tensor().set_shape(anakin_shape);
  weight1->d_tensor().copy_from(weight1->h_tensor());
  engine_->AddOpAttr(op_name, "weight_1", *weight1);

  auto bias_shape = framework::vectorize2int(b_t->dims());
  framework::LoDTensor bias_tensor;
  bias_tensor.Resize(b_t->dims());
  TensorCopySync((*b_t), platform::CPUPlace(), &bias_tensor);
  auto *bias_data = bias_tensor.data<float>();
  bias_shape.insert(bias_shape.begin(), 1);
  bias_shape.insert(bias_shape.begin(), 1);
  bias_shape.insert(bias_shape.begin(), 1);
  // bias_shape.push_back(1);
  // bias_shape.push_back(1);
  Shape anakin_bias_shape(bias_shape);

  auto *weight2 = GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(
      anakin_bias_shape);
  float *cpu_data2 = static_cast<float *>(weight2->h_tensor().mutable_data());
  std::copy_n(bias_data, bias_tensor.numel(), cpu_data2);
  weight2->d_tensor().set_shape(anakin_bias_shape);
  weight2->d_tensor().copy_from(weight2->h_tensor());
  engine_->AddOpAttr(op_name, "weight_2", *weight2);
}

}  // namespace anakin
}  // namespace inference
}  // namespace paddle

REGISTER_ANAKIN_OP_CONVERTER(conv2d_fusion, Conv2dFusionOpConverter);