// 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/operators/conv_op.h"
#include "ai_ddk_lib/include/graph/buffer.h"
#include "ai_ddk_lib/include/graph/graph.h"
#include "ai_ddk_lib/include/graph/model.h"
#include "ai_ddk_lib/include/graph/op/all_ops.h"
#include "ai_ddk_lib/include/graph/operator.h"
#include "ai_ddk_lib/include/graph/operator_reg.h"
#include "lite/npu/bridge/registry.h"
#include "lite/npu/bridge/utils.h"

namespace paddle {
namespace lite {
namespace npu {
namespace bridge {

node_map_type ConvConverter(const std::shared_ptr<lite::OpLite> conv_op,
                            const node_map_type& inputs_map) {
  auto scope = conv_op->scope();
  auto op_info = conv_op->op_info();
  auto op_type = op_info->Type();
  auto unique_op_type = UniqueName(op_type);
  LOG(INFO) << "Converting " << op_type << "... ";

  // get input, filter and op attributes
  auto input_var_name = op_info->Input("Input").front();
  auto input = scope->FindVar(input_var_name)->GetMutable<lite::Tensor>();
  auto input_dims = input->dims();
  auto filter_var_name = op_info->Input("Filter").front();
  auto filter = scope->FindVar(filter_var_name)->GetMutable<lite::Tensor>();
  auto filter_dims = filter->dims();
  CHECK_EQ(input_dims.size(), 4);
  CHECK_EQ(filter_dims.size(), 4);
  auto strides = op_info->GetAttr<std::vector<int>>("strides");
  auto paddings = op_info->GetAttr<std::vector<int>>("paddings");
  auto groups = op_info->GetAttr<int>("groups");
  auto dilations = op_info->GetAttr<std::vector<int>>("dilations");
  auto fuse_relu = op_info->GetAttr<bool>("fuse_relu");
  CHECK_EQ(strides.size(), 2);
  CHECK_EQ(paddings.size(), 2);
  CHECK_EQ(dilations.size(), 2);

  // check depthwise mode, and decide whether use ConvolutionDepthwise Op
  bool use_depthwise_conv =
      false;  // whether use ge::op::ConvolutionDepthwise ?
  bool is_depthwise_mode = input_dims[1] == groups && filter_dims[0] == groups;
  if (is_depthwise_mode &&
      !((groups == 1 || groups >= 5) && dilations[0] == 1 &&
        dilations[1] == 1)) {
    use_depthwise_conv = true;
    LOG(WARNING) << "For depthwise mode, dilation = 1 and groups >= 5 (or "
                    "groups = 1) is only supported in "
                    "Convolution Op, so force to use ConvolutionDepthwise Op, "
                    "but may lead poor performance.";
  }

  // check input
  CHECK(inputs_map.count(input_var_name));
  OpList::Global().add(inputs_map.at(input_var_name));

  // create filter node
  CHECK(!inputs_map.count(filter_var_name));
  auto filter_const_node = std::make_shared<ge::op::Const>(filter_var_name);
  filter_const_node->set_attr_value(CvtFromLiteTensor(filter));
  OpList::Global().add(filter_const_node);

  // create bias node if has bias
  std::shared_ptr<ge::op::Const> bias_const_node = nullptr;
  if (HasInputArg(op_info, scope, "Bias")) {
    auto bias_var_name = op_info->Input("Bias").front();
    CHECK(!inputs_map.count(bias_var_name));
    auto* bias = scope->FindVar(bias_var_name)->GetMutable<lite::Tensor>();
    auto channel_size = bias->dims().production();
    CHECK_EQ(channel_size, filter_dims[0]);
    bias_const_node = std::make_shared<ge::op::Const>(bias_var_name);
    bias_const_node->set_attr_value(
        CvtFromLiteTensor(bias, {1, channel_size, 1, 1}));
    OpList::Global().add(bias_const_node);
  }

  // create conv node and set input, filter, bias nodes and attributes
  std::shared_ptr<ge::Operator> conv_node = nullptr;
  if (use_depthwise_conv && is_depthwise_mode) {
    auto depthwise_conv_node =
        std::make_shared<ge::op::ConvolutionDepthwise>(unique_op_type);
    depthwise_conv_node->set_input_x(*inputs_map.at(input_var_name));
    depthwise_conv_node->set_input_filter(*filter_const_node);
    depthwise_conv_node->set_attr_mode(1);
    depthwise_conv_node->set_attr_algo(0);
    depthwise_conv_node->set_attr_format(0);    // NCHW
    depthwise_conv_node->set_attr_pad_mode(5);  // VALID
    depthwise_conv_node->set_attr_group(groups);
    depthwise_conv_node->set_attr_pad(ge::AttrValue::LIST_INT(
        {paddings[0], paddings[0], paddings[1], paddings[1]}));
    depthwise_conv_node->set_attr_dilation(
        ge::AttrValue::LIST_INT({dilations[0], dilations[1]}));
    depthwise_conv_node->set_attr_stride(
        ge::AttrValue::LIST_INT({strides[0], strides[1]}));
    depthwise_conv_node->set_attr_kernel(
        ge::AttrValue::LIST_INT({filter_dims[2], filter_dims[3]}));
    OpList::Global().add(depthwise_conv_node);
    conv_node = depthwise_conv_node;
    if (bias_const_node != nullptr) {
      auto add_node = std::make_shared<ge::op::Add>(unique_op_type + "/add");
      add_node->set_input_x1(*depthwise_conv_node);
      add_node->set_input_x2(*bias_const_node);
      OpList::Global().add(add_node);
      conv_node = add_node;
    }
  } else {
    auto common_conv_node =
        std::make_shared<ge::op::Convolution>(unique_op_type);
    common_conv_node->set_input_x(*inputs_map.at(input_var_name));
    common_conv_node->set_input_w(*filter_const_node);
    common_conv_node->set_attr_mode(1);
    common_conv_node->set_attr_pad_mode(0);  // NOTSET
    common_conv_node->set_attr_group(groups);
    common_conv_node->set_attr_pad(ge::AttrValue::LIST_INT(
        {paddings[0], paddings[0], paddings[1], paddings[1]}));
    common_conv_node->set_attr_dilation(
        ge::AttrValue::LIST_INT({dilations[0], dilations[1]}));
    common_conv_node->set_attr_stride(
        ge::AttrValue::LIST_INT({strides[0], strides[1]}));
    common_conv_node->set_attr_kernel(
        ge::AttrValue::LIST_INT({filter_dims[2], filter_dims[3]}));
    if (bias_const_node != nullptr) {
      common_conv_node->set_input_b(*bias_const_node);
    }
    OpList::Global().add(common_conv_node);
    conv_node = common_conv_node;
  }
  CHECK(conv_node);

  node_map_type outputs_map;
  if (fuse_relu) {
    // append relu node if fuse_relu is true
    auto relu_node =
        std::make_shared<ge::op::Activation>(unique_op_type + "/relu");
    relu_node->set_input_x(*conv_node);
    relu_node->set_attr_mode(1);
    OpList::Global().add(relu_node);
    outputs_map[op_info->Output("Output").front()] = relu_node;
  } else {
    outputs_map[op_info->Output("Output").front()] = conv_node;
  }
  return outputs_map;
}

}  // namespace bridge
}  // namespace npu
}  // namespace lite
}  // namespace paddle

REGISTER_NPU_BRIDGE(conv2d, paddle::lite::npu::bridge::ConvConverter);
REGISTER_NPU_BRIDGE(depthwise_conv2d, paddle::lite::npu::bridge::ConvConverter);