/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * 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 "backend/optimizer/gpu/replace_bn_grad_cast2_fusion.h"

#include <memory>
#include <vector>
#include <string>

#include "backend/session/anf_runtime_algorithm.h"
#include "ir/primitive.h"
#include "utils/utils.h"
#include "backend/optimizer/common/helper.h"

namespace mindspore {
namespace opt {
const BaseRef ReplaceBNGradCast2Fusion::DefinePattern() const {
  VectorRef fbn2g = VectorRef({prim::kPrimFusedBatchNormGrad, dy_, x_, scale_, mean_, var_});
  VectorRef tupleget = VectorRef({prim::kPrimTupleGetItem, fbn2g, index_});
  VectorRef out_cast = VectorRef({prim::kPrimCast, tupleget});
  return out_cast;
}

const AnfNodePtr ReplaceBNGradCast2Fusion::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
                                                   const EquivPtr &equiv) const {
  MS_EXCEPTION_IF_NULL(graph);
  MS_EXCEPTION_IF_NULL(node);
  MS_EXCEPTION_IF_NULL(equiv);
  auto tuple = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), 0);
  auto index_node = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 1);
  MS_EXCEPTION_IF_NULL(index_node);
  auto value_node = index_node->cast<ValueNodePtr>();
  MS_EXCEPTION_IF_NULL(value_node);
  int item_idx = GetValue<int>(value_node->value());
  if (item_idx != 0) {
    return nullptr;
  }
  auto fbn2g = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple), 0);

  auto dy_ = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 0);
  auto x_ = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 1);

  auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 2);
  auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 3);
  auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(fbn2g), 4);

  MS_EXCEPTION_IF_NULL(fbn2g);
  MS_EXCEPTION_IF_NULL(dy_);
  MS_EXCEPTION_IF_NULL(scale);
  MS_EXCEPTION_IF_NULL(x_);
  MS_EXCEPTION_IF_NULL(mean);
  MS_EXCEPTION_IF_NULL(var);

  auto manager = graph->manager();
  MS_EXCEPTION_IF_NULL(manager);
  manager->Replace(utils::cast<CNodePtr>(node), utils::cast<CNodePtr>(tuple));
  std::vector<TypeId> outputs_type;
  std::vector<std::vector<size_t>> outputs_shape;
  auto output_num = AnfAlgo::GetOutputTensorNum(fbn2g);
  for (size_t i = 0; i < output_num; i++) {
    outputs_type.push_back(AnfAlgo::GetOutputInferDataType(fbn2g, i));
    outputs_shape.push_back(AnfAlgo::GetOutputInferShape(fbn2g, i));
  }
  outputs_type[0] = AnfAlgo::GetPrevNodeOutputInferDataType(fbn2g, 0);
  AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fbn2g.get());

  outputs_type.clear();
  outputs_shape.clear();
  outputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(fbn2g, 0));
  outputs_shape.push_back(AnfAlgo::GetOutputInferShape(tuple, 0));
  AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, tuple.get());

  return tuple;
}
}  // namespace opt
}  // namespace mindspore
