text: /**
   * 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 "pre_activate/ascend/ir_fusion/confusion_mul_grad_fusion.h"
  #include <utility>
  #include <memory>
  #include <vector>
  #include <algorithm>
  #include <string>
  #include "session/anf_runtime_algorithm.h"
  #include "ir/primitive.h"
  #include "utils/utils.h"
  #include "pipeline/static_analysis/abstract_value.h"
  #include "pre_activate/common/helper.h"
  
  namespace mindspore {
  namespace opt {
  namespace {
  const size_t kConfusionMulGradOutputNum = 2;
  
  CNodePtr CreateFusionNode(const FuncGraphPtr &graph, const CNodePtr &reduce_sum, const AnfNodePtr &mul0_anf,
                            const AnfNodePtr &input3) {
    MS_EXCEPTION_IF_NULL(graph);
    MS_EXCEPTION_IF_NULL(reduce_sum);
    MS_EXCEPTION_IF_NULL(mul0_anf);
    MS_EXCEPTION_IF_NULL(input3);
    auto mul0 = mul0_anf->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(mul0);
  
    auto prim = std::make_shared<Primitive>(kConfusionMulGradOpName);
    std::vector<AnfNodePtr> inputs = {NewValueNode(prim), mul0->input(1), mul0->input(2), input3};
    auto fusion_node = graph->NewCNode(inputs);
    MS_EXCEPTION_IF_NULL(fusion_node);
    fusion_node->set_scope(reduce_sum->scope());
    AnfAlgo::CopyNodeAttr(kAttrAxis, reduce_sum, fusion_node);
    AnfAlgo::CopyNodeAttr(kAttrKeepDims, reduce_sum, fusion_node);
    auto types = {AnfAlgo::GetOutputInferDataType(mul0, 0), AnfAlgo::GetOutputInferDataType(reduce_sum, 0)};
    auto shapes = {AnfAlgo::GetOutputInferShape(mul0, 0), AnfAlgo::GetOutputInferShape(reduce_sum, 0)};
    AnfAlgo::SetOutputInferTypeAndShape(types, shapes, fusion_node.get());
    return fusion_node;
  }
  
  AnfNodePtr GetMul0(const FuncGraphPtr &graph, const AnfNodePtr &input2, const AnfNodePtr &mul1) {
    MS_EXCEPTION_IF_NULL(graph);
    MS_EXCEPTION_IF_NULL(input2);
    auto manager = graph->manager();
    MS_EXCEPTION_IF_NULL(manager);
    if (manager->node_users().find(input2) == manager->node_users().end()) {
      MS_LOG(EXCEPTION) << "node has no output in manager";
    }
  
    AnfNodePtr mul0 = nullptr;
    const AnfNodeIndexSet &outputs_set = manager->node_users()[input2];
    // input2 must be the 2rd input of mul0
    auto it = std::find_if(outputs_set.begin(), outputs_set.end(), [&mul1](const std::pair<AnfNodePtr, int> &node_index) {
      return node_index.first != mul1 && node_index.second == 2;
    });
    if (it != outputs_set.end() && AnfAlgo::GetCNodeName(it->first) == prim::kPrimMul->name()) {
      mul0 = it->first;
    }
    return mul0;
  }
  
  bool QuitFusion(const FuncGraphPtr &graph, const AnfNodePtr &mul0_anf, const AnfNodePtr &mul1_anf,
                  const AnfNodePtr &reduce_sum, const AnfNodePtr &input2) {
    MS_EXCEPTION_IF_NULL(mul0_anf);
    MS_EXCEPTION_IF_NULL(mul1_anf);
    MS_EXCEPTION_IF_NULL(reduce_sum);
    MS_EXCEPTION_IF_NULL(input2);
    auto addn = input2->cast<CNodePtr>();
    if (addn == nullptr || AnfAlgo::GetCNodeName(addn) != prim::kPrimAddN->name()) {
      MS_LOG(INFO) << "mul's second input is not addn";
      return true;
    }
    std::vector<size_t> shape = AnfAlgo::GetOutputInferShape(addn, 0);
    if (shape.size() != 2 || !(shape[1] == 1024 || shape[1] == 768)) {
      MS_LOG(INFO) << "Addn's infer shape is not equal [x,1024] or [x,768]";
      return true;
    }
    if (!mul0_anf->isa<CNode>() || !mul1_anf->isa<CNode>()) {
      return true;
    }
    auto mul1 = mul1_anf->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(mul1);
    auto mul0 = mul0_anf->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(mul0);
  
    if (IsDepend(graph, mul0->input(1), reduce_sum)) {
      MS_LOG(INFO) << "mul0->input(1) depends on reduce_sum, quit fusion";
      return true;
    }
    if (IsDepend(graph, mul1->input(1), mul0)) {
      MS_LOG(INFO) << "mul1->input(1) depends on mul0, quit fusion";
      return true;
    }
    return false;
  }
  }  // namespace
  
  const BaseRef ConfusionMulGradFusion::DefinePattern() const {
    VectorRef mul1({prim::kPrimMul, input3_, input2_});
    VectorRef reduce_sum({prim::kPrimReduceSum, mul1});
    return reduce_sum;
  }
  
  const AnfNodePtr ConfusionMulGradFusion::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 input2 = utils::cast<AnfNodePtr>((*equiv)[input2_]);
    auto input3 = utils::cast<AnfNodePtr>((*equiv)[input3_]);
    auto reduce_sum = node->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(reduce_sum);
    auto mul1 = reduce_sum->input(1);
    if (IsUsedByOthers(graph, mul1)) {
      MS_LOG(INFO) << "Mul1 is used by others, quit fusion!";
      return nullptr;
    }
    auto mul0 = GetMul0(graph, input2, mul1);
    if (mul0 == nullptr) {
      MS_LOG(INFO) << "Mul0 do not exist, quit fusion";
      return nullptr;
    }
    if (QuitFusion(graph, mul0, mul1, node, input2)) {
      return nullptr;
    }
  
    auto fusion_node = CreateFusionNode(graph, reduce_sum, mul0, input3);
    std::vector<AnfNodePtr> fusion_node_outputs;
    CreateMultipleOutputsOfAnfNode(graph, fusion_node, kConfusionMulGradOutputNum, &fusion_node_outputs);
  
    auto manage = graph->manager();
    MS_EXCEPTION_IF_NULL(manage);
    manage->Replace(mul0, fusion_node_outputs[0]);
    return fusion_node_outputs[1];
  }
  }  // namespace opt
  }  // namespace mindspore
  