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 "common/backend_common_test.h"
  #include "common/py_func_graph_fetcher.h"
  #include "backend/common/optimizer/optimizer.h"
  #include "plugin/device/ascend/optimizer/ir_fusion/batchnormgrad_to_bninfergrad.h"
  #include "debug/anf_ir_dump.h"
  
  namespace mindspore {
  namespace opt {
  class TestHWOptimizeBatchNormGrad2BNInferGrad : public BackendCommon {
   public:
    TestHWOptimizeBatchNormGrad2BNInferGrad()
        : get_py_fun_("gtest_input.pre_activate.batchnormgrad_to_bninfergrad", true) {}
    ~TestHWOptimizeBatchNormGrad2BNInferGrad() override = default;
  
    UT::PyFuncGraphFetcher get_py_fun_;
  };
  
  TEST_F(TestHWOptimizeBatchNormGrad2BNInferGrad, test_fusion) {
    FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_batchnormgrad_to_bninfergrad", "before");
    EXPECT_NE(g, nullptr);
    std::vector<int64_t> shp_x{32, 64, 112, 112};
    auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
    std::vector<int64_t> shp_y{64};
    auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_y);
    AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract, y_abstract};
    auto fg = GetKernelGraph(g, args_spec_list);
  
    auto optimizer = std::make_shared<opt::GraphOptimizer>();
    auto pm = std::make_shared<opt::PassManager>();
    pm->AddPass(std::make_shared<opt::BatchNormGrad2BNInferGrad>());
    optimizer->AddPassManager(pm);
    FuncGraphPtr new_graph = optimizer->Optimize(fg);
  
    FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_batchnormgrad_to_bninfergrad", "after");
    EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
  }
  
  TEST_F(TestHWOptimizeBatchNormGrad2BNInferGrad, test_no_fusion) {
    FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_batchnormgrad_to_bninfergrad", "no_fusion");
    EXPECT_NE(g, nullptr);
    std::vector<int64_t> shp_x{32, 64, 112, 112};
    auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
    std::vector<int64_t> shp_y{64};
    auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_y);
    AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract, y_abstract};
    auto fg = GetKernelGraph(g, args_spec_list);
    auto origin_graph = std::make_shared<session::KernelGraph>(*fg);
  
    auto optimizer = std::make_shared<opt::GraphOptimizer>();
    auto pm = std::make_shared<opt::PassManager>();
    pm->AddPass(std::make_shared<opt::BatchNormGrad2BNInferGrad>());
    optimizer->AddPassManager(pm);
    FuncGraphPtr new_graph = optimizer->Optimize(fg);
  
    EXPECT_TRUE(CheckEqualGraph(origin_graph, new_graph));
  }
  }  // namespace opt
  }  // namespace mindspore
  