text: // 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 <gtest/gtest.h>
  #include "lite/api/paddle_use_kernels.h"
  #include "lite/api/paddle_use_ops.h"
  #include "lite/core/arena/framework.h"
  #include "lite/tests/utils/fill_data.h"
  
  namespace paddle {
  namespace lite {
  
  class IncrementComputeTester : public arena::TestCase {
   protected:
    // common attributes for this op.
    std::string input_ = "x";
    std::string output_ = "out";
    float step_ = 1.f;
    DDim dims_{{3, 5, 4, 4}};
    bool bias_after_scale_;
  
   public:
    IncrementComputeTester(const Place& place,
                           const std::string& alias,
                           float step,
                           DDim dims)
        : TestCase(place, alias), step_(step), dims_(dims) {}
  
    void RunBaseline(Scope* scope) override {
      auto* out = scope->NewTensor(output_);
      CHECK(out);
      out->Resize(dims_);
      auto* out_data = out->mutable_data<float>();
  
      auto* x = scope->FindTensor(input_);
      const auto* x_data = x->data<float>();
      int n = dims_.production();
      for (int i = 0; i < n; i++) {
        out_data[i] = x_data[i] + step_;
      }
    }
  
    void PrepareOpDesc(cpp::OpDesc* op_desc) {
      op_desc->SetType("increment");
      op_desc->SetInput("X", {input_});
      op_desc->SetOutput("Out", {output_});
      op_desc->SetAttr("step", step_);
    }
  
    void PrepareData() override {
      std::vector<float> din(dims_.production());
      fill_data_rand(din.data(), -5.f, 5.f, dims_.production());
      SetCommonTensor(input_, dims_, din.data());
    }
  };
  
  void test_increment(Place place, float abs_error) {
    std::vector<std::vector<int64_t>> x_dims{{3, 5, 4, 4}, {3, 5}, {1}};
    for (auto dims : x_dims) {
      for (float step : {1, 2}) {
  #if LITE_WITH_NPU
        if (dims.size() != 1) continue;
  #endif
        std::unique_ptr<arena::TestCase> tester(
            new IncrementComputeTester(place, "def", step, DDim(dims)));
        arena::Arena arena(std::move(tester), place, abs_error);
        arena.TestPrecision();
      }
    }
  }
  
  TEST(Increment, precision) {
    Place place;
    float abs_error = 2e-5;
  #if defined(LITE_WITH_NPU)
    place = TARGET(kNPU);
    abs_error = 1e-2;  // use fp16 in npu
  #elif defined(LITE_WITH_XPU) && !defined(LITE_WITH_XTCL)
    place = TARGET(kXPU);
    abs_error = 1e-2;
  #elif defined(LITE_WITH_ARM)
    place = TARGET(kARM);
  #elif defined(LITE_WITH_X86)
    place = TARGET(kHost);
  #else
    return;
  #endif
  
    test_increment(place, abs_error);
  }
  
  }  // namespace lite
  }  // namespace paddle
  