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 <memory>
  #include <random>
  #include "lite/backends/opencl/target_wrapper.h"
  #include "lite/core/op_registry.h"
  #include "lite/core/tensor.h"
  #include "lite/kernels/opencl/test_helper.h"
  
  #define FP16_MAX_DIFF (5e-1)
  
  namespace paddle {
  namespace lite {
  
  void sin(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::sin(input_data[i]);
    }
  }
  
  void cos(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::cos(input_data[i]);
    }
  }
  
  void tan(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::tan(input_data[i]);
    }
  }
  
  void atan(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::atan(input_data[i]);
    }
  }
  
  void asin(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::asin(input_data[i]);
    }
  }
  
  void acos(const float* input_data, const DDim& in_dim, float* output_data) {
    for (int i = 0; i < in_dim.production(); i++) {
      output_data[i] = std::acos(input_data[i]);
    }
  }
  
  TEST(trigonometrics_image2d_fp16, compute) {
    std::vector<std::string> trigonometrics{
        "sin", "cos", "tan", "atan", "asin", "acos"};
    for (size_t i = 0; i < trigonometrics.size(); i++) {
      auto trigonometric = trigonometrics[i];
  
      LOG(INFO) << "trigonometric:   " << trigonometric;
      auto kernels = KernelRegistry::Global().Create(trigonometric,
                                                     TARGET(kOpenCL),
                                                     PRECISION(kFP16),
                                                     DATALAYOUT(kImageDefault));
      ASSERT_FALSE(kernels.empty());
  
      auto kernel = std::move(kernels.front());
  
      LOG(INFO) << "Get kernel:" << kernel->doc();
  
      lite::Tensor x, out;
      operators::TrigonometricParam param;
      param.X = &x;
      param.Out = &out;
  
      std::unique_ptr<KernelContext> context(new KernelContext);
      context->As<OpenCLContext>().InitOnce();
  
      kernel->SetParam(param);
      std::unique_ptr<KernelContext> trigonometric_context(new KernelContext);
      context->As<OpenCLContext>().CopySharedTo(
          &(trigonometric_context->As<OpenCLContext>()));
      kernel->SetContext(std::move(trigonometric_context));
  
      const DDim in_dim = DDim(std::vector<DDim::value_type>{4, 11, 107, 107});
      const DDim out_dim = DDim(std::vector<DDim::value_type>{4, 11, 107, 107});
      x.Resize(in_dim);
      out.Resize(out_dim);
  
      std::default_random_engine engine;
      std::uniform_real_distribution<float> dist(-1, 1);
      std::vector<float> input_v(4 * 11 * 107 * 107);
      for (auto& i : input_v) {
        i = dist(engine);
      }
  
      LOG(INFO) << "prepare input";
      CLImageConverterDefault* default_converter = new CLImageConverterDefault();
      DDim image_shape = default_converter->InitImageDimInfoWith(in_dim);
      LOG(INFO) << "image_shape = " << image_shape[0] << " " << image_shape[1];
      std::vector<half_t> x_image_data(image_shape.production() * 4);  // 4 : RGBA
      default_converter->NCHWToImage(input_v.data(), x_image_data.data(), in_dim);
      auto* x_image = x.mutable_data<half_t, cl::Image2D>(
          image_shape[0], image_shape[1], x_image_data.data());
      LOG(INFO) << "x_image:" << x_image;
  
      auto* out_image =
          out.mutable_data<half_t, cl::Image2D>(image_shape[0], image_shape[1]);
      LOG(INFO) << "out_image:" << out_image;
      kernel->Launch();
  
      CLRuntime::Global()->command_queue().finish();
  
      std::unique_ptr<float[]> out_ref(new float[out_dim.production()]);
  
      if (trigonometric == "sin") {
        sin(input_v.data(), in_dim, out_ref.get());
      } else if (trigonometric == "cos") {
        cos(input_v.data(), in_dim, out_ref.get());
      } else if (trigonometric == "tan") {
        tan(input_v.data(), in_dim, out_ref.get());
      } else if (trigonometric == "atan") {
        atan(input_v.data(), in_dim, out_ref.get());
      } else if (trigonometric == "asin") {
        asin(input_v.data(), in_dim, out_ref.get());
      } else if (trigonometric == "acos") {
        acos(input_v.data(), in_dim, out_ref.get());
      }
  
      const size_t cl_image2d_row_pitch{0};
      const size_t cl_image2d_slice_pitch{0};
      half_t* out_image_data = new half_t[image_shape.production() * 4];
      TargetWrapperCL::ImgcpySync(out_image_data,
                                  out_image,
                                  image_shape[0],
                                  image_shape[1],
                                  cl_image2d_row_pitch,
                                  cl_image2d_slice_pitch,
                                  IoDirection::DtoH);
      float* out_data = new float[image_shape.production() * 4];
      default_converter->ImageToNCHW(
          out_image_data, out_data, image_shape, out_dim);
  
      for (int i = 0; i < out_dim.production(); i++) {
        auto abs_diff = abs(out_data[i] - out_ref[i]);
        auto relative_diff = COMPUTE_RELATIVE_DIFF(out_data[i], out_ref[i]);
        EXPECT_EQ((relative_diff <= FP16_MAX_DIFF) || (abs_diff <= FP16_MAX_DIFF),
                  true);
        if ((relative_diff > FP16_MAX_DIFF) && (abs_diff > FP16_MAX_DIFF)) {
          LOG(ERROR) << "error idx:" << i << " out_data[" << i
                     << "]:" << out_data[i] << " out_ref[" << i
                     << "]:" << out_ref[i] << " abs_diff:" << abs_diff
                     << " relative_diff:" << relative_diff
                     << " FP16_MAX_DIFF:" << FP16_MAX_DIFF;
        }
      }
    }
  }
  
  }  // namespace lite
  }  // namespace paddle
  
  USE_LITE_KERNEL(sin, kOpenCL, kFP16, kImageDefault, image2d);
  USE_LITE_KERNEL(cos, kOpenCL, kFP16, kImageDefault, image2d);
  USE_LITE_KERNEL(tan, kOpenCL, kFP16, kImageDefault, image2d);
  USE_LITE_KERNEL(atan, kOpenCL, kFP16, kImageDefault, image2d);
  USE_LITE_KERNEL(asin, kOpenCL, kFP16, kImageDefault, image2d);
  USE_LITE_KERNEL(acos, kOpenCL, kFP16, kImageDefault, image2d);
  