cxx11_tensor_generator_sycl.cpp File Reference

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

Classes
struct	Generator1D
struct	Generator2D

Macros
#define	EIGEN_TEST_NO_LONGDOUBLE
#define	EIGEN_TEST_NO_COMPLEX
#define	EIGEN_DEFAULT_DENSE_INDEX_TYPE   int64_t
#define	EIGEN_USE_SYCL

Functions
template<typename DataType , int DataLayout, typename IndexType >
static void	test_1D_sycl (const Eigen::SyclDevice &sycl_device)
template<typename DataType , int DataLayout, typename IndexType >
static void	test_2D_sycl (const Eigen::SyclDevice &sycl_device)
template<typename DataType , int DataLayout, typename IndexType >
static void	test_gaussian_sycl (const Eigen::SyclDevice &sycl_device)
template<typename DataType , typename dev_Selector >
void	sycl_generator_test_per_device (dev_Selector s)
	EIGEN_DECLARE_TEST (cxx11_tensor_generator_sycl)

Variables
static const float	error_threshold = 1e-8f

Macro Definition Documentation

EIGEN_DEFAULT_DENSE_INDEX_TYPE

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE   int64_t

EIGEN_TEST_NO_COMPLEX

#define EIGEN_TEST_NO_COMPLEX

EIGEN_TEST_NO_LONGDOUBLE

#define EIGEN_TEST_NO_LONGDOUBLE

EIGEN_USE_SYCL

#define EIGEN_USE_SYCL

Function Documentation

EIGEN_DECLARE_TEST()

EIGEN_DECLARE_TEST

(

cxx11_tensor_generator_sycl

)

                                                {
  for (const auto& device : Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(sycl_generator_test_per_device<float>(device));
  }
}

References CALL_SUBTEST.

sycl_generator_test_per_device()

template<typename DataType , typename dev_Selector >

void sycl_generator_test_per_device

(

dev_Selector

s

)

                                                    {
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_1D_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_1D_sycl<DataType, ColMajor, int64_t>(sycl_device);
  test_2D_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_2D_sycl<DataType, ColMajor, int64_t>(sycl_device);
  test_gaussian_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_gaussian_sycl<DataType, ColMajor, int64_t>(sycl_device);
}

References s.

test_1D_sycl()

template<typename DataType , int DataLayout, typename IndexType >

static void test_1D_sycl ( const Eigen::SyclDevice &  sycl_device )

static

                                                             {
  IndexType sizeDim1 = 6;
  array<IndexType, 1> tensorRange = {{sizeDim1}};
  Tensor<DataType, 1, DataLayout, IndexType> vec(tensorRange);
  Tensor<DataType, 1, DataLayout, IndexType> result(tensorRange);
 
  const size_t tensorBuffSize = vec.size() * sizeof(DataType);
  DataType* gpu_data_vec = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
 
  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu_vec(gpu_data_vec, tensorRange);
  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
 
  sycl_device.memcpyHostToDevice(gpu_data_vec, vec.data(), tensorBuffSize);
  gpu_result.device(sycl_device) = gpu_vec.generate(Generator1D());
  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
 
  for (IndexType i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(result(i), i);
  }
}

References Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::data(), Eigen::TensorBase< Derived, AccessLevel >::device(), i, Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::size(), and VERIFY_IS_EQUAL.

test_2D_sycl()

template<typename DataType , int DataLayout, typename IndexType >

static void test_2D_sycl ( const Eigen::SyclDevice &  sycl_device )

static

                                                             {
  IndexType sizeDim1 = 5;
  IndexType sizeDim2 = 7;
  array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
  Tensor<DataType, 2, DataLayout, IndexType> matrix(tensorRange);
  Tensor<DataType, 2, DataLayout, IndexType> result(tensorRange);
 
  const size_t tensorBuffSize = matrix.size() * sizeof(DataType);
  DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
 
  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
 
  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
  gpu_result.device(sycl_device) = gpu_matrix.generate(Generator2D());
  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
 
  for (IndexType i = 0; i < 5; ++i) {
    for (IndexType j = 0; j < 5; ++j) {
      VERIFY_IS_EQUAL(result(i, j), 3 * i + 11 * j);
    }
  }
}

References Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::data(), Eigen::TensorBase< Derived, AccessLevel >::device(), i, j, matrix(), and VERIFY_IS_EQUAL.

test_gaussian_sycl()

template<typename DataType , int DataLayout, typename IndexType >

static void test_gaussian_sycl ( const Eigen::SyclDevice &  sycl_device )

static

                                                                   {
  IndexType rows = 32;
  IndexType cols = 48;
  array<DataType, 2> means;
  means[0] = rows / 2.0f;
  means[1] = cols / 2.0f;
  array<DataType, 2> std_devs;
  std_devs[0] = 3.14f;
  std_devs[1] = 2.7f;
  internal::GaussianGenerator<DataType, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);
 
  array<IndexType, 2> tensorRange = {{rows, cols}};
  Tensor<DataType, 2, DataLayout, IndexType> matrix(tensorRange);
  Tensor<DataType, 2, DataLayout, IndexType> result(tensorRange);
 
  const size_t tensorBuffSize = matrix.size() * sizeof(DataType);
  DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
 
  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
 
  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
  gpu_result.device(sycl_device) = gpu_matrix.generate(gaussian_gen);
  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
 
  for (IndexType i = 0; i < rows; ++i) {
    for (IndexType j = 0; j < cols; ++j) {
      DataType g_rows = powf(rows / 2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
      DataType g_cols = powf(cols / 2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
      DataType gaussian = expf(-g_rows - g_cols);
      Eigen::internal::isApprox(result(i, j), gaussian, error_threshold);
    }
  }
}

References cols, Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::data(), Eigen::TensorBase< Derived, AccessLevel >::device(), error_threshold, i, Eigen::internal::isApprox(), j, matrix(), and rows.

Variable Documentation

error_threshold

const float error_threshold = 1e-8f

static

Referenced by test_gaussian_sycl().