cxx11_tensor_shuffling_sycl.cpp File Reference

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

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_simple_shuffling_sycl (const Eigen::SyclDevice &sycl_device)
template<typename DataType , typename dev_Selector >
void	sycl_shuffling_test_per_device (dev_Selector s)
	EIGEN_DECLARE_TEST (cxx11_tensor_shuffling_sycl)

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_shuffling_sycl

)

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

References CALL_SUBTEST.

sycl_shuffling_test_per_device()

template<typename DataType , typename dev_Selector >

void sycl_shuffling_test_per_device

(

dev_Selector

s

)

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

References s.

test_simple_shuffling_sycl()

template<typename DataType , int DataLayout, typename IndexType >

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

static

                                                                           {
  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
  Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
  tensor.setRandom();
 
  const size_t buffSize = tensor.size() * sizeof(DataType);
  array<IndexType, 4> shuffles;
  shuffles[0] = 0;
  shuffles[1] = 1;
  shuffles[2] = 2;
  shuffles[3] = 3;
  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
 
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
 
  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
 
  gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
  sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
  sycl_device.synchronize();
 
  VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
  VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
 
  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim3; ++k) {
        for (IndexType l = 0; l < sizeDim4; ++l) {
          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
        }
      }
    }
  }
 
  shuffles[0] = 2;
  shuffles[1] = 3;
  shuffles[2] = 1;
  shuffles[3] = 0;
  array<IndexType, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
  Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(gpu_data3, tensorrangeShuffle);
 
  gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
  sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
  sycl_device.synchronize();
 
  VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
  VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
  VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
  VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
 
  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim3; ++k) {
        for (IndexType l = 0; l < sizeDim4; ++l) {
          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
        }
      }
    }
  }
}

References Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::data(), Eigen::TensorBase< Derived, AccessLevel >::device(), Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::dimension(), i, j, k, Eigen::TensorBase< Derived, AccessLevel >::setRandom(), Eigen::internal::shuffle(), Eigen::TensorBase< Derived, AccessLevel >::shuffle(), Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >::size(), and VERIFY_IS_EQUAL.