cxx11_tensor_argmax_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 Layout, typename DenseIndex >
static void	test_sycl_simple_argmax (const Eigen::SyclDevice &sycl_device)
template<typename DataType , int DataLayout, typename DenseIndex >
static void	test_sycl_argmax_dim (const Eigen::SyclDevice &sycl_device)
template<typename DataType , int DataLayout, typename DenseIndex >
static void	test_sycl_argmin_dim (const Eigen::SyclDevice &sycl_device)
template<typename DataType , typename Device_Selector >
void	sycl_argmax_test_per_device (const Device_Selector &d)
	EIGEN_DECLARE_TEST (cxx11_tensor_argmax_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_argmax_sycl

)

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

References CALL_SUBTEST.

sycl_argmax_test_per_device()

template<typename DataType , typename Device_Selector >

void sycl_argmax_test_per_device

(

const Device_Selector &

d

)

                                                           {
  QueueInterface queueInterface(d);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
  test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
  test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
  test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
  test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
  test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
}

test_sycl_argmax_dim()

template<typename DataType , int DataLayout, typename DenseIndex >

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

static

                                                                     {
  DenseIndex sizeDim0 = 9;
  DenseIndex sizeDim1 = 3;
  DenseIndex sizeDim2 = 5;
  DenseIndex sizeDim3 = 7;
  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
 
  std::vector<DenseIndex> dims;
  dims.push_back(sizeDim0);
  dims.push_back(sizeDim1);
  dims.push_back(sizeDim2);
  dims.push_back(sizeDim3);
  for (DenseIndex dim = 0; dim < 4; ++dim) {
    array<DenseIndex, 3> out_shape;
    for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
 
    Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
 
    array<DenseIndex, 4> ix;
    for (DenseIndex i = 0; i < sizeDim0; ++i) {
      for (DenseIndex j = 0; j < sizeDim1; ++j) {
        for (DenseIndex k = 0; k < sizeDim2; ++k) {
          for (DenseIndex l = 0; l < sizeDim3; ++l) {
            ix[0] = i;
            ix[1] = j;
            ix[2] = k;
            ix[3] = l;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l)
            // = 10.0
            tensor(ix) = static_cast<DataType>((ix[dim] != 0) ? -1.0 : 10.0);
          }
        }
      }
    }
 
    std::size_t in_bytes = tensor.size() * sizeof(DataType);
    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
 
    DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
    DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
 
    Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
        d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
 
    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
    gpu_out.device(sycl_device) = gpu_in.argmax(dim);
    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
 
    VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
                    size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
 
    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
      // Expect max to be in the first index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
    }
 
    sycl_device.synchronize();
 
    for (DenseIndex i = 0; i < sizeDim0; ++i) {
      for (DenseIndex j = 0; j < sizeDim1; ++j) {
        for (DenseIndex k = 0; k < sizeDim2; ++k) {
          for (DenseIndex l = 0; l < sizeDim3; ++l) {
            ix[0] = i;
            ix[1] = j;
            ix[2] = k;
            ix[3] = l;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
            tensor(ix) = static_cast<DataType>((ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0);
          }
        }
      }
    }
 
    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
    gpu_out.device(sycl_device) = gpu_in.argmax(dim);
    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
 
    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
      // Expect max to be in the last index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
    }
    sycl_device.deallocate(d_in);
    sycl_device.deallocate(d_out);
  }
}

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

test_sycl_argmin_dim()

template<typename DataType , int DataLayout, typename DenseIndex >

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

static

                                                                     {
  DenseIndex sizeDim0 = 9;
  DenseIndex sizeDim1 = 3;
  DenseIndex sizeDim2 = 5;
  DenseIndex sizeDim3 = 7;
  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
 
  std::vector<DenseIndex> dims;
  dims.push_back(sizeDim0);
  dims.push_back(sizeDim1);
  dims.push_back(sizeDim2);
  dims.push_back(sizeDim3);
  for (DenseIndex dim = 0; dim < 4; ++dim) {
    array<DenseIndex, 3> out_shape;
    for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
 
    Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
 
    array<DenseIndex, 4> ix;
    for (DenseIndex i = 0; i < sizeDim0; ++i) {
      for (DenseIndex j = 0; j < sizeDim1; ++j) {
        for (DenseIndex k = 0; k < sizeDim2; ++k) {
          for (DenseIndex l = 0; l < sizeDim3; ++l) {
            ix[0] = i;
            ix[1] = j;
            ix[2] = k;
            ix[3] = l;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
            tensor(ix) = static_cast<DataType>((ix[dim] != 0) ? 1.0 : -10.0);
          }
        }
      }
    }
 
    std::size_t in_bytes = tensor.size() * sizeof(DataType);
    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
 
    DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
    DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
 
    Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
        d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
 
    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
    gpu_out.device(sycl_device) = gpu_in.argmin(dim);
    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
 
    VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
                    size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
 
    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
      // Expect max to be in the first index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
    }
 
    sycl_device.synchronize();
 
    for (DenseIndex i = 0; i < sizeDim0; ++i) {
      for (DenseIndex j = 0; j < sizeDim1; ++j) {
        for (DenseIndex k = 0; k < sizeDim2; ++k) {
          for (DenseIndex l = 0; l < sizeDim3; ++l) {
            ix[0] = i;
            ix[1] = j;
            ix[2] = k;
            ix[3] = l;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
            tensor(ix) = static_cast<DataType>((ix[dim] != tensor.dimension(dim) - 1) ? 1.0 : -20.0);
          }
        }
      }
    }
 
    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
    gpu_out.device(sycl_device) = gpu_in.argmin(dim);
    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
 
    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
      // Expect max to be in the last index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
    }
    sycl_device.deallocate(d_in);
    sycl_device.deallocate(d_out);
  }
}

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

test_sycl_simple_argmax()

template<typename DataType , int Layout, typename DenseIndex >

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

static

                                                                        {
  Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2, 2, 2}});
  Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
  Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
  in.setRandom();
  in *= in.constant(static_cast<DataType>(100.0));
  in(0, 0, 0) = static_cast<DataType>(-1000.0);
  in(1, 1, 1) = static_cast<DataType>(1000.0);
 
  std::size_t in_bytes = in.size() * sizeof(DataType);
  std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
 
  DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
  DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
  DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
 
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in,
                                                                           Eigen::array<DenseIndex, 3>{{2, 2, 2}});
  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
  sycl_device.memcpyHostToDevice(d_in, in.data(), in_bytes);
 
  gpu_out_max.device(sycl_device) = gpu_in.argmax();
  gpu_out_min.device(sycl_device) = gpu_in.argmin();
 
  sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
  sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
 
  VERIFY_IS_EQUAL(out_max(), 2 * 2 * 2 - 1);
  VERIFY_IS_EQUAL(out_min(), 0);
 
  sycl_device.deallocate(d_in);
  sycl_device.deallocate(d_out_max);
  sycl_device.deallocate(d_out_min);
}

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