Quelle cxx11_tensor_chipping_sycl.cpp Sprache: C

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"

#include <Eigen/CXX11/Tensor>

using Eigen::Tensor;

template <typename DataType, int DataLayout, typename IndexType>
static void test_static_chip_sycl(const Eigen::SyclDevice& sycl_device)
{
  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  IndexType sizeDim5 = 11;

  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};

  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);

  tensor.setRandom();

  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));

  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
  gpu_chip1.device(sycl_device)=gpu_tensor.template chip<0l>(1l);
  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);

  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim2; ++i) {
    for (IndexType j = 0; j < sizeDim3; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1l,i,j,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip2(chip2TensorRange);
  const size_t chip2TensorBuffSize =chip2.size()*sizeof(DataType);
  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);

  gpu_chip2.device(sycl_device)=gpu_tensor.template chip<1l>(1l);
  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);

  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim3; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1l,j,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip3(chip3TensorRange);
  const size_t chip3TensorBuffSize =chip3.size()*sizeof(DataType);
  DataType* gpu_data_chip3  = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);

  gpu_chip3.device(sycl_device)=gpu_tensor.template chip<2l>(2l);
  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);

  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2l,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip4(chip4TensorRange);
  const size_t chip4TensorBuffSize =chip4.size()*sizeof(DataType);
  DataType* gpu_data_chip4  = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);

  gpu_chip4.device(sycl_device)=gpu_tensor.template chip<3l>(5l);
  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);

  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);

  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 < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5l,l));
        }
      }
    }
  }

  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
  Tensor<DataType, 4, DataLayout,IndexType> chip5(chip5TensorRange);
  const size_t chip5TensorBuffSize =chip5.size()*sizeof(DataType);
  DataType* gpu_data_chip5  = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);

  gpu_chip5.device(sycl_device)=gpu_tensor.template chip<4l>(7l);
  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);

  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
  VERIFY_IS_EQUAL(chip5.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(chip5(i,j,k,l), tensor(i,j,k,l,7l));
        }
      }
    }
  }

  sycl_device.deallocate(gpu_data_tensor);
  sycl_device.deallocate(gpu_data_chip1);
  sycl_device.deallocate(gpu_data_chip2);
  sycl_device.deallocate(gpu_data_chip3);
  sycl_device.deallocate(gpu_data_chip4);
  sycl_device.deallocate(gpu_data_chip5);
}

template <typename DataType, int DataLayout, typename IndexType>
static void test_dynamic_chip_sycl(const Eigen::SyclDevice& sycl_device)
{
  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  IndexType sizeDim5 = 11;

  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};

  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);

  tensor.setRandom();

  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));

  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
  gpu_chip1.device(sycl_device)=gpu_tensor.chip(1l,0l);
  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);

  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim2; ++i) {
    for (IndexType j = 0; j < sizeDim3; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1l,i,j,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip2(chip2TensorRange);
  const size_t chip2TensorBuffSize =chip2.size()*sizeof(DataType);
  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);

  gpu_chip2.device(sycl_device)=gpu_tensor.chip(1l,1l);
  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);

  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim3; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1l,j,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip3(chip3TensorRange);
  const size_t chip3TensorBuffSize =chip3.size()*sizeof(DataType);
  DataType* gpu_data_chip3  = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);

  gpu_chip3.device(sycl_device)=gpu_tensor.chip(2l,2l);
  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);

  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);

  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim4; ++k) {
        for (IndexType l = 0; l < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2l,k,l));
        }
      }
    }
  }

  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> chip4(chip4TensorRange);
  const size_t chip4TensorBuffSize =chip4.size()*sizeof(DataType);
  DataType* gpu_data_chip4  = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);

  gpu_chip4.device(sycl_device)=gpu_tensor.chip(5l,3l);
  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);

  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);

  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 < sizeDim5; ++l) {
          VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5l,l));
        }
      }
    }
  }

  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
  Tensor<DataType, 4, DataLayout,IndexType> chip5(chip5TensorRange);
  const size_t chip5TensorBuffSize =chip5.size()*sizeof(DataType);
  DataType* gpu_data_chip5  = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);

  gpu_chip5.device(sycl_device)=gpu_tensor.chip(7l,4l);
  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);

  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
  VERIFY_IS_EQUAL(chip5.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(chip5(i,j,k,l), tensor(i,j,k,l,7l));
        }
      }
    }
  }
  sycl_device.deallocate(gpu_data_tensor);
  sycl_device.deallocate(gpu_data_chip1);
  sycl_device.deallocate(gpu_data_chip2);
  sycl_device.deallocate(gpu_data_chip3);
  sycl_device.deallocate(gpu_data_chip4);
  sycl_device.deallocate(gpu_data_chip5);
}

template <typename DataType, int DataLayout, typename IndexType>
static void test_chip_in_expr(const Eigen::SyclDevice& sycl_device) {

  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  IndexType sizeDim5 = 11;

  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};

  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);

  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);
  Tensor<DataType, 4, DataLayout,IndexType> tensor1(chip1TensorRange);
  tensor.setRandom();
  tensor1.setRandom();

  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
  DataType* gpu_data_tensor1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));

  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor1(gpu_data_tensor1, chip1TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
  sycl_device.memcpyHostToDevice(gpu_data_tensor1, tensor1.data(), chip1TensorBuffSize);
  gpu_chip1.device(sycl_device)=gpu_tensor.template chip<0l>(0l) + gpu_tensor1;
  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);

  for (int i = 0; i < sizeDim2; ++i) {
    for (int j = 0; j < sizeDim3; ++j) {
      for (int k = 0; k < sizeDim4; ++k) {
        for (int l = 0; l < sizeDim5; ++l) {
          float expected = tensor(0l,i,j,k,l) + tensor1(i,j,k,l);
          VERIFY_IS_EQUAL(chip1(i,j,k,l), expected);
        }
      }
    }
  }

  array<IndexType, 3> chip2TensorRange = {{sizeDim2, sizeDim4, sizeDim5}};
  Tensor<DataType, 3, DataLayout,IndexType> tensor2(chip2TensorRange);
  Tensor<DataType, 3, DataLayout,IndexType> chip2(chip2TensorRange);
  tensor2.setRandom();
  const size_t chip2TensorBuffSize =tensor2.size()*sizeof(DataType);
  DataType* gpu_data_tensor2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
  TensorMap<Tensor<DataType, 3, DataLayout,IndexType>> gpu_tensor2(gpu_data_tensor2, chip2TensorRange);
  TensorMap<Tensor<DataType, 3, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_tensor2, tensor2.data(), chip2TensorBuffSize);
  gpu_chip2.device(sycl_device)=gpu_tensor.template chip<0l>(0l).template chip<1l>(2l) + gpu_tensor2;
  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);

  for (int i = 0; i < sizeDim2; ++i) {
    for (int j = 0; j < sizeDim4; ++j) {
      for (int k = 0; k < sizeDim5; ++k) {
        float expected = tensor(0l,i,2l,j,k) + tensor2(i,j,k);
        VERIFY_IS_EQUAL(chip2(i,j,k), expected);
      }
    }
  }
  sycl_device.deallocate(gpu_data_tensor);
  sycl_device.deallocate(gpu_data_tensor1);
  sycl_device.deallocate(gpu_data_chip1);
  sycl_device.deallocate(gpu_data_tensor2);
  sycl_device.deallocate(gpu_data_chip2);
}

template <typename DataType, int DataLayout, typename IndexType>
static void test_chip_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device)
{

  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  IndexType sizeDim5 = 11;

  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
  array<IndexType, 4> input2TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};

  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
  Tensor<DataType, 5, DataLayout,IndexType> input1(tensorRange);
  Tensor<DataType, 4, DataLayout,IndexType> input2(input2TensorRange);
  input1.setRandom();
  input2.setRandom();

  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
  const size_t input2TensorBuffSize =input2.size()*sizeof(DataType);
  std::cout << tensorBuffSize << " , "<<  input2TensorBuffSize << std::endl;
  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_input1  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  DataType* gpu_data_input2  = static_cast<DataType*>(sycl_device.allocate(input2TensorBuffSize));

  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_input1(gpu_data_input1, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input2(gpu_data_input2, input2TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input1, input1.data(), tensorBuffSize);
  gpu_tensor.device(sycl_device)=gpu_input1;
  sycl_device.memcpyHostToDevice(gpu_data_input2, input2.data(), input2TensorBuffSize);
  gpu_tensor.template chip<0l>(1l).device(sycl_device)=gpu_input2;
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k < sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (i != 1) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m));
            }
          }
        }
      }
    }
  }

  gpu_tensor.device(sycl_device)=gpu_input1;
  array<IndexType, 4> input3TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> input3(input3TensorRange);
  input3.setRandom();

  const size_t input3TensorBuffSize =input3.size()*sizeof(DataType);
  DataType* gpu_data_input3  = static_cast<DataType*>(sycl_device.allocate(input3TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input3(gpu_data_input3, input3TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input3, input3.data(), input3TensorBuffSize);
  gpu_tensor.template chip<1l>(1l).device(sycl_device)=gpu_input3;
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k <sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (j != 1) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m));
            }
          }
        }
      }
    }
  }

  gpu_tensor.device(sycl_device)=gpu_input1;
  array<IndexType, 4> input4TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> input4(input4TensorRange);
  input4.setRandom();

  const size_t input4TensorBuffSize =input4.size()*sizeof(DataType);
  DataType* gpu_data_input4  = static_cast<DataType*>(sycl_device.allocate(input4TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input4(gpu_data_input4, input4TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input4, input4.data(), input4TensorBuffSize);
  gpu_tensor.template chip<2l>(3l).device(sycl_device)=gpu_input4;
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k <sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (k != 3) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m));
            }
          }
        }
      }
    }
  }

  gpu_tensor.device(sycl_device)=gpu_input1;
  array<IndexType, 4> input5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
  Tensor<DataType, 4, DataLayout,IndexType> input5(input5TensorRange);
  input5.setRandom();

  const size_t input5TensorBuffSize =input5.size()*sizeof(DataType);
  DataType* gpu_data_input5  = static_cast<DataType*>(sycl_device.allocate(input5TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input5(gpu_data_input5, input5TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input5, input5.data(), input5TensorBuffSize);
  gpu_tensor.template chip<3l>(4l).device(sycl_device)=gpu_input5;
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k <sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (l != 4) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m));
            }
          }
        }
      }
    }
  }
  gpu_tensor.device(sycl_device)=gpu_input1;
  array<IndexType, 4> input6TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
  Tensor<DataType, 4, DataLayout,IndexType> input6(input6TensorRange);
  input6.setRandom();

  const size_t input6TensorBuffSize =input6.size()*sizeof(DataType);
  DataType* gpu_data_input6  = static_cast<DataType*>(sycl_device.allocate(input6TensorBuffSize));
  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input6(gpu_data_input6, input6TensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input6, input6.data(), input6TensorBuffSize);
  gpu_tensor.template chip<4l>(5l).device(sycl_device)=gpu_input6;
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k <sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (m != 5) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l));
            }
          }
        }
      }
    }
  }

  gpu_tensor.device(sycl_device)=gpu_input1;
  Tensor<DataType, 5, DataLayout,IndexType> input7(tensorRange);
  input7.setRandom();

  DataType* gpu_data_input7  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_input7(gpu_data_input7, tensorRange);

  sycl_device.memcpyHostToDevice(gpu_data_input7, input7.data(), tensorBuffSize);
  gpu_tensor.chip(0l,0l).device(sycl_device)=gpu_input7.chip(0l,0l);
  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);

  for (int i = 0; i < sizeDim1; ++i) {
    for (int j = 0; j < sizeDim2; ++j) {
      for (int k = 0; k <sizeDim3; ++k) {
        for (int l = 0; l < sizeDim4; ++l) {
          for (int m = 0; m < sizeDim5; ++m) {
            if (i != 0) {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
            } else {
              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input7(i,j,k,l,m));
            }
          }
        }
      }
    }
  }
  sycl_device.deallocate(gpu_data_tensor);
  sycl_device.deallocate(gpu_data_input1);
  sycl_device.deallocate(gpu_data_input2);
  sycl_device.deallocate(gpu_data_input3);
  sycl_device.deallocate(gpu_data_input4);
  sycl_device.deallocate(gpu_data_input5);
  sycl_device.deallocate(gpu_data_input6);
  sycl_device.deallocate(gpu_data_input7);

}

template<typename DataType, typename dev_Selector> void sycl_chipping_test_per_device(dev_Selector s){
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
/* test_static_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_static_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
  test_dynamic_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_dynamic_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
  test_chip_in_expr<DataType, RowMajor, int64_t>(sycl_device);
  test_chip_in_expr<DataType, ColMajor, int64_t>(sycl_device);*/
  test_chip_as_lvalue_sycl<DataType, RowMajor, int64_t>(sycl_device);
// test_chip_as_lvalue_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_chipping_sycl)
{
  for (const auto& device :Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(sycl_chipping_test_per_device<float>(device));
  }
}

quality42%

¤ Dauer der Verarbeitung: 0.15 Sekunden (vorverarbeitet) ¤

Wurzel

Suchen

Beweissystem der NASA

Beweissystem Isabelle

NIST Cobol Testsuite

Cephes Mathematical Library

Wiener Entwicklungsmethode

Haftungshinweis

Die Informationen auf dieser Webseite wurden nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit, noch Qualität der bereit gestellten Informationen zugesichert.

Bemerkung:

Die farbliche Syntaxdarstellung ist noch experimentell.