Quelle cxx11_tensor_forced_eval_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>
//
// 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 <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;
template <typename DataType, int DataLayout, typename IndexType>
void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {

  IndexType sizeDim1 = 100;
  IndexType sizeDim2 = 20;
  IndexType sizeDim3 = 20;
  Eigen::array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);

  DataType * gpu_in1_data  = static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(DataType)));
  DataType * gpu_in2_data  = static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(DataType)));
  DataType * gpu_out_data =  static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));

  in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
  in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));

  // creating TensorMap from tensor
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(DataType));
  /// c=(a+b)*b
  gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim3; ++k) {
        VERIFY_IS_APPROX(out(i, j, k),
                         (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
      }
    }
  }
  printf("(a+b)*b Test Passed\n");
  sycl_device.deallocate(gpu_in1_data);
  sycl_device.deallocate(gpu_in2_data);
  sycl_device.deallocate(gpu_out_data);

}

template <typename DataType, typename Dev_selector> void tensorForced_evalperDevice(Dev_selector s){
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval_sycl) {
  for (const auto& device :Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
    CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
  }
}

quality80%

¤ Dauer der Verarbeitung: 0.3 Sekunden ¤

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.