cxx11_tensor_device_sycl.cpp

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// 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>
#include <stdint.h>
#include <iostream>

template <typename DataType, int DataLayout, typename IndexType>
void test_device_memory(const Eigen::SyclDevice &sycl_device) {
  std::cout << "Running on : "
            << sycl_device.sycl_queue().get_device(). template get_info<cl::sycl::info::device::name>()
            <<std::endl;
  IndexType sizeDim1 = 100;
  array<IndexType, 1> tensorRange = {{sizeDim1}};
  Tensor<DataType, 1, DataLayout,IndexType> in(tensorRange);
  Tensor<DataType, 1, DataLayout,IndexType> in1(tensorRange);
  memset(in1.data(), 1, in1.size() * sizeof(DataType));
  DataType* gpu_in_data  = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType)));
  sycl_device.memset(gpu_in_data, 1, in.size()*sizeof(DataType));
  sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType));
  for (IndexType i=0; i<in.size(); i++) {
    VERIFY_IS_EQUAL(in(i), in1(i));
  }
  sycl_device.deallocate(gpu_in_data);
}

template <typename DataType, int DataLayout, typename IndexType>
void test_device_exceptions(const Eigen::SyclDevice &sycl_device) {
  VERIFY(sycl_device.ok());
  IndexType sizeDim1 = 100;
  array<IndexType, 1> tensorDims = {{sizeDim1}};
  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1*sizeof(DataType)));
  sycl_device.memset(gpu_data, 1, sizeDim1*sizeof(DataType));

  TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> in(gpu_data, tensorDims);
  TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> out(gpu_data, tensorDims);
  out.device(sycl_device) = in / in.constant(0);

  sycl_device.synchronize();
  VERIFY(!sycl_device.ok());
  sycl_device.deallocate(gpu_data);
}

template<typename DataType> void sycl_device_test_per_device(const cl::sycl::device& d){
  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
  QueueInterface queueInterface(d);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_device_memory<DataType, RowMajor, int64_t>(sycl_device);
  test_device_memory<DataType, ColMajor, int64_t>(sycl_device);
  //test_device_exceptions<DataType, RowMajor>(sycl_device);
  //test_device_exceptions<DataType, ColMajor>(sycl_device);
}

EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl) {
  for (const auto& device :Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(sycl_device_test_per_device<float>(device));
  }
}