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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
static const float error_threshold =1e-8f;
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
struct Generator1D {
Generator1D() { }
float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const {
return coordinates[0];
}
};
template <typename DataType, int DataLayout, typename IndexType>
static void test_1D_sycl(const Eigen::SyclDevice& sycl_device)
{
IndexType sizeDim1 = 6;
array<IndexType, 1> tensorRange = {{sizeDim1}};
Tensor<DataType, 1, DataLayout,IndexType> vec(tensorRange);
Tensor<DataType, 1, DataLayout,IndexType> result(tensorRange);
const size_t tensorBuffSize =vec.size()*sizeof(DataType);
DataType* gpu_data_vec = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_vec(gpu_data_vec, tensorRange);
TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data_vec, vec.data(), tensorBuffSize);
gpu_result.device(sycl_device)=gpu_vec.generate(Generator1D());
sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
for (IndexType i = 0; i < 6; ++i) {
VERIFY_IS_EQUAL(result(i), i);
}
}
struct Generator2D {
Generator2D() { }
float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const {
return 3 * coordinates[0] + 11 * coordinates[1];
}
};
template <typename DataType, int DataLayout, typename IndexType>
static void test_2D_sycl(const Eigen::SyclDevice& sycl_device)
{
IndexType sizeDim1 = 5;
IndexType sizeDim2 = 7;
array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange);
Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange);
const size_t tensorBuffSize =matrix.size()*sizeof(DataType);
DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
gpu_result.device(sycl_device)=gpu_matrix.generate(Generator2D());
sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
for (IndexType i = 0; i < 5; ++i) {
for (IndexType j = 0; j < 5; ++j) {
VERIFY_IS_EQUAL(result(i, j), 3*i + 11*j);
}
}
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_gaussian_sycl(const Eigen::SyclDevice& sycl_device)
{
IndexType rows = 32;
IndexType cols = 48;
array<DataType, 2> means;
means[0] = rows / 2.0f;
means[1] = cols / 2.0f;
array<DataType, 2> std_devs;
std_devs[0] = 3.14f;
std_devs[1] = 2.7f;
internal::GaussianGenerator<DataType, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);
array<IndexType, 2> tensorRange = {{rows, cols}};
Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange);
Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange);
const size_t tensorBuffSize =matrix.size()*sizeof(DataType);
DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
gpu_result.device(sycl_device)=gpu_matrix.generate(gaussian_gen);
sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
for (IndexType i = 0; i < rows; ++i) {
for (IndexType j = 0; j < cols; ++j) {
DataType g_rows = powf(rows/2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
DataType g_cols = powf(cols/2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
DataType gaussian = expf(-g_rows - g_cols);
Eigen::internal::isApprox(result(i, j), gaussian, error_threshold);
}
}
}
template<typename DataType, typename dev_Selector> void sycl_generator_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_1D_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_1D_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_2D_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_2D_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_gaussian_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_gaussian_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_generator_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_generator_test_per_device<float>(device));
}
}
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