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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_TEST_FUNC cxx11_tensor_builtins_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
namespace std {
template <typename T> T rsqrt(T x) { return 1 / std::sqrt(x); }
template <typename T> T square(T x) { return x * x; }
template <typename T> T cube(T x) { return x * x * x; }
template <typename T> T inverse(T x) { return 1 / x; }
}
#define TEST_UNARY_BUILTINS_FOR_SCALAR(FUNC, SCALAR) \
{ \
Tensor<SCALAR, 3> in(tensorRange); \
Tensor<SCALAR, 3> out(tensorRange); \
in = in.random() + static_cast<SCALAR>(0.01); \
SCALAR *gpu_data = static_cast<SCALAR *>( \
sycl_device.allocate(in.size() * sizeof(SCALAR))); \
SCALAR *gpu_data_out = static_cast<SCALAR *>( \
sycl_device.allocate(out.size() * sizeof(SCALAR))); \
TensorMap<Tensor<SCALAR, 3>> gpu(gpu_data, tensorRange); \
TensorMap<Tensor<SCALAR, 3>> gpu_out(gpu_data_out, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
(in.size()) * sizeof(SCALAR)); \
gpu_out.device(sycl_device) = gpu.FUNC(); \
sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
(out.size()) * sizeof(SCALAR)); \
for (int i = 0; i < in.size(); ++i) { \
VERIFY_IS_APPROX(out(i), std::FUNC(in(i))); \
} \
sycl_device.deallocate(gpu_data); \
sycl_device.deallocate(gpu_data_out); \
}
#define TEST_UNARY_BUILTINS(SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(sqrt, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(rsqrt, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(square, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(cube, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(inverse, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(tanh, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(exp, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(log, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(ceil, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(floor, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(round, SCALAR) \
TEST_UNARY_BUILTINS_FOR_SCALAR(log1p, SCALAR)
static void test_builtin_unary_sycl(const Eigen::SyclDevice &sycl_device) {
int sizeDim1 = 100;
int sizeDim2 = 100;
int sizeDim3 = 100;
array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
TEST_UNARY_BUILTINS(float)
TEST_UNARY_BUILTINS(double)
}
void test_cxx11_tensor_builtins_sycl() {
cl::sycl::gpu_selector s;
Eigen::SyclDevice sycl_device(s);
CALL_SUBTEST(test_builtin_unary_sycl(sycl_device));
}
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