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Specialised basic math functions for SYCL device.

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This commit is contained in:
Luke Iwanski
2016-11-17 11:47:13 +00:00
parent b5c75351e3
commit c5130dedbe
5 changed files with 198 additions and 9 deletions
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1
unsupported/test/CMakeLists.txt
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@@ -147,6 +147,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_reduction_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_morphing_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_builtins_sycl "-std=c++11")
endif(EIGEN_TEST_SYCL)
# It should be safe to always run these tests as there is some fallback code for
# older compiler that don't support cxx11.

83
unsupported/test/cxx11_tensor_builtins_sycl.cpp Normal file
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@@ -0,0 +1,83 @@
// 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> in1(tensorRange); \
Tensor<SCALAR, 3> out1(tensorRange); \
in1 = in1.random(); \
SCALAR* gpu_data1 = static_cast<SCALAR*>(sycl_device.allocate(in1.size()*sizeof(SCALAR))); \
TensorMap<Tensor<SCALAR, 3>> gpu1(gpu_data1, tensorRange); \
sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(SCALAR)); \
gpu1.device(sycl_device) = gpu1.FUNC(); \
sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1,(out1.size())*sizeof(SCALAR)); \
for (int i = 0; i < in1.size(); ++i) { \
VERIFY_IS_APPROX(out1(i), std::FUNC(in1(i))); \
} \
sycl_device.deallocate(gpu_data1); \
}
#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));
}