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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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13
Eigen/Core
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@ -43,10 +43,12 @@
#else
#define EIGEN_DEVICE_FUNC
#endif
#else
#define EIGEN_DEVICE_FUNC
#endif
#if defined(EIGEN_USE_SYCL)
#define EIGEN_DONT_VECTORIZE
#endif
// When compiling CUDA device code with NVCC, pull in math functions from the
@ -283,6 +285,15 @@
#include <intrin.h>
#endif
#if defined(__SYCL_DEVICE_ONLY__)
#undef min
#undef max
#undef isnan
#undef isinf
#undef isfinite
#include <SYCL/sycl.hpp>
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {

108
Eigen/src/Core/MathFunctions.h
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@ -413,7 +413,7 @@ inline NewType cast(const OldType& x)
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
using std::round;
EIGEN_USING_STD_MATH(round);
return round(x);
}
};
@ -640,7 +640,7 @@ template<typename Scalar>
struct random_default_impl<Scalar, false, true>
{
static inline Scalar run(const Scalar& x, const Scalar& y)
{
{
typedef typename conditional<NumTraits<Scalar>::IsSigned,std::ptrdiff_t,std::size_t>::type ScalarX;
if(y<x)
return x;
@ -954,6 +954,11 @@ inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float log1p(float x) { return cl::sycl::log1p(x); }
EIGEN_ALWAYS_INLINE double log1p(double x) { return cl::sycl::log1p(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float log1p(const float &x) { return ::log1pf(x); }
@ -969,6 +974,11 @@ inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const Scala
return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float pow(float x, float y) { return cl::sycl::pow(x, y); }
EIGEN_ALWAYS_INLINE double pow(double x, double y) { return cl::sycl::pow(x, y); }
#endif // defined(__SYCL_DEVICE_ONLY__)
template<typename T> EIGEN_DEVICE_FUNC bool (isnan) (const T &x) { return internal::isnan_impl(x); }
template<typename T> EIGEN_DEVICE_FUNC bool (isinf) (const T &x) { return internal::isinf_impl(x); }
template<typename T> EIGEN_DEVICE_FUNC bool (isfinite)(const T &x) { return internal::isfinite_impl(x); }
@ -980,6 +990,11 @@ inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float round(float x) { return cl::sycl::round(x); }
EIGEN_ALWAYS_INLINE double round(double x) { return cl::sycl::round(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
template<typename T>
EIGEN_DEVICE_FUNC
T (floor)(const T& x)
@ -988,6 +1003,11 @@ T (floor)(const T& x)
return floor(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float floor(float x) { return cl::sycl::floor(x); }
EIGEN_ALWAYS_INLINE double floor(double x) { return cl::sycl::floor(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float floor(const float &x) { return ::floorf(x); }
@ -1004,6 +1024,11 @@ T (ceil)(const T& x)
return ceil(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float ceil(float x) { return cl::sycl::ceil(x); }
EIGEN_ALWAYS_INLINE double ceil(double x) { return cl::sycl::ceil(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float ceil(const float &x) { return ::ceilf(x); }
@ -1044,6 +1069,11 @@ T sqrt(const T &x)
return sqrt(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float sqrt(float x) { return cl::sycl::sqrt(x); }
EIGEN_ALWAYS_INLINE double sqrt(double x) { return cl::sycl::sqrt(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T log(const T &x) {
@ -1051,6 +1081,12 @@ T log(const T &x) {
return log(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float log(float x) { return cl::sycl::log(x); }
EIGEN_ALWAYS_INLINE double log(double x) { return cl::sycl::log(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float log(const float &x) { return ::logf(x); }
@ -1066,6 +1102,11 @@ typename NumTraits<T>::Real abs(const T &x) {
return abs(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float abs(float x) { return cl::sycl::fabs(x); }
EIGEN_ALWAYS_INLINE double abs(double x) { return cl::sycl::fabs(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float abs(const float &x) { return ::fabsf(x); }
@ -1091,6 +1132,11 @@ T exp(const T &x) {
return exp(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float exp(float x) { return cl::sycl::exp(x); }
EIGEN_ALWAYS_INLINE double exp(double x) { return cl::sycl::exp(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float exp(const float &x) { return ::expf(x); }
@ -1106,6 +1152,11 @@ T cos(const T &x) {
return cos(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float cos(float x) { return cl::sycl::cos(x); }
EIGEN_ALWAYS_INLINE double cos(double x) { return cl::sycl::cos(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float cos(const float &x) { return ::cosf(x); }
@ -1121,6 +1172,11 @@ T sin(const T &x) {
return sin(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float sin(float x) { return cl::sycl::sin(x); }
EIGEN_ALWAYS_INLINE double sin(double x) { return cl::sycl::sin(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float sin(const float &x) { return ::sinf(x); }
@ -1136,6 +1192,11 @@ T tan(const T &x) {
return tan(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float tan(float x) { return cl::sycl::tan(x); }
EIGEN_ALWAYS_INLINE double tan(double x) { return cl::sycl::tan(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tan(const float &x) { return ::tanf(x); }
@ -1151,6 +1212,11 @@ T acos(const T &x) {
return acos(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float acos(float x) { return cl::sycl::acos(x); }
EIGEN_ALWAYS_INLINE double acos(double x) { return cl::sycl::acos(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float acos(const float &x) { return ::acosf(x); }
@ -1166,6 +1232,11 @@ T asin(const T &x) {
return asin(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float asin(float x) { return cl::sycl::asin(x); }
EIGEN_ALWAYS_INLINE double asin(double x) { return cl::sycl::asin(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float asin(const float &x) { return ::asinf(x); }
@ -1181,6 +1252,11 @@ T atan(const T &x) {
return atan(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float atan(float x) { return cl::sycl::atan(x); }
EIGEN_ALWAYS_INLINE double atan(double x) { return cl::sycl::atan(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float atan(const float &x) { return ::atanf(x); }
@ -1197,6 +1273,11 @@ T cosh(const T &x) {
return cosh(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float cosh(float x) { return cl::sycl::cosh(x); }
EIGEN_ALWAYS_INLINE double cosh(double x) { return cl::sycl::cosh(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float cosh(const float &x) { return ::coshf(x); }
@ -1212,6 +1293,11 @@ T sinh(const T &x) {
return sinh(x);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float sinh(float x) { return cl::sycl::sinh(x); }
EIGEN_ALWAYS_INLINE double sinh(double x) { return cl::sycl::sinh(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float sinh(const float &x) { return ::sinhf(x); }
@ -1227,7 +1313,10 @@ T tanh(const T &x) {
return tanh(x);
}
#if (!defined(__CUDACC__)) && EIGEN_FAST_MATH
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float tanh(float x) { return cl::sycl::tanh(x); }
EIGEN_ALWAYS_INLINE double tanh(double x) { return cl::sycl::tanh(x); }
#elif (!defined(__CUDACC__)) && EIGEN_FAST_MATH
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tanh(float x) { return internal::generic_fast_tanh_float(x); }
#endif
@ -1247,6 +1336,11 @@ T fmod(const T& a, const T& b) {
return fmod(a, b);
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float fmod(float x, float y) { return cl::sycl::fmod(x, y); }
EIGEN_ALWAYS_INLINE double fmod(double x, double y) { return cl::sycl::fmod(x, y); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template <>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
@ -1389,13 +1483,13 @@ template<> struct random_impl<bool>
template<> struct scalar_fuzzy_impl<bool>
{
typedef bool RealScalar;
template<typename OtherScalar> EIGEN_DEVICE_FUNC
static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&)
{
return !x;
}
EIGEN_DEVICE_FUNC
static inline bool isApprox(bool x, bool y, bool)
{
@ -1407,10 +1501,10 @@ template<> struct scalar_fuzzy_impl<bool>
{
return (!x) || y;
}
};
} // end namespace internal
} // end namespace Eigen

2
unsupported/Eigen/CXX11/Tensor
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@ -13,7 +13,7 @@
#include "../../../Eigen/Core"
#ifdef EIGEN_USE_SYCL
#if defined(EIGEN_USE_SYCL)
#undef min
#undef max
#undef isnan

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));
}