forked from OSchip/llvm-project
513 lines
18 KiB
C++
513 lines
18 KiB
C++
/*===---- __clang_cuda_cmath.h - Device-side CUDA cmath support ------------===
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*
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* Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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* See https://llvm.org/LICENSE.txt for license information.
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* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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*
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*===-----------------------------------------------------------------------===
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*/
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#ifndef __CLANG_CUDA_CMATH_H__
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#define __CLANG_CUDA_CMATH_H__
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#ifndef __CUDA__
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#error "This file is for CUDA compilation only."
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#endif
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#ifndef __OPENMP_NVPTX__
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#include <limits>
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#endif
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// CUDA lets us use various std math functions on the device side. This file
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// works in concert with __clang_cuda_math_forward_declares.h to make this work.
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//
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// Specifically, the forward-declares header declares __device__ overloads for
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// these functions in the global namespace, then pulls them into namespace std
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// with 'using' statements. Then this file implements those functions, after
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// their implementations have been pulled in.
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//
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// It's important that we declare the functions in the global namespace and pull
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// them into namespace std with using statements, as opposed to simply declaring
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// these functions in namespace std, because our device functions need to
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// overload the standard library functions, which may be declared in the global
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// namespace or in std, depending on the degree of conformance of the stdlib
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// implementation. Declaring in the global namespace and pulling into namespace
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// std covers all of the known knowns.
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#ifdef __OPENMP_NVPTX__
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#define __DEVICE__ static constexpr __attribute__((always_inline, nothrow))
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#else
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#define __DEVICE__ static __device__ __inline__ __attribute__((always_inline))
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#endif
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__DEVICE__ long long abs(long long __n) { return ::llabs(__n); }
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__DEVICE__ long abs(long __n) { return ::labs(__n); }
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__DEVICE__ float abs(float __x) { return ::fabsf(__x); }
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__DEVICE__ double abs(double __x) { return ::fabs(__x); }
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__DEVICE__ float acos(float __x) { return ::acosf(__x); }
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__DEVICE__ float asin(float __x) { return ::asinf(__x); }
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__DEVICE__ float atan(float __x) { return ::atanf(__x); }
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__DEVICE__ float atan2(float __x, float __y) { return ::atan2f(__x, __y); }
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__DEVICE__ float ceil(float __x) { return ::ceilf(__x); }
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__DEVICE__ float cos(float __x) { return ::cosf(__x); }
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__DEVICE__ float cosh(float __x) { return ::coshf(__x); }
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__DEVICE__ float exp(float __x) { return ::expf(__x); }
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__DEVICE__ float fabs(float __x) { return ::fabsf(__x); }
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__DEVICE__ float floor(float __x) { return ::floorf(__x); }
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__DEVICE__ float fmod(float __x, float __y) { return ::fmodf(__x, __y); }
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__DEVICE__ int fpclassify(float __x) {
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return __builtin_fpclassify(FP_NAN, FP_INFINITE, FP_NORMAL, FP_SUBNORMAL,
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FP_ZERO, __x);
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}
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__DEVICE__ int fpclassify(double __x) {
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return __builtin_fpclassify(FP_NAN, FP_INFINITE, FP_NORMAL, FP_SUBNORMAL,
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FP_ZERO, __x);
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}
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__DEVICE__ float frexp(float __arg, int *__exp) {
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return ::frexpf(__arg, __exp);
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}
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// For inscrutable reasons, the CUDA headers define these functions for us on
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// Windows.
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#if !defined(_MSC_VER) || defined(__OPENMP_NVPTX__)
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// For OpenMP we work around some old system headers that have non-conforming
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// `isinf(float)` and `isnan(float)` implementations that return an `int`. We do
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// this by providing two versions of these functions, differing only in the
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// return type. To avoid conflicting definitions we disable implicit base
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// function generation. That means we will end up with two specializations, one
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// per type, but only one has a base function defined by the system header.
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#if defined(__OPENMP_NVPTX__)
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#pragma omp begin declare variant match( \
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implementation = {extension(disable_implicit_base)})
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// FIXME: We lack an extension to customize the mangling of the variants, e.g.,
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// add a suffix. This means we would clash with the names of the variants
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// (note that we do not create implicit base functions here). To avoid
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// this clash we add a new trait to some of them that is always true
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// (this is LLVM after all ;)). It will only influence the mangled name
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// of the variants inside the inner region and avoid the clash.
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#pragma omp begin declare variant match(implementation = {vendor(llvm)})
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__DEVICE__ int isinf(float __x) { return ::__isinff(__x); }
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__DEVICE__ int isinf(double __x) { return ::__isinf(__x); }
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__DEVICE__ int isfinite(float __x) { return ::__finitef(__x); }
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__DEVICE__ int isfinite(double __x) { return ::__isfinited(__x); }
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__DEVICE__ int isnan(float __x) { return ::__isnanf(__x); }
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__DEVICE__ int isnan(double __x) { return ::__isnan(__x); }
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#pragma omp end declare variant
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#endif
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__DEVICE__ bool isinf(float __x) { return ::__isinff(__x); }
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__DEVICE__ bool isinf(double __x) { return ::__isinf(__x); }
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__DEVICE__ bool isfinite(float __x) { return ::__finitef(__x); }
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// For inscrutable reasons, __finite(), the double-precision version of
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// __finitef, does not exist when compiling for MacOS. __isfinited is available
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// everywhere and is just as good.
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__DEVICE__ bool isfinite(double __x) { return ::__isfinited(__x); }
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__DEVICE__ bool isnan(float __x) { return ::__isnanf(__x); }
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__DEVICE__ bool isnan(double __x) { return ::__isnan(__x); }
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#if defined(__OPENMP_NVPTX__)
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#pragma omp end declare variant
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#endif
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#endif
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__DEVICE__ bool isgreater(float __x, float __y) {
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return __builtin_isgreater(__x, __y);
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}
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__DEVICE__ bool isgreater(double __x, double __y) {
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return __builtin_isgreater(__x, __y);
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}
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__DEVICE__ bool isgreaterequal(float __x, float __y) {
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return __builtin_isgreaterequal(__x, __y);
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}
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__DEVICE__ bool isgreaterequal(double __x, double __y) {
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return __builtin_isgreaterequal(__x, __y);
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}
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__DEVICE__ bool isless(float __x, float __y) {
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return __builtin_isless(__x, __y);
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}
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__DEVICE__ bool isless(double __x, double __y) {
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return __builtin_isless(__x, __y);
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}
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__DEVICE__ bool islessequal(float __x, float __y) {
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return __builtin_islessequal(__x, __y);
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}
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__DEVICE__ bool islessequal(double __x, double __y) {
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return __builtin_islessequal(__x, __y);
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}
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__DEVICE__ bool islessgreater(float __x, float __y) {
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return __builtin_islessgreater(__x, __y);
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}
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__DEVICE__ bool islessgreater(double __x, double __y) {
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return __builtin_islessgreater(__x, __y);
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}
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__DEVICE__ bool isnormal(float __x) { return __builtin_isnormal(__x); }
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__DEVICE__ bool isnormal(double __x) { return __builtin_isnormal(__x); }
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__DEVICE__ bool isunordered(float __x, float __y) {
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return __builtin_isunordered(__x, __y);
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}
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__DEVICE__ bool isunordered(double __x, double __y) {
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return __builtin_isunordered(__x, __y);
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}
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__DEVICE__ float ldexp(float __arg, int __exp) {
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return ::ldexpf(__arg, __exp);
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}
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__DEVICE__ float log(float __x) { return ::logf(__x); }
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__DEVICE__ float log10(float __x) { return ::log10f(__x); }
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__DEVICE__ float modf(float __x, float *__iptr) { return ::modff(__x, __iptr); }
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__DEVICE__ float pow(float __base, float __exp) {
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return ::powf(__base, __exp);
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}
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__DEVICE__ float pow(float __base, int __iexp) {
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return ::powif(__base, __iexp);
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}
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__DEVICE__ double pow(double __base, int __iexp) {
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return ::powi(__base, __iexp);
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}
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__DEVICE__ bool signbit(float __x) { return ::__signbitf(__x); }
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__DEVICE__ bool signbit(double __x) { return ::__signbitd(__x); }
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__DEVICE__ float sin(float __x) { return ::sinf(__x); }
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__DEVICE__ float sinh(float __x) { return ::sinhf(__x); }
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__DEVICE__ float sqrt(float __x) { return ::sqrtf(__x); }
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__DEVICE__ float tan(float __x) { return ::tanf(__x); }
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__DEVICE__ float tanh(float __x) { return ::tanhf(__x); }
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// There was a redefinition error for this this overload in CUDA mode.
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// We restrict it to OpenMP mode for now, that is where it is actually needed
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// anyway.
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#ifdef __OPENMP_NVPTX__
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__DEVICE__ float remquo(float __n, float __d, int *__q) {
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return ::remquof(__n, __d, __q);
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}
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#endif
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// Notably missing above is nexttoward. We omit it because
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// libdevice doesn't provide an implementation, and we don't want to be in the
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// business of implementing tricky libm functions in this header.
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#ifndef __OPENMP_NVPTX__
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// Now we've defined everything we promised we'd define in
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// __clang_cuda_math_forward_declares.h. We need to do two additional things to
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// fix up our math functions.
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//
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// 1) Define __device__ overloads for e.g. sin(int). The CUDA headers define
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// only sin(float) and sin(double), which means that e.g. sin(0) is
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// ambiguous.
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//
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// 2) Pull the __device__ overloads of "foobarf" math functions into namespace
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// std. These are defined in the CUDA headers in the global namespace,
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// independent of everything else we've done here.
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// We can't use std::enable_if, because we want to be pre-C++11 compatible. But
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// we go ahead and unconditionally define functions that are only available when
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// compiling for C++11 to match the behavior of the CUDA headers.
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template<bool __B, class __T = void>
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struct __clang_cuda_enable_if {};
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template <class __T> struct __clang_cuda_enable_if<true, __T> {
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typedef __T type;
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};
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// Defines an overload of __fn that accepts one integral argument, calls
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// __fn((double)x), and returns __retty.
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#define __CUDA_CLANG_FN_INTEGER_OVERLOAD_1(__retty, __fn) \
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template <typename __T> \
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__DEVICE__ \
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typename __clang_cuda_enable_if<std::numeric_limits<__T>::is_integer, \
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__retty>::type \
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__fn(__T __x) { \
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return ::__fn((double)__x); \
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}
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// Defines an overload of __fn that accepts one two arithmetic arguments, calls
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// __fn((double)x, (double)y), and returns a double.
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//
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// Note this is different from OVERLOAD_1, which generates an overload that
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// accepts only *integral* arguments.
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#define __CUDA_CLANG_FN_INTEGER_OVERLOAD_2(__retty, __fn) \
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template <typename __T1, typename __T2> \
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__DEVICE__ typename __clang_cuda_enable_if< \
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std::numeric_limits<__T1>::is_specialized && \
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std::numeric_limits<__T2>::is_specialized, \
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__retty>::type \
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__fn(__T1 __x, __T2 __y) { \
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return __fn((double)__x, (double)__y); \
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}
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, acos)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, acosh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, asin)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, asinh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, atan)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, atan2);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, atanh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, cbrt)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, ceil)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, copysign);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, cos)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, cosh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, erf)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, erfc)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, exp)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, exp2)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, expm1)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, fabs)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, fdim);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, floor)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, fmax);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, fmin);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, fmod);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(int, fpclassify)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, hypot);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(int, ilogb)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(bool, isfinite)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, isgreater);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, isgreaterequal);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(bool, isinf);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, isless);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, islessequal);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, islessgreater);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(bool, isnan);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(bool, isnormal)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(bool, isunordered);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, lgamma)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, log)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, log10)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, log1p)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, log2)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, logb)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(long long, llrint)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(long long, llround)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(long, lrint)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(long, lround)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, nearbyint);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, nextafter);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, pow);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_2(double, remainder);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, rint);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, round);
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(bool, signbit)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, sin)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, sinh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, sqrt)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, tan)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, tanh)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, tgamma)
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__CUDA_CLANG_FN_INTEGER_OVERLOAD_1(double, trunc);
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#undef __CUDA_CLANG_FN_INTEGER_OVERLOAD_1
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#undef __CUDA_CLANG_FN_INTEGER_OVERLOAD_2
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// Overloads for functions that don't match the patterns expected by
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// __CUDA_CLANG_FN_INTEGER_OVERLOAD_{1,2}.
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template <typename __T1, typename __T2, typename __T3>
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__DEVICE__ typename __clang_cuda_enable_if<
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std::numeric_limits<__T1>::is_specialized &&
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std::numeric_limits<__T2>::is_specialized &&
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std::numeric_limits<__T3>::is_specialized,
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double>::type
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fma(__T1 __x, __T2 __y, __T3 __z) {
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return std::fma((double)__x, (double)__y, (double)__z);
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}
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template <typename __T>
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__DEVICE__ typename __clang_cuda_enable_if<std::numeric_limits<__T>::is_integer,
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double>::type
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frexp(__T __x, int *__exp) {
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return std::frexp((double)__x, __exp);
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}
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template <typename __T>
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__DEVICE__ typename __clang_cuda_enable_if<std::numeric_limits<__T>::is_integer,
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double>::type
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ldexp(__T __x, int __exp) {
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return std::ldexp((double)__x, __exp);
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}
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template <typename __T1, typename __T2>
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__DEVICE__ typename __clang_cuda_enable_if<
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std::numeric_limits<__T1>::is_specialized &&
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std::numeric_limits<__T2>::is_specialized,
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double>::type
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remquo(__T1 __x, __T2 __y, int *__quo) {
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return std::remquo((double)__x, (double)__y, __quo);
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}
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template <typename __T>
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__DEVICE__ typename __clang_cuda_enable_if<std::numeric_limits<__T>::is_integer,
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double>::type
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scalbln(__T __x, long __exp) {
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return std::scalbln((double)__x, __exp);
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}
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template <typename __T>
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__DEVICE__ typename __clang_cuda_enable_if<std::numeric_limits<__T>::is_integer,
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double>::type
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scalbn(__T __x, int __exp) {
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return std::scalbn((double)__x, __exp);
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}
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// We need to define these overloads in exactly the namespace our standard
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// library uses (including the right inline namespace), otherwise they won't be
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// picked up by other functions in the standard library (e.g. functions in
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// <complex>). Thus the ugliness below.
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#ifdef _LIBCPP_BEGIN_NAMESPACE_STD
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_LIBCPP_BEGIN_NAMESPACE_STD
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#else
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namespace std {
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#ifdef _GLIBCXX_BEGIN_NAMESPACE_VERSION
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_GLIBCXX_BEGIN_NAMESPACE_VERSION
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#endif
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#endif
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// Pull the new overloads we defined above into namespace std.
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using ::acos;
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using ::acosh;
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using ::asin;
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using ::asinh;
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using ::atan;
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using ::atan2;
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using ::atanh;
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using ::cbrt;
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using ::ceil;
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using ::copysign;
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using ::cos;
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using ::cosh;
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using ::erf;
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using ::erfc;
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using ::exp;
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using ::exp2;
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using ::expm1;
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using ::fabs;
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using ::fdim;
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using ::floor;
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using ::fma;
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using ::fmax;
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using ::fmin;
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using ::fmod;
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using ::fpclassify;
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using ::frexp;
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using ::hypot;
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using ::ilogb;
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using ::isfinite;
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using ::isgreater;
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using ::isgreaterequal;
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using ::isless;
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using ::islessequal;
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using ::islessgreater;
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using ::isnormal;
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using ::isunordered;
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using ::ldexp;
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using ::lgamma;
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using ::llrint;
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using ::llround;
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using ::log;
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using ::log10;
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using ::log1p;
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using ::log2;
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using ::logb;
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using ::lrint;
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using ::lround;
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using ::nearbyint;
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using ::nextafter;
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using ::pow;
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using ::remainder;
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using ::remquo;
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using ::rint;
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using ::round;
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using ::scalbln;
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using ::scalbn;
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using ::signbit;
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using ::sin;
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using ::sinh;
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using ::sqrt;
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using ::tan;
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using ::tanh;
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using ::tgamma;
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using ::trunc;
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// Well this is fun: We need to pull these symbols in for libc++, but we can't
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// pull them in with libstdc++, because its ::isinf and ::isnan are different
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// than its std::isinf and std::isnan.
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#ifndef __GLIBCXX__
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using ::isinf;
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using ::isnan;
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#endif
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// Finally, pull the "foobarf" functions that CUDA defines in its headers into
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// namespace std.
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using ::acosf;
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using ::acoshf;
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using ::asinf;
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using ::asinhf;
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using ::atan2f;
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using ::atanf;
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using ::atanhf;
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using ::cbrtf;
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using ::ceilf;
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using ::copysignf;
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using ::cosf;
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using ::coshf;
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using ::erfcf;
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using ::erff;
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using ::exp2f;
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using ::expf;
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using ::expm1f;
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using ::fabsf;
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using ::fdimf;
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using ::floorf;
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using ::fmaf;
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using ::fmaxf;
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using ::fminf;
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using ::fmodf;
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using ::frexpf;
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using ::hypotf;
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using ::ilogbf;
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using ::ldexpf;
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using ::lgammaf;
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using ::llrintf;
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using ::llroundf;
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using ::log10f;
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using ::log1pf;
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using ::log2f;
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using ::logbf;
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using ::logf;
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using ::lrintf;
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using ::lroundf;
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using ::modff;
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using ::nearbyintf;
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using ::nextafterf;
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using ::powf;
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using ::remainderf;
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using ::remquof;
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using ::rintf;
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using ::roundf;
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using ::scalblnf;
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using ::scalbnf;
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using ::sinf;
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using ::sinhf;
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using ::sqrtf;
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using ::tanf;
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using ::tanhf;
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using ::tgammaf;
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using ::truncf;
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#ifdef _LIBCPP_END_NAMESPACE_STD
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_LIBCPP_END_NAMESPACE_STD
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#else
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#ifdef _GLIBCXX_BEGIN_NAMESPACE_VERSION
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_GLIBCXX_END_NAMESPACE_VERSION
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#endif
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} // namespace std
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#endif
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#endif // __OPENMP_NVPTX__
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#undef __DEVICE__
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#endif
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