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코드 이슈 풀 리퀘스트 액션 패키지 프로젝트 릴리즈 위키 활동

EXSWHTEC-285 - Implement tests for exponential and power device math functions #229

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Change-Id: I34ad7ee92960500bcd14dfd7d230ca8f8f77c172


[ROCm/hip-tests commit: 87d601411b]
이 커밋은 다음에 포함됨:
Nives Vukovic
2024-01-22 23:41:01 +05:30
커밋한 사람 Rakesh Roy
부모 1ca4fdc6f7
커밋 8f9f3e61d2
6개의 변경된 파일과 844개의 추가작업 그리고 1개의 파일을 삭제
패치 파일 다운로드 Diff 파일 다운로드 모든 파일 펼침 모든 파일 접기
projects/hip-tests/catch/unit/math/CMakeLists.txt
+5
파일 보기
@@ -26,6 +26,7 @@ set(TEST_SRC
double_precision_intrinsics.cc
integer_intrinsics.cc
root_funcs.cc
pow_funcs.cc
)
if(HIP_PLATFORM MATCHES "nvidia")
@@ -86,3 +87,7 @@ add_test(NAME Unit_Device_root_3Dand4D_Negative
COMMAND python3 ${CMAKE_CURRENT_SOURCE_DIR}/../compileAndCaptureOutput.py
${CMAKE_CURRENT_SOURCE_DIR} ${HIP_PLATFORM} ${HIP_PATH}
math_root_negative_kernels_3Dand4D.cc 56)
add_test(NAME Unit_Device_pow_Negative
COMMAND python3 ${CMAKE_CURRENT_SOURCE_DIR}/../compileAndCaptureOutput.py
${CMAKE_CURRENT_SOURCE_DIR} ${HIP_PLATFORM} ${HIP_PATH}
math_pow_negative_kernels.cc 76)
projects/hip-tests/catch/unit/math/math_pow_negative_kernels.cc
+92
파일 보기
@@ -0,0 +1,92 @@
/*
Copyright (c) 2021 Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#include <hip_test_common.hh>
class Dummy {
public:
__device__ Dummy() {}
__device__ ~Dummy() {}
};
#define NEGATIVE_KERNELS_SHELL_EXP(func_name) \
__global__ void func_name##_kernel_v1(double* x) { double result = func_name(x); } \
__global__ void func_name##_kernel_v2(Dummy x) { double result = func_name(x); } \
__global__ void func_name##f_kernel_v1(float* x) { float result = func_name##f(x); } \
__global__ void func_name##f_kernel_v2(Dummy x) { float result = func_name##f(x); }
#define NEGATIVE_KERNELS_SHELL_INT_2ND(func_name) \
__global__ void func_name##_kernel_v1(double* x, int e) { double result = func_name(x, e); } \
__global__ void func_name##_kernel_v2(Dummy x, int e) { double result = func_name(x, e); } \
__global__ void func_name##_kernel_v3(double x, int* e) { double result = func_name(x, e); } \
__global__ void func_name##_kernel_v4(double x, Dummy e) { double result = func_name(x, e); } \
__global__ void func_name##f_kernel_v1(float* x, int e) { float result = func_name##f(x, e); } \
__global__ void func_name##f_kernel_v2(Dummy x, int e) { float result = func_name##f(x, e); } \
__global__ void func_name##f_kernel_v3(float x, int* e) { float result = func_name##f(x, e); } \
__global__ void func_name##f_kernel_v4(float x, Dummy e) { float result = func_name##f(x, e); }
NEGATIVE_KERNELS_SHELL_EXP(exp)
NEGATIVE_KERNELS_SHELL_EXP(exp2)
NEGATIVE_KERNELS_SHELL_EXP(exp10)
NEGATIVE_KERNELS_SHELL_EXP(expm1)
__global__ void frexp_kernel_v1(double* x, int* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v2(Dummy x, int* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v3(double x, char* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v4(double x, short* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v5(double x, long* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v6(double x, long long* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v7(double x, float* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v8(double x, double* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v9(double x, Dummy* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v10(double x, const int* nptr) { double result = frexp(x, nptr); }
__global__ void frexpf_kernel_v1(float* x, int* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v2(Dummy x, int* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v3(float x, char* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v4(float x, short* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v5(float x, long* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v6(float x, long long* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v7(float x, float* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v8(float x, double* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v9(float x, Dummy* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v10(float x, const int* nptr) { float result = frexpf(x, nptr); }
NEGATIVE_KERNELS_SHELL_INT_2ND(ldexp)
__global__ void pow_kernel_v1(double* x, double e) { double result = pow(x, e); }
__global__ void pow_kernel_v2(Dummy x, double e) { double result = pow(x, e); }
__global__ void pow_kernel_v3(double x, double* e) { double result = pow(x, e); }
__global__ void pow_kernel_v4(double x, Dummy e) { double result = pow(x, e); }
__global__ void powf_kernel_v1(float* x, float e) { float result = powf(x, e); }
__global__ void powf_kernel_v2(Dummy x, float e) { float result = powf(x, e); }
__global__ void powf_kernel_v3(float x, float* e) { float result = powf(x, e); }
__global__ void powf_kernel_v4(float x, Dummy e) { float result = powf(x, e); }
NEGATIVE_KERNELS_SHELL_INT_2ND(powi)
NEGATIVE_KERNELS_SHELL_INT_2ND(scalbn)
__global__ void scalbln_kernel_v1(double* x, long int n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v2(Dummy x, long int n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v3(double x, long int* n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v4(double x, Dummy n) { double result = scalbln(x, n); }
__global__ void scalblnf_kernel_v1(float* x, long int n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v2(Dummy x, long int n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v3(float x, long int* n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v4(float x, Dummy n) { float result = scalblnf(x, n); }
projects/hip-tests/catch/unit/math/math_pow_negative_kernels_rtc.hh
+150
파일 보기
@@ -0,0 +1,150 @@
/*
Copyright (c) 2021 Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#pragma once
/*
Negative kernels used for the math pow negative Test Cases that are using RTC.
*/
static constexpr auto kExp{R"(
class Dummy {
public:
__device__ Dummy() {}
__device__ ~Dummy() {}
};
__global__ void exp_kernel_v1(double* x) { double result = exp(x); }
__global__ void exp_kernel_v2(Dummy x) { double result = exp(x); }
__global__ void expf_kernel_v1(float* x) { float result = expf(x); }
__global__ void expf_kernel_v2(Dummy x) { float result = expf(x); }
)"};
static constexpr auto kExp2{R"(
class Dummy {
public:
__device__ Dummy() {}
__device__ ~Dummy() {}
};
__global__ void exp2_kernel_v1(double* x) { double result = exp2(x); }
__global__ void exp2_kernel_v2(Dummy x) { double result = exp2(x); }
__global__ void exp2f_kernel_v1(float* x) { float result = exp2f(x); }
__global__ void exp2f_kernel_v2(Dummy x) { float result = exp2f(x); }
)"};
static constexpr auto kExp10{R"(
class Dummy {
public:
__device__ Dummy() {}
__device__ ~Dummy() {}
};
__global__ void exp10_kernel_v1(double* x) { double result = exp10(x); }
__global__ void exp10_kernel_v2(Dummy x) { double result = exp10(x); }
__global__ void exp10f_kernel_v1(float* x) { float result = exp10f(x); }
__global__ void exp10f_kernel_v2(Dummy x) { float result = exp10f(x); }
)"};
static constexpr auto kExpm1{R"(
class Dummy {
public:
__device__ Dummy() {}
__device__ ~Dummy() {}
};
__global__ void expm1_kernel_v1(double* x) { double result = expm1(x); }
__global__ void expm1_kernel_v2(Dummy x) { double result = expm1(x); }
__global__ void expm1f_kernel_v1(float* x) { float result = expm1f(x); }
__global__ void expm1f_kernel_v2(Dummy x) { float result = expm1f(x); }
)"};
static constexpr auto kFrexp{R"(
__global__ void frexp_kernel_v1(double* x, int* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v2(Dummy x, int* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v3(double x, char* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v4(double x, short* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v5(double x, long* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v6(double x, long long* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v7(double x, float* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v8(double x, double* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v9(double x, Dummy* nptr) { double result = frexp(x, nptr); }
__global__ void frexp_kernel_v10(double x, const int* nptr) { double result = frexp(x, nptr); }
__global__ void frexpf_kernel_v1(float* x, int* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v2(Dummy x, int* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v3(float x, char* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v4(float x, short* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v5(float x, long* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v6(float x, long long* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v7(float x, float* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v8(float x, double* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v9(float x, Dummy* nptr) { float result = frexpf(x, nptr); }
__global__ void frexpf_kernel_v10(float x, const int* nptr) { float result = frexpf(x, nptr); }
)"};
static constexpr auto kLdexp{R"(
__global__ void ldexp_kernel_v1(double* x, int e) { double result = ldexp(x, e); }
__global__ void ldexp_kernel_v2(Dummy x, int e) { double result = ldexp(x, e); }
__global__ void ldexp_kernel_v3(double x, int* e) { double result = ldexp(x, e); }
__global__ void ldexp_kernel_v4(double x, Dummy e) { double result = ldexp(x, e); }
__global__ void ldexpf_kernel_v1(float* x, int e) { float result = ldexpf(x, e); }
__global__ void ldexpf_kernel_v2(Dummy x, int e) { float result = ldexpf(x, e); }
__global__ void ldexpf_kernel_v3(float x, int* e) { float result = ldexpf(x, e); }
__global__ void ldexpf_kernel_v4(float x, Dummy e) { float result = ldexpf(x, e); }
)"};
static constexpr auto kPow{R"(
__global__ void pow_kernel_v1(double* x, double e) { double result = pow(x, e); }
__global__ void pow_kernel_v2(Dummy x, double e) { double result = pow(x, e); }
__global__ void pow_kernel_v3(double x, double* e) { double result = pow(x, e); }
__global__ void pow_kernel_v4(double x, Dummy e) { double result = pow(x, e); }
__global__ void powf_kernel_v1(float* x, float e) { float result = powf(x, e); }
__global__ void powf_kernel_v2(Dummy x, float e) { float result = powf(x, e); }
__global__ void powf_kernel_v3(float x, float* e) { float result = powf(x, e); }
__global__ void powf_kernel_v4(float x, Dummy e) { float result = powf(x, e); }
)"};
static constexpr auto kPowi{R"(
__global__ void powi_kernel_v1(double* x, int e) { double result = powi(x, e); }
__global__ void powi_kernel_v2(Dummy x, int e) { double result = powi(x, e); }
__global__ void powi_kernel_v3(double x, int* e) { double result = powi(x, e); }
__global__ void powi_kernel_v4(double x, Dummy e) { double result = powi(x, e); }
__global__ void powif_kernel_v1(float* x, int e) { float result = powif(x, e); }
__global__ void powif_kernel_v2(Dummy x, int e) { float result = powif(x, e); }
__global__ void powif_kernel_v3(float x, int* e) { float result = powif(x, e); }
__global__ void powif_kernel_v4(float x, Dummy e) { float result = powif(x, e); }
)"};
static constexpr auto kScalbn{R"(
__global__ void scalbn_kernel_v1(double* x, int e) { double result = scalbn(x, e); }
__global__ void scalbn_kernel_v2(Dummy x, int e) { double result = scalbn(x, e); }
__global__ void scalbn_kernel_v3(double x, int* e) { double result = scalbn(x, e); }
__global__ void scalbn_kernel_v4(double x, Dummy e) { double result = scalbn(x, e); }
__global__ void scalbnf_kernel_v1(float* x, int e) { float result = scalbnf(x, e); }
__global__ void scalbnf_kernel_v2(Dummy x, int e) { float result = scalbnf(x, e); }
__global__ void scalbnf_kernel_v3(float x, int* e) { float result = scalbnf(x, e); }
__global__ void scalbnf_kernel_v4(float x, Dummy e) { float result = scalbnf(x, e); }
)"};
static constexpr auto kScalbln{R"(
__global__ void scalbln_kernel_v1(double* x, long int n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v2(Dummy x, long int n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v3(double x, long int* n) { double result = scalbln(x, n); }
__global__ void scalbln_kernel_v4(double x, Dummy n) { double result = scalbln(x, n); }
__global__ void scalblnf_kernel_v1(float* x, long int n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v2(Dummy x, long int n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v3(float x, long int* n) { float result = scalblnf(x, n); }
__global__ void scalblnf_kernel_v4(float x, Dummy n) { float result = scalblnf(x, n); }
)"};
projects/hip-tests/catch/unit/math/math_special_values.hh
+8 -1
파일 보기
@@ -277,6 +277,12 @@ inline constexpr std::array kSpecialValuesFloat{
+0.0f,
};
inline constexpr std::array kSpecialValuesInt{
0, 1, 2, 3, 126, 127, 128, 1022, 1023, 1024, 0x02000001, 0x04000001, 1465264071, 1488522147,
std::numeric_limits<int>::max(), -1, -2, -3, -126, -127, -128, -1022, -1023, -11024, -0x02000001,
-0x04000001, -1465264071, -1488522147, std::numeric_limits<int>::min(), -std::numeric_limits<int>::max()
};
template <typename T> struct SpecialVals {
const T* const data;
const size_t size;
@@ -284,4 +290,5 @@ template <typename T> struct SpecialVals {
inline constexpr auto kSpecialValRegistry =
std::make_tuple(SpecialVals<float>{kSpecialValuesFloat.data(), kSpecialValuesFloat.size()},
SpecialVals<double>{kSpecialValuesDouble.data(), kSpecialValuesDouble.size()});
SpecialVals<double>{kSpecialValuesDouble.data(), kSpecialValuesDouble.size()},
SpecialVals<int>{kSpecialValuesInt.data(), kSpecialValuesInt.size()});
projects/hip-tests/catch/unit/math/pow_common.hh
+134
파일 보기
@@ -0,0 +1,134 @@
/*
Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#pragma once
#include "math_common.hh"
#include "math_special_values.hh"
#include <hip/hip_cooperative_groups.h>
namespace cg = cooperative_groups;
#define MATH_POW_INT_KERNEL_DEF(func_name) \
template <typename T1, typename T2> \
__global__ void func_name##_kernel(T1* const ys, const size_t num_xs, T1* const x1s, \
T2* const x2s) { \
const auto tid = cg::this_grid().thread_rank(); \
const auto stride = cg::this_grid().size(); \
\
for (auto i = tid; i < num_xs; i += stride) { \
if constexpr (std::is_same_v<float, T1>) { \
ys[i] = func_name##f(x1s[i], x2s[i]); \
} else if constexpr (std::is_same_v<double, T1>) { \
ys[i] = func_name(x1s[i], x2s[i]); \
} \
} \
}
template <typename T1, typename T2>
using kernel_pow_int_sig = void (*)(T1*, const size_t, T1*, T2*);
template <typename T1, typename T2> using ref_pow_int_sig = T1 (*)(T1, T2);
template <typename T1, typename T2, typename RT1, typename RT2, typename ValidatorBuilder>
void PowIntFloatingPointBruteForceTest(kernel_pow_int_sig<T1, T2> kernel,
ref_pow_int_sig<RT1, RT2> ref_func,
const ValidatorBuilder& validator_builder) {
const auto [grid_size, block_size] = GetOccupancyMaxPotentialBlockSize(kernel);
const uint64_t num_iterations = GetTestIterationCount();
const auto max_batch_size =
std::min(GetMaxAllowedDeviceMemoryUsage() / (sizeof(T1) * 2 + sizeof(T2)), num_iterations);
LinearAllocGuard<T1> x1s{LinearAllocs::hipHostMalloc, max_batch_size * sizeof(T1)};
LinearAllocGuard<T2> x2s{LinearAllocs::hipHostMalloc, max_batch_size * sizeof(T2)};
MathTest math_test(kernel, max_batch_size);
auto batch_size = max_batch_size;
const auto num_threads = thread_pool.thread_count();
for (uint64_t i = 0ul; i < num_iterations; i += batch_size) {
batch_size = std::min<uint64_t>(max_batch_size, num_iterations - i);
const auto min_sub_batch_size = batch_size / num_threads;
const auto tail = batch_size % num_threads;
auto base_idx = 0u;
for (auto i = 0u; i < num_threads; ++i) {
const auto sub_batch_size = min_sub_batch_size + (i < tail);
thread_pool.Post([=, &x1s, &x2s] {
const auto generator1 = [=] {
static thread_local std::mt19937 rng(std::random_device{}());
std::uniform_real_distribution<RefType_t<T1>> unif_dist(std::numeric_limits<T1>::lowest(),
std::numeric_limits<T1>::max());
return static_cast<T1>(unif_dist(rng));
};
const auto generator2 = [] {
static thread_local std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<T2> unif_dist(std::numeric_limits<T2>::lowest(),
std::numeric_limits<T2>::max());
return unif_dist(rng);
};
std::generate(x1s.ptr() + base_idx, x1s.ptr() + base_idx + sub_batch_size, generator1);
std::generate(x2s.ptr() + base_idx, x2s.ptr() + base_idx + sub_batch_size, generator2);
});
base_idx += sub_batch_size;
}
thread_pool.Wait();
math_test.Run(validator_builder, grid_size, block_size, ref_func, batch_size, x1s.ptr(),
x2s.ptr());
}
}
template <typename T1, typename T2, typename RT1, typename RT2, typename ValidatorBuilder>
void PowIntFloatingPointSpecialValuesTest(kernel_pow_int_sig<T1, T2> kernel,
ref_pow_int_sig<RT1, RT2> ref_func,
const ValidatorBuilder& validator_builder) {
const auto [grid_size, block_size] = GetOccupancyMaxPotentialBlockSize(kernel);
const auto values1 = std::get<SpecialVals<T1>>(kSpecialValRegistry);
const auto values2 = std::get<SpecialVals<int>>(kSpecialValRegistry);
const auto size = values1.size * values2.size;
LinearAllocGuard<T1> x1s{LinearAllocs::hipHostMalloc, size * sizeof(T1)};
LinearAllocGuard<T2> x2s{LinearAllocs::hipHostMalloc, size * sizeof(T2)};
for (auto i = 0u; i < values1.size; ++i) {
for (auto j = 0u; j < values2.size; ++j) {
x1s.ptr()[i * values2.size + j] = values1.data[i];
x2s.ptr()[i * values2.size + j] = static_cast<T2>(values2.data[j]);
}
}
MathTest math_test(kernel, size);
math_test.template Run<false>(validator_builder, grid_size, block_size, ref_func, size, x1s.ptr(),
x2s.ptr());
}
template <typename T1, typename T2, typename RT1, typename RT2, typename ValidatorBuilder>
void PowIntFloatingPointTest(kernel_pow_int_sig<T1, T2> kernel, ref_pow_int_sig<RT1, RT2> ref_func,
const ValidatorBuilder& validator_builder) {
SECTION("Special values") {
PowIntFloatingPointSpecialValuesTest(kernel, ref_func, validator_builder);
}
SECTION("Brute force") { PowIntFloatingPointBruteForceTest(kernel, ref_func, validator_builder); }
}
projects/hip-tests/catch/unit/math/pow_funcs.cc
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/*
Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#include "unary_common.hh"
#include "binary_common.hh"
#include "pow_common.hh"
#include "math_pow_negative_kernels_rtc.hh"
/**
* @addtogroup PowMathFuncs PowMathFuncs
* @{
* @ingroup MathTest
*/
/********** Unary Functions **********/
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `expf(x)` for all possible inputs and `exp(x)` against a
* table of difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::exp(T)`. The maximum ulp error for single
* precision is 2 and for double precision is 1.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
MATH_UNARY_WITHIN_ULP_STL_REF_TEST_DEF(exp, 2, 1)
/**
* Test Description
* ------------------------
* - RTCs kernels that pass argument of invalid type for expf and exp.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_exp_expf_Negative_RTC") { NegativeTestRTCWrapper<4>(kExp); }
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `exp2f(x)` for all possible inputs and `exp2(x)` against a
* table of difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::exp2(T)`. The maximum ulp error for single
* precision is 2 and for double precision is 1.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
MATH_UNARY_WITHIN_ULP_STL_REF_TEST_DEF(exp2, 2, 1)
/**
* Test Description
* ------------------------
* - RTCs kernels that pass argument of invalid type for exp2f and exp2.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_exp2_exp2f_Negative_RTC") { NegativeTestRTCWrapper<4>(kExp2); }
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `expm1f(x)` for all possible inputs and `expm1(x)` against a
* table of difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::exp(T)`. The maximum ulp error is 1.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
MATH_UNARY_WITHIN_ULP_STL_REF_TEST_DEF(expm1, 1, 1)
/**
* Test Description
* ------------------------
* - RTCs kernels that pass argument of invalid type for expm1f and expm1.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_expm1_expm1f_Negative_RTC") { NegativeTestRTCWrapper<4>(kExpm1); }
MATH_UNARY_KERNEL_DEF(exp10)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `exp10f(x)` for all possible inputs. The maximum ulp error
* is 2.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_exp10f_Accuracy_Positive") {
auto exp10_ref = [](double arg) -> double { return std::pow(10, arg); };
double (*ref)(double) = exp10_ref;
UnarySinglePrecisionTest(exp10_kernel<float>, ref, ULPValidatorBuilderFactory<float>(2));
}
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `exp10(x)` against a table of difficult values,
* followed by a large number of randomly generated values. The maximum ulp error is 1.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_exp10_Accuracy_Positive") {
auto exp10_ref = [](long double arg) -> long double { return std::pow(10, arg); };
long double (*ref)(long double) = exp10_ref;
UnaryDoublePrecisionTest(exp10_kernel<double>, ref, ULPValidatorBuilderFactory<double>(1));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass argument of invalid type for exp10f and exp10.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_exp10_exp10f_Negative_RTC") { NegativeTestRTCWrapper<4>(kExp10); }
template <typename T>
__global__ void frexp_kernel(std::pair<T, int>* const ys, const size_t num_xs, T* const xs) {
const auto tid = cg::this_grid().thread_rank();
const auto stride = cg::this_grid().size();
for (auto i = tid; i < num_xs; i += stride) {
if constexpr (std::is_same_v<float, T>) {
ys[i].first = frexpf(xs[i], &ys[i].second);
} else if constexpr (std::is_same_v<double, T>) {
ys[i].first = frexp(xs[i], &ys[i].second);
}
}
}
template <typename T> std::pair<T, int> frexp_ref(T arg) {
int exp_v;
T res = std::frexp(arg, &exp_v);
return {res, exp_v};
}
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `frexpf(x, exp)` for all possible inputs. The results are
* compared against reference function `double std::frexp(double, int*)`. The maximum ulp error is
* 0.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_frexpf_Accuracy_Positive") {
UnarySinglePrecisionTest(
frexp_kernel<float>, frexp_ref<double>,
PairValidatorBuilderFactory<float, int>(ULPValidatorBuilderFactory<float>(0),
EqValidatorBuilderFactory<int>()));
}
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `frexp(x, exp)` against a table of difficult values,
* followed by a large number of randomly generated values. The results are
* compared against reference function `long double std::frexp(long double, int*)`. The maximum ulp
* error is 0.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_frexp_Accuracy_Positive") {
UnaryDoublePrecisionTest(
frexp_kernel<double>, frexp_ref<long double>,
PairValidatorBuilderFactory<double, int>(ULPValidatorBuilderFactory<double>(0),
EqValidatorBuilderFactory<int>()));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass argument of invalid type for frexpf and frexp.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_frexp_frexpf_Negative_RTC") { NegativeTestRTCWrapper<20>(kFrexp); }
/********** Binary Functions **********/
MATH_BINARY_KERNEL_DEF(pow)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `powf(x, y)` and `pow(x, y)`against a table of
* difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::pow(T, T)`. The maximum ulp error
* for single precision is 4 and for double precision is 2.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEMPLATE_TEST_CASE("Unit_Device_pow_Accuracy_Positive", "", float, double) {
using RT = RefType_t<TestType>;
auto pow_ref = [](RT arg1, RT arg2) -> RT {
if (std::isinf(arg1) && arg2 < 0) return 0;
return std::pow(arg1, arg2);
};
RT (*ref)(RT, RT) = pow_ref;
const auto ulp = std::is_same_v<float, TestType> ? 4 : 2;
BinaryFloatingPointTest(pow_kernel<TestType>, ref, ULPValidatorBuilderFactory<TestType>(ulp));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass combinations of arguments of invalid types for powf and pow.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_pow_powf_Negative_RTC") { NegativeTestRTCWrapper<8>(kPow); }
MATH_POW_INT_KERNEL_DEF(ldexp)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `ldexpf(x, exp)` and `ldexp(x, exp)`against a table of
* difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::ldexp(T, int)`. The maximum ulp error is 0.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEMPLATE_TEST_CASE("Unit_Device_ldexp_Accuracy_Positive", "", float, double) {
using RT = RefType_t<TestType>;
RT (*ref)(RT, int) = std::ldexp;
PowIntFloatingPointTest(ldexp_kernel<TestType, int>, ref,
ULPValidatorBuilderFactory<TestType>(0));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass combinations of arguments of invalid types for ldexpf and ldexp.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_ldexp_ldexpf_Negative_RTC") { NegativeTestRTCWrapper<8>(kLdexp); }
MATH_POW_INT_KERNEL_DEF(powi)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `powi(x, exp)` and `powi(x, exp)`against a table of
* difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::pow(T, T)`. The maximum ulp error
* for single precision is 4 and for double precision is 2.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEMPLATE_TEST_CASE("Unit_Device_powi_Accuracy_Positive", "", float, double) {
using RT = RefType_t<TestType>;
auto pow_ref = [](RT arg1, int arg2) -> RT {
if (std::isinf(arg1) && arg2 < 0) return 0;
return std::pow(arg1, static_cast<RT>(arg2));
};
RT (*ref)(RT, int) = pow_ref;
const auto ulp = std::is_same_v<float, TestType> ? 4 : 2;
PowIntFloatingPointTest(powi_kernel<TestType, int>, ref,
ULPValidatorBuilderFactory<TestType>(ulp));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass combinations of arguments of invalid types for powif and powi.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_powi_powif_Negative_RTC") { NegativeTestRTCWrapper<8>(kPowi); }
MATH_POW_INT_KERNEL_DEF(scalbn)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `scalbnf(x, n)` and `scalbn(x, n)`against a table of
* difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::scalbn(T, int)`. The maximum ulp error is 0.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEMPLATE_TEST_CASE("Unit_Device_scalbn_Accuracy_Positive", "", float, double) {
using RT = RefType_t<TestType>;
RT (*ref)(RT, int) = std::scalbn;
PowIntFloatingPointTest(scalbn_kernel<TestType, int>, ref,
ULPValidatorBuilderFactory<TestType>(0));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass combinations of arguments of invalid types for scalbnf and scalbn.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_scalbn_scalbnf_Negative_RTC") { NegativeTestRTCWrapper<8>(kScalbn); }
MATH_POW_INT_KERNEL_DEF(scalbln)
/**
* Test Description
* ------------------------
* - Tests the numerical accuracy of `scalblnf(x, l)` and `scalbln(x, l)`against a table of
* difficult values, followed by a large number of randomly generated values. The results
* are compared against reference function `T std::scalbn(T, long int)`. The maximum ulp error is 0.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEMPLATE_TEST_CASE("Unit_Device_scalbln_Accuracy_Positive", "", float, double) {
using RT = RefType_t<TestType>;
RT (*ref)(RT, long int) = std::scalbln;
PowIntFloatingPointTest(scalbln_kernel<TestType, long int>, ref,
ULPValidatorBuilderFactory<TestType>(0));
}
/**
* Test Description
* ------------------------
* - RTCs kernels that pass combinations of arguments of invalid types for scalblnf and scalbln.
*
* Test source
* ------------------------
* - unit/math/pow_funcs.cc
* Test requirements
* ------------------------
* - HIP_VERSION >= 5.2
*/
TEST_CASE("Unit_Device_scalbln_scalblnf_Negative_RTC") { NegativeTestRTCWrapper<8>(kScalbln); }