Abseil Common Libraries (C++) (grcp 依赖) https://abseil.io/
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Export of internal Abseil changes -- f012012ef78234a6a4585321b67d7b7c92ebc266 by Laramie Leavitt <lar@google.com>: Slight restructuring of absl/random/internal randen implementation. Convert round-keys.inc into randen_round_keys.cc file. Consistently use a 128-bit pointer type for internal method parameters. This allows simpler pointer arithmetic in C++ & permits removal of some constants and casts. Remove some redundancy in comments & constexpr variables. Specifically, all references to Randen algorithm parameters use RandenTraits; duplication in RandenSlow removed. PiperOrigin-RevId: 312190313 -- dc8b42e054046741e9ed65335bfdface997c6063 by Abseil Team <absl-team@google.com>: Internal change. PiperOrigin-RevId: 312167304 -- f13d248fafaf206492c1362c3574031aea3abaf7 by Matthew Brown <matthewbr@google.com>: Cleanup StrFormat extensions a little. PiperOrigin-RevId: 312166336 -- 9d9117589667afe2332bb7ad42bc967ca7c54502 by Derek Mauro <dmauro@google.com>: Internal change PiperOrigin-RevId: 312105213 -- 9a12b9b3aa0e59b8ee6cf9408ed0029045543a9b by Abseil Team <absl-team@google.com>: Complete IGNORE_TYPE macro renaming. PiperOrigin-RevId: 311999699 -- 64756f20d61021d999bd0d4c15e9ad3857382f57 by Gennadiy Rozental <rogeeff@google.com>: Switch to fixed bytes specific default value. This fixes the Abseil Flags for big endian platforms. PiperOrigin-RevId: 311844448 -- bdbe6b5b29791dbc3816ada1828458b3010ff1e9 by Laramie Leavitt <lar@google.com>: Change many distribution tests to use pcg_engine as a deterministic source of entropy. It's reasonable to test that the BitGen itself has good entropy, however when testing the cross product of all random distributions x all the architecture variations x all submitted changes results in a large number of tests. In order to account for these failures while still using good entropy requires that our allowed sigma need to account for all of these independent tests. Our current sigma values are too restrictive, and we see a lot of failures, so we have to either relax the sigma values or convert some of the statistical tests to use deterministic values. This changelist does the latter. PiperOrigin-RevId: 311840096 GitOrigin-RevId: f012012ef78234a6a4585321b67d7b7c92ebc266 Change-Id: Ic84886f38ff30d7d72c126e9b63c9a61eb729a1a
5 years ago
// Copyright 2017 The Abseil Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Benchmarks for absl random distributions as well as a selection of the
// C++ standard library random distributions.
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <initializer_list>
#include <iterator>
#include <limits>
#include <random>
#include <type_traits>
#include <vector>
#include "absl/base/macros.h"
#include "absl/meta/type_traits.h"
#include "absl/random/bernoulli_distribution.h"
#include "absl/random/beta_distribution.h"
#include "absl/random/exponential_distribution.h"
#include "absl/random/gaussian_distribution.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/randen_engine.h"
#include "absl/random/log_uniform_int_distribution.h"
#include "absl/random/poisson_distribution.h"
#include "absl/random/random.h"
#include "absl/random/uniform_int_distribution.h"
#include "absl/random/uniform_real_distribution.h"
#include "absl/random/zipf_distribution.h"
#include "benchmark/benchmark.h"
namespace {
// Seed data to avoid reading random_device() for benchmarks.
uint32_t kSeedData[] = {
0x1B510052, 0x9A532915, 0xD60F573F, 0xBC9BC6E4, 0x2B60A476, 0x81E67400,
0x08BA6FB5, 0x571BE91F, 0xF296EC6B, 0x2A0DD915, 0xB6636521, 0xE7B9F9B6,
0xFF34052E, 0xC5855664, 0x53B02D5D, 0xA99F8FA1, 0x08BA4799, 0x6E85076A,
0x4B7A70E9, 0xB5B32944, 0xDB75092E, 0xC4192623, 0xAD6EA6B0, 0x49A7DF7D,
0x9CEE60B8, 0x8FEDB266, 0xECAA8C71, 0x699A18FF, 0x5664526C, 0xC2B19EE1,
0x193602A5, 0x75094C29, 0xA0591340, 0xE4183A3E, 0x3F54989A, 0x5B429D65,
0x6B8FE4D6, 0x99F73FD6, 0xA1D29C07, 0xEFE830F5, 0x4D2D38E6, 0xF0255DC1,
0x4CDD2086, 0x8470EB26, 0x6382E9C6, 0x021ECC5E, 0x09686B3F, 0x3EBAEFC9,
0x3C971814, 0x6B6A70A1, 0x687F3584, 0x52A0E286, 0x13198A2E, 0x03707344,
};
// PrecompiledSeedSeq provides kSeedData to a conforming
// random engine to speed initialization in the benchmarks.
class PrecompiledSeedSeq {
public:
using result_type = uint32_t;
PrecompiledSeedSeq() {}
template <typename Iterator>
PrecompiledSeedSeq(Iterator begin, Iterator end) {}
template <typename T>
PrecompiledSeedSeq(std::initializer_list<T> il) {}
template <typename OutIterator>
void generate(OutIterator begin, OutIterator end) {
static size_t idx = 0;
for (; begin != end; begin++) {
*begin = kSeedData[idx++];
if (idx >= ABSL_ARRAYSIZE(kSeedData)) {
idx = 0;
}
}
}
size_t size() const { return ABSL_ARRAYSIZE(kSeedData); }
template <typename OutIterator>
void param(OutIterator out) const {
std::copy(std::begin(kSeedData), std::end(kSeedData), out);
}
};
// use_default_initialization<T> indicates whether the random engine
// T must be default initialized, or whether we may initialize it using
// a seed sequence. This is used because some engines do not accept seed
// sequence-based initialization.
template <typename E>
using use_default_initialization = std::false_type;
// make_engine<T, SSeq> returns a random_engine which is initialized,
// either via the default constructor, when use_default_initialization<T>
// is true, or via the indicated seed sequence, SSeq.
template <typename Engine, typename SSeq = PrecompiledSeedSeq>
typename absl::enable_if_t<!use_default_initialization<Engine>::value, Engine>
make_engine() {
// Initialize the random engine using the seed sequence SSeq, which
// is constructed from the precompiled seed data.
SSeq seq(std::begin(kSeedData), std::end(kSeedData));
return Engine(seq);
}
template <typename Engine, typename SSeq = PrecompiledSeedSeq>
typename absl::enable_if_t<use_default_initialization<Engine>::value, Engine>
make_engine() {
// Initialize the random engine using the default constructor.
return Engine();
}
template <typename Engine, typename SSeq>
void BM_Construct(benchmark::State& state) {
for (auto _ : state) {
auto rng = make_engine<Engine, SSeq>();
benchmark::DoNotOptimize(rng());
}
}
template <typename Engine>
void BM_Direct(benchmark::State& state) {
using value_type = typename Engine::result_type;
// Direct use of the URBG.
auto rng = make_engine<Engine>();
for (auto _ : state) {
benchmark::DoNotOptimize(rng());
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
template <typename Engine>
void BM_Generate(benchmark::State& state) {
// std::generate makes a copy of the RNG; thus this tests the
// copy-constructor efficiency.
using value_type = typename Engine::result_type;
std::vector<value_type> v(64);
auto rng = make_engine<Engine>();
while (state.KeepRunningBatch(64)) {
std::generate(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, size_t elems>
void BM_Shuffle(benchmark::State& state) {
// Direct use of the Engine.
std::vector<uint32_t> v(elems);
while (state.KeepRunningBatch(elems)) {
auto rng = make_engine<Engine>();
std::shuffle(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, size_t elems>
void BM_ShuffleReuse(benchmark::State& state) {
// Direct use of the Engine.
std::vector<uint32_t> v(elems);
auto rng = make_engine<Engine>();
while (state.KeepRunningBatch(elems)) {
std::shuffle(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, typename Dist, typename... Args>
void BM_Dist(benchmark::State& state, Args&&... args) {
using value_type = typename Dist::result_type;
auto rng = make_engine<Engine>();
Dist dis{std::forward<Args>(args)...};
// Compare the following loop performance:
for (auto _ : state) {
benchmark::DoNotOptimize(dis(rng));
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
template <typename Engine, typename Dist>
void BM_Large(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type kMin = 0;
volatile value_type kMax = std::numeric_limits<value_type>::max() / 2 + 1;
BM_Dist<Engine, Dist>(state, kMin, kMax);
}
template <typename Engine, typename Dist>
void BM_Small(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type kMin = 0;
volatile value_type kMax = std::numeric_limits<value_type>::max() / 64 + 1;
BM_Dist<Engine, Dist>(state, kMin, kMax);
}
template <typename Engine, typename Dist, int A>
void BM_Bernoulli(benchmark::State& state) {
volatile double a = static_cast<double>(A) / 1000000;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int A, int B>
void BM_Beta(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type a = static_cast<value_type>(A) / 100;
volatile value_type b = static_cast<value_type>(B) / 100;
BM_Dist<Engine, Dist>(state, a, b);
}
template <typename Engine, typename Dist, int A>
void BM_Gamma(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type a = static_cast<value_type>(A) / 100;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int A = 100>
void BM_Poisson(benchmark::State& state) {
volatile double a = static_cast<double>(A) / 100;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int Q = 2, int V = 1>
void BM_Zipf(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile double q = Q;
volatile double v = V;
BM_Dist<Engine, Dist>(state, std::numeric_limits<value_type>::max(), q, v);
}
template <typename Engine, typename Dist>
void BM_Thread(benchmark::State& state) {
using value_type = typename Dist::result_type;
auto rng = make_engine<Engine>();
Dist dis{};
for (auto _ : state) {
benchmark::DoNotOptimize(dis(rng));
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
// NOTES:
//
// std::geometric_distribution is similar to the zipf distributions.
// The algorithm for the geometric_distribution is, basically,
// floor(log(1-X) / log(1-p))
// Normal benchmark suite
#define BM_BASIC(Engine) \
BENCHMARK_TEMPLATE(BM_Construct, Engine, PrecompiledSeedSeq); \
BENCHMARK_TEMPLATE(BM_Construct, Engine, std::seed_seq); \
BENCHMARK_TEMPLATE(BM_Direct, Engine); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 10); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::random_internal::FastUniformBits<uint32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::random_internal::FastUniformBits<uint64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<double>)
#define BM_COPY(Engine) BENCHMARK_TEMPLATE(BM_Generate, Engine)
#define BM_THREAD(Engine) \
BENCHMARK_TEMPLATE(BM_Thread, Engine, \
absl::uniform_int_distribution<int64_t>) \
->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Thread, Engine, \
absl::uniform_real_distribution<double>) \
->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000)->ThreadPerCpu();
#define BM_EXTENDED(Engine) \
/* -------------- Extended Uniform -----------------------*/ \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, std::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_real_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_real_distribution<double>); \
/* -------------- Other -----------------------*/ \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::normal_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::gaussian_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::exponential_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::exponential_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
10 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
10 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
13 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
13 * 100); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::log_uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::log_uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::geometric_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>); \
BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>, 2, \
3); \
BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, std::bernoulli_distribution, \
257305); \
BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, absl::bernoulli_distribution, \
257305); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 65, \
41); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 99, \
330); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 150, \
150); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 410, \
580); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 65, 41); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 99, \
330); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 150, \
150); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 410, \
580); \
BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<float>, 199); \
BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<double>, 199);
// ABSL Recommended interfaces.
BM_BASIC(absl::InsecureBitGen); // === pcg64_2018_engine
BM_BASIC(absl::BitGen); // === randen_engine<uint64_t>.
BM_THREAD(absl::BitGen);
BM_EXTENDED(absl::BitGen);
// Instantiate benchmarks for multiple engines.
using randen_engine_64 = absl::random_internal::randen_engine<uint64_t>;
using randen_engine_32 = absl::random_internal::randen_engine<uint32_t>;
// Comparison interfaces.
BM_BASIC(std::mt19937_64);
BM_COPY(std::mt19937_64);
BM_EXTENDED(std::mt19937_64);
BM_BASIC(randen_engine_64);
BM_COPY(randen_engine_64);
BM_EXTENDED(randen_engine_64);
BM_BASIC(std::mt19937);
BM_COPY(std::mt19937);
BM_BASIC(randen_engine_32);
BM_COPY(randen_engine_32);
} // namespace