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9dd3461 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | #pragma once
#include <c10/util/irange.h>
// define constants like M_PI and C keywords for MSVC
#ifdef _MSC_VER
#ifndef _USE_MATH_DEFINES
#define _USE_MATH_DEFINES
#endif
#include <math.h>
#endif
#include <array>
#include <cmath>
#include <cstdint>
namespace at {
constexpr int MERSENNE_STATE_N = 624;
constexpr int MERSENNE_STATE_M = 397;
constexpr uint32_t MATRIX_A = 0x9908b0df;
constexpr uint32_t UMASK = 0x80000000;
constexpr uint32_t LMASK = 0x7fffffff;
/**
* Note [Mt19937 Engine implementation]
* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Originally implemented in:
* http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/MTARCOK/mt19937ar-cok.c
* and modified with C++ constructs. Moreover the state array of the engine
* has been modified to hold 32 bit uints instead of 64 bits.
*
* Note that we reimplemented mt19937 instead of using std::mt19937 because,
* at::mt19937 turns out to be faster in the pytorch codebase. PyTorch builds with -O2
* by default and following are the benchmark numbers (benchmark code can be found at
* https://github.com/syed-ahmed/benchmark-rngs):
*
* with -O2
* Time to get 100000000 philox randoms with at::uniform_real_distribution = 0.462759s
* Time to get 100000000 at::mt19937 randoms with at::uniform_real_distribution = 0.39628s
* Time to get 100000000 std::mt19937 randoms with std::uniform_real_distribution = 0.352087s
* Time to get 100000000 std::mt19937 randoms with at::uniform_real_distribution = 0.419454s
*
* std::mt19937 is faster when used in conjunction with std::uniform_real_distribution,
* however we can't use std::uniform_real_distribution because of this bug:
* http://open-std.org/JTC1/SC22/WG21/docs/lwg-active.html#2524. Plus, even if we used
* std::uniform_real_distribution and filtered out the 1's, it is a different algorithm
* than what's in pytorch currently and that messes up the tests in tests_distributions.py.
* The other option, using std::mt19937 with at::uniform_real_distribution is a tad bit slower
* than at::mt19937 with at::uniform_real_distribution and hence, we went with the latter.
*
* Copyright notice:
* A C-program for MT19937, with initialization improved 2002/2/10.
* Coded by Takuji Nishimura and Makoto Matsumoto.
* This is a faster version by taking Shawn Cokus's optimization,
* Matthe Bellew's simplification, Isaku Wada's real version.
*
* Before using, initialize the state by using init_genrand(seed)
* or init_by_array(init_key, key_length).
*
* Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* 3. The names of its contributors may not be used to endorse or promote
* products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
*
* Any feedback is very welcome.
* http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
* email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
*/
/**
* mt19937_data_pod is used to get POD data in and out
* of mt19937_engine. Used in torch.get_rng_state and
* torch.set_rng_state functions.
*/
struct mt19937_data_pod {
uint64_t seed_;
int left_;
bool seeded_;
uint32_t next_;
std::array<uint32_t, MERSENNE_STATE_N> state_;
};
class mt19937_engine {
public:
inline explicit mt19937_engine(uint64_t seed = 5489) {
init_with_uint32(seed);
}
inline mt19937_data_pod data() const {
return data_;
}
inline void set_data(mt19937_data_pod data) {
data_ = data;
}
inline uint64_t seed() const {
return data_.seed_;
}
inline bool is_valid() {
if ((data_.seeded_ == true)
&& (data_.left_ > 0 && data_.left_ <= MERSENNE_STATE_N)
&& (data_.next_ <= MERSENNE_STATE_N)) {
return true;
}
return false;
}
inline uint32_t operator()() {
uint32_t y;
if (--(data_.left_) == 0) {
next_state();
}
y = *(data_.state_.data() + data_.next_++);
y ^= (y >> 11);
y ^= (y << 7) & 0x9d2c5680;
y ^= (y << 15) & 0xefc60000;
y ^= (y >> 18);
return y;
}
private:
mt19937_data_pod data_;
inline void init_with_uint32(uint64_t seed) {
data_.seed_ = seed;
data_.seeded_ = true;
data_.state_[0] = seed & 0xffffffff;
for (const auto j : c10::irange(1, MERSENNE_STATE_N)) {
data_.state_[j] = (1812433253 * (data_.state_[j-1] ^ (data_.state_[j-1] >> 30)) + j);
}
data_.left_ = 1;
data_.next_ = 0;
}
inline uint32_t mix_bits(uint32_t u, uint32_t v) {
return (u & UMASK) | (v & LMASK);
}
inline uint32_t twist(uint32_t u, uint32_t v) {
return (mix_bits(u,v) >> 1) ^ (v & 1 ? MATRIX_A : 0);
}
inline void next_state() {
uint32_t* p = data_.state_.data();
data_.left_ = MERSENNE_STATE_N;
data_.next_ = 0;
for(int j = MERSENNE_STATE_N - MERSENNE_STATE_M + 1; --j; p++) {
*p = p[MERSENNE_STATE_M] ^ twist(p[0], p[1]);
}
for(int j = MERSENNE_STATE_M; --j; p++) {
*p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], p[1]);
}
*p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], data_.state_[0]);
}
};
typedef mt19937_engine mt19937;
} // namespace at
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