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MT19937RNGEngine.h
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MT19937RNGEngine.h
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#pragma once
// define constants like M_PI and C keywords for MSVC
#ifdef _MSC_VER
#define _USE_MATH_DEFINES
#include <math.h>
#endif
#include <stdint.h>
#include <cmath>
#include <array>
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 conjuction 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(int j = 1; j < MERSENNE_STATE_N; j++) {
data_.state_[j] = (1812433253 * (data_.state_[j-1] ^ (data_.state_[j-1] >> 30)) + j);
data_.state_[j] &= 0xffffffff;
}
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