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https://github.com/paboyle/Grid.git
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Merge branch 'develop' into feature/hmc_generalise
This commit is contained in:
Guido Cossu
@ -30,11 +30,19 @@
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#define GRID_LATTICE_RNG_H
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#include <random>
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#ifdef RNG_SITMO
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#include <Grid/sitmo_rng/sitmo_prng_engine.hpp>
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#endif
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#if defined(RNG_SITMO)
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#define RNG_FAST_DISCARD
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#else
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#undef RNG_FAST_DISCARD
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#endif
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namespace Grid {
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//////////////////////////////////////////////////////////////
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// Allow the RNG state to be less dense than the fine grid
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//////////////////////////////////////////////////////////////
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@ -64,16 +72,19 @@ namespace Grid {
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multiplicity = multiplicity *fine->_rdimensions[fd] / coarse->_rdimensions[d];
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}
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return multiplicity;
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}
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// merge of April 11 2017
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//<<<<<<< HEAD
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inline int RNGfillable_general(GridBase *coarse,GridBase *fine)
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// this function is necessary for the LS vectorised field
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inline int RNGfillable_general(GridBase *coarse,GridBase *fine)
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{
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int rngdims = coarse->_ndimension;
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// trivially extended in higher dims, with locality guaranteeing RNG state is local to node
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int lowerdims = fine->_ndimension - coarse->_ndimension; assert(lowerdims >= 0);
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// assumes that the higher dimensions are not using more processors
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@ -92,6 +103,7 @@ namespace Grid {
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return fine->lSites() / coarse->lSites();
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}
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/*
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// Wrap seed_seq to give common interface with random_device
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class fixedSeed {
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public:
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@ -108,89 +120,140 @@ namespace Grid {
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};
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=======
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>>>>>>> develop
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*/
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// real scalars are one component
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template<class scalar,class distribution,class generator> void fillScalar(scalar &s,distribution &dist,generator & gen)
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template<class scalar,class distribution,class generator>
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void fillScalar(scalar &s,distribution &dist,generator & gen)
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{
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s=dist(gen);
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}
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template<class distribution,class generator> void fillScalar(ComplexF &s,distribution &dist, generator &gen)
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template<class distribution,class generator>
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void fillScalar(ComplexF &s,distribution &dist, generator &gen)
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{
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s=ComplexF(dist(gen),dist(gen));
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}
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template<class distribution,class generator> void fillScalar(ComplexD &s,distribution &dist,generator &gen)
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template<class distribution,class generator>
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void fillScalar(ComplexD &s,distribution &dist,generator &gen)
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{
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s=ComplexD(dist(gen),dist(gen));
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}
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class GridRNGbase {
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public:
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int _seeded;
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// One generator per site.
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// Uniform and Gaussian distributions from these generators.
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#ifdef RNG_RANLUX
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typedef uint64_t RngStateType;
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typedef std::ranlux48 RngEngine;
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typedef uint64_t RngStateType;
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static const int RngStateCount = 15;
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#elif RNG_MT19937
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#endif
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#ifdef RNG_MT19937
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typedef std::mt19937 RngEngine;
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typedef uint32_t RngStateType;
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static const int RngStateCount = std::mt19937::state_size;
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#elif RNG_SITMO
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#endif
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#ifdef RNG_SITMO
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typedef sitmo::prng_engine RngEngine;
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typedef uint64_t RngStateType;
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static const int RngStateCount = 4;
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#endif
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std::vector<RngEngine> _generators;
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std::vector<std::uniform_real_distribution<RealD>> _uniform;
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std::vector<std::normal_distribution<RealD>> _gaussian;
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std::vector<std::discrete_distribution<int32_t>> _bernoulli;
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void GetState(std::vector<RngStateType> & saved,int gen) {
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std::vector<RngEngine> _generators;
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std::vector<std::uniform_real_distribution<RealD> > _uniform;
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std::vector<std::normal_distribution<RealD> > _gaussian;
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std::vector<std::discrete_distribution<int32_t> > _bernoulli;
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std::vector<std::uniform_int_distribution<uint32_t> > _uid;
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///////////////////////
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// support for parallel init
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///////////////////////
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#ifdef RNG_FAST_DISCARD
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static void Skip(RngEngine &eng)
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{
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/////////////////////////////////////////////////////////////////////////////////////
|
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// Skip by 2^40 elements between successive lattice sites
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// This goes by 10^12.
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// Consider quenched updating; likely never exceeding rate of 1000 sweeps
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// per second on any machine. This gives us of order 10^9 seconds, or 100 years
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// skip ahead.
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// For HMC unlikely to go at faster than a solve per second, and
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// tens of seconds per trajectory so this is clean in all reasonable cases,
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// and margin of safety is orders of magnitude.
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// We could hack Sitmo to skip in the higher order words of state if necessary
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/////////////////////////////////////////////////////////////////////////////////////
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uint64_t skip = 0x1; skip = skip<<40;
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eng.discard(skip);
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}
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#endif
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static RngEngine Reseed(RngEngine &eng)
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{
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std::vector<uint32_t> newseed;
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std::uniform_int_distribution<uint32_t> uid;
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return Reseed(eng,newseed,uid);
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}
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static RngEngine Reseed(RngEngine &eng,std::vector<uint32_t> & newseed,
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std::uniform_int_distribution<uint32_t> &uid)
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{
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const int reseeds=4;
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newseed.resize(reseeds);
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for(int i=0;i<reseeds;i++){
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newseed[i] = uid(eng);
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}
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std::seed_seq sseq(newseed.begin(),newseed.end());
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return RngEngine(sseq);
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}
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void GetState(std::vector<RngStateType> & saved,RngEngine &eng) {
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saved.resize(RngStateCount);
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std::stringstream ss;
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ss<<_generators[gen];
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ss<<eng;
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ss.seekg(0,ss.beg);
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for(int i=0;i<RngStateCount;i++){
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ss>>saved[i];
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}
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}
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void SetState(std::vector<RngStateType> & saved,int gen){
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void GetState(std::vector<RngStateType> & saved,int gen) {
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GetState(saved,_generators[gen]);
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}
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void SetState(std::vector<RngStateType> & saved,RngEngine &eng){
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assert(saved.size()==RngStateCount);
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std::stringstream ss;
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for(int i=0;i<RngStateCount;i++){
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ss<< saved[i]<<" ";
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}
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||||
ss.seekg(0,ss.beg);
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ss>>_generators[gen];
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ss>>eng;
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}
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void SetState(std::vector<RngStateType> & saved,int gen){
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SetState(saved,_generators[gen]);
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}
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void SetEngine(RngEngine &Eng, int gen){
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_generators[gen]=Eng;
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}
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void GetEngine(RngEngine &Eng, int gen){
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Eng=_generators[gen];
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}
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template<class source> void Seed(source &src, int gen)
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{
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_generators[gen] = RngEngine(src);
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}
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};
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class GridSerialRNG : public GridRNGbase {
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public:
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// FIXME ... do we require lockstep draws of randoms
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// from all nodes keeping seeds consistent.
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// place a barrier/broadcast in the fill routine
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template<class source> void Seed(source &src)
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{
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typename source::result_type init = src();
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CartesianCommunicator::BroadcastWorld(0,(void *)&init,sizeof(init));
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_generators[0] = RngEngine(init);
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_seeded=1;
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}
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GridSerialRNG() : GridRNGbase() {
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_generators.resize(1);
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_uniform.resize(1,std::uniform_real_distribution<RealD>{0,1});
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_gaussian.resize(1,std::normal_distribution<RealD>(0.0,1.0) );
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_bernoulli.resize(1,std::discrete_distribution<int32_t>{1,1});
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_seeded=0;
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_uid.resize(1,std::uniform_int_distribution<uint32_t>() );
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}
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template <class sobj,class distribution> inline void fill(sobj &l,std::vector<distribution> &dist){
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typedef typename sobj::scalar_type scalar_type;
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@ -203,7 +266,7 @@ namespace Grid {
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for(int idx=0;idx<words;idx++){
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fillScalar(buf[idx],dist[0],_generators[0]);
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}
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CartesianCommunicator::BroadcastWorld(0,(void *)&l,sizeof(l));
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};
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@ -257,21 +320,16 @@ namespace Grid {
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RealD *pointer=(RealD *)&l;
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dist[0].reset();
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for(int i=0;i<vRealD::Nsimd();i++){
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fillScalar(pointer[i],dist[0],_generators[0]);
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fillScalar(pointer[i],dist[0],_generators[0]);
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}
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CartesianCommunicator::BroadcastWorld(0,(void *)&l,sizeof(l));
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}
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|
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|
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void SeedRandomDevice(void){
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std::random_device rd;
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Seed(rd);
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}
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void SeedFixedIntegers(const std::vector<int> &seeds){
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fixedSeed src(seeds);
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Seed(src);
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CartesianCommunicator::BroadcastWorld(0,(void *)&seeds[0],sizeof(int)*seeds.size());
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std::seed_seq src(seeds.begin(),seeds.end());
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Seed(src,0);
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}
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};
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class GridParallelRNG : public GridRNGbase {
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@ -279,7 +337,6 @@ namespace Grid {
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double _time_counter;
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public:
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||||
|
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GridBase *_grid;
|
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unsigned int _vol;
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@ -295,61 +352,11 @@ namespace Grid {
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||||
_uniform.resize(_vol,std::uniform_real_distribution<RealD>{0,1});
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_gaussian.resize(_vol,std::normal_distribution<RealD>(0.0,1.0) );
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_bernoulli.resize(_vol,std::discrete_distribution<int32_t>{1,1});
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_seeded = 0;
|
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|
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_time_counter = 0.0;
|
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|
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_uid.resize(_vol,std::uniform_int_distribution<uint32_t>() );
|
||||
}
|
||||
|
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template <class vobj,class distribution> inline void fill(Lattice<vobj> &l,std::vector<distribution> &dist){
|
||||
|
||||
// This loop could be made faster to avoid the Ahmdahl by
|
||||
// i) seed generators on each timeslice, for x=y=z=0;
|
||||
// ii) seed generators on each z for x=y=0
|
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// iii)seed generators on each y,z for x=0
|
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// iv) seed generators on each y,z,x
|
||||
// made possible by physical indexing.
|
||||
template<class source> void Seed(source &src)
|
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{
|
||||
std::vector<int> gcoor;
|
||||
|
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int gsites = _grid->_gsites;
|
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|
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typename source::result_type init = src();
|
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RngEngine pseeder(init);
|
||||
std::uniform_int_distribution<uint64_t> ui;
|
||||
|
||||
for(int gidx=0;gidx<gsites;gidx++){
|
||||
|
||||
int rank,o_idx,i_idx;
|
||||
_grid->GlobalIndexToGlobalCoor(gidx,gcoor);
|
||||
_grid->GlobalCoorToRankIndex(rank,o_idx,i_idx,gcoor);
|
||||
|
||||
int l_idx=generator_idx(o_idx,i_idx);
|
||||
|
||||
const int num_rand_seed=16;
|
||||
std::vector<int> site_seeds(num_rand_seed);
|
||||
for(int i=0;i<site_seeds.size();i++){
|
||||
site_seeds[i]= ui(pseeder);
|
||||
}
|
||||
|
||||
|
||||
_grid->Broadcast(0,(void *)&site_seeds[0],sizeof(int)*site_seeds.size());
|
||||
|
||||
if( rank == _grid->ThisRank() ){
|
||||
fixedSeed ssrc(site_seeds);
|
||||
typename source::result_type sinit = ssrc();
|
||||
_generators[l_idx] = RngEngine(sinit);
|
||||
}
|
||||
}
|
||||
_seeded=1;
|
||||
}
|
||||
|
||||
//FIXME implement generic IO and create state save/restore
|
||||
//void SaveState(const std::string<char> &file);
|
||||
//void LoadState(const std::string<char> &file);
|
||||
|
||||
template <class vobj, class distribution>
|
||||
inline void fill(Lattice<vobj> &l, std::vector<distribution> &dist) {
|
||||
typedef typename vobj::scalar_object scalar_object;
|
||||
typedef typename vobj::scalar_type scalar_type;
|
||||
typedef typename vobj::vector_type vector_type;
|
||||
@ -357,14 +364,11 @@ namespace Grid {
|
||||
double inner_time_counter = usecond();
|
||||
|
||||
int multiplicity = RNGfillable_general(_grid, l._grid); // l has finer or same grid
|
||||
int Nsimd = _grid->Nsimd(); // guaranteed to be the same for l._grid too
|
||||
int osites = _grid->oSites(); // guaranteed to be <= l._grid->oSites() by a factor multiplicity
|
||||
int words = sizeof(scalar_object) / sizeof(scalar_type);
|
||||
|
||||
int Nsimd = _grid->Nsimd();// guaranteed to be the same for l._grid too
|
||||
int osites = _grid->oSites();// guaranteed to be <= l._grid->oSites() by a factor multiplicity
|
||||
int words = sizeof(scalar_object) / sizeof(scalar_type);
|
||||
|
||||
PARALLEL_FOR_LOOP
|
||||
for (int ss = 0; ss < osites; ss++) {
|
||||
|
||||
parallel_for(int ss=0;ss<osites;ss++){
|
||||
std::vector<scalar_object> buf(Nsimd);
|
||||
for (int m = 0; m < multiplicity; m++) { // Draw from same generator multiplicity times
|
||||
|
||||
@ -386,13 +390,79 @@ namespace Grid {
|
||||
_time_counter += usecond()- inner_time_counter;
|
||||
};
|
||||
|
||||
void SeedRandomDevice(void) {
|
||||
std::random_device rd;
|
||||
Seed(rd);
|
||||
}
|
||||
void SeedFixedIntegers(const std::vector<int> &seeds) {
|
||||
fixedSeed src(seeds);
|
||||
Seed(src);
|
||||
void SeedFixedIntegers(const std::vector<int> &seeds){
|
||||
|
||||
// Everyone generates the same seed_seq based on input seeds
|
||||
CartesianCommunicator::BroadcastWorld(0,(void *)&seeds[0],sizeof(int)*seeds.size());
|
||||
|
||||
std::seed_seq source(seeds.begin(),seeds.end());
|
||||
|
||||
RngEngine master_engine(source);
|
||||
|
||||
#ifdef RNG_FAST_DISCARD
|
||||
////////////////////////////////////////////////
|
||||
// Skip ahead through a single stream.
|
||||
// Applicable to SITMO and other has based/crypto RNGs
|
||||
// Should be applicable to Mersenne Twister, but the C++11
|
||||
// MT implementation does not implement fast discard even though
|
||||
// in principle this is possible
|
||||
////////////////////////////////////////////////
|
||||
std::vector<int> gcoor;
|
||||
int rank,o_idx,i_idx;
|
||||
|
||||
// Everybody loops over global volume.
|
||||
for(int gidx=0;gidx<_grid->_gsites;gidx++){
|
||||
|
||||
Skip(master_engine); // Skip to next RNG sequence
|
||||
|
||||
// Where is it?
|
||||
_grid->GlobalIndexToGlobalCoor(gidx,gcoor);
|
||||
_grid->GlobalCoorToRankIndex(rank,o_idx,i_idx,gcoor);
|
||||
|
||||
// If this is one of mine we take it
|
||||
if( rank == _grid->ThisRank() ){
|
||||
int l_idx=generator_idx(o_idx,i_idx);
|
||||
_generators[l_idx] = master_engine;
|
||||
}
|
||||
|
||||
}
|
||||
#else
|
||||
////////////////////////////////////////////////////////////////
|
||||
// Machine and thread decomposition dependent seeding is efficient
|
||||
// and maximally parallel; but NOT reproducible from machine to machine.
|
||||
// Not ideal, but fastest way to reseed all nodes.
|
||||
////////////////////////////////////////////////////////////////
|
||||
{
|
||||
// Obtain one Reseed per processor
|
||||
int Nproc = _grid->ProcessorCount();
|
||||
std::vector<RngEngine> seeders(Nproc);
|
||||
int me= _grid->ThisRank();
|
||||
for(int p=0;p<Nproc;p++){
|
||||
seeders[p] = Reseed(master_engine);
|
||||
}
|
||||
master_engine = seeders[me];
|
||||
}
|
||||
|
||||
{
|
||||
// Obtain one reseeded generator per thread
|
||||
int Nthread = GridThread::GetThreads();
|
||||
std::vector<RngEngine> seeders(Nthread);
|
||||
for(int t=0;t<Nthread;t++){
|
||||
seeders[t] = Reseed(master_engine);
|
||||
}
|
||||
|
||||
parallel_for(int t=0;t<Nthread;t++) {
|
||||
// set up one per local site in threaded fashion
|
||||
std::vector<uint32_t> newseeds;
|
||||
std::uniform_int_distribution<uint32_t> uid;
|
||||
for(int l=0;l<_grid->lSites();l++) {
|
||||
if ( (l%Nthread)==t ) {
|
||||
_generators[l] = Reseed(seeders[t],newseeds,uid);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void Report(){
|
||||
@ -400,31 +470,39 @@ namespace Grid {
|
||||
}
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
// Support for rigorous test of RNG's
|
||||
// Return uniform random uint32_t from requested site generator
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
uint32_t GlobalU01(int gsite){
|
||||
|
||||
uint32_t the_number;
|
||||
// who
|
||||
std::vector<int> gcoor;
|
||||
int rank,o_idx,i_idx;
|
||||
_grid->GlobalIndexToGlobalCoor(gsite,gcoor);
|
||||
_grid->GlobalCoorToRankIndex(rank,o_idx,i_idx,gcoor);
|
||||
|
||||
// draw
|
||||
int l_idx=generator_idx(o_idx,i_idx);
|
||||
if( rank == _grid->ThisRank() ){
|
||||
the_number = _uid[l_idx](_generators[l_idx]);
|
||||
}
|
||||
|
||||
// share & return
|
||||
_grid->Broadcast(rank,(void *)&the_number,sizeof(the_number));
|
||||
return the_number;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
template <class vobj> inline void random(GridParallelRNG &rng,Lattice<vobj> &l){
|
||||
rng.fill(l,rng._uniform);
|
||||
}
|
||||
template <class vobj> inline void random(GridParallelRNG &rng,Lattice<vobj> &l) { rng.fill(l,rng._uniform); }
|
||||
template <class vobj> inline void gaussian(GridParallelRNG &rng,Lattice<vobj> &l) { rng.fill(l,rng._gaussian); }
|
||||
template <class vobj> inline void bernoulli(GridParallelRNG &rng,Lattice<vobj> &l){ rng.fill(l,rng._bernoulli);}
|
||||
|
||||
template <class vobj> inline void gaussian(GridParallelRNG &rng,Lattice<vobj> &l){
|
||||
rng.fill(l,rng._gaussian);
|
||||
}
|
||||
|
||||
template <class vobj> inline void bernoulli(GridParallelRNG &rng,Lattice<vobj> &l){
|
||||
rng.fill(l,rng._bernoulli);
|
||||
}
|
||||
|
||||
template <class sobj> inline void random(GridSerialRNG &rng,sobj &l){
|
||||
rng.fill(l,rng._uniform);
|
||||
}
|
||||
|
||||
template <class sobj> inline void gaussian(GridSerialRNG &rng,sobj &l){
|
||||
rng.fill(l,rng._gaussian);
|
||||
}
|
||||
|
||||
template <class sobj> inline void bernoulli(GridSerialRNG &rng,sobj &l){
|
||||
rng.fill(l,rng._bernoulli);
|
||||
}
|
||||
template <class sobj> inline void random(GridSerialRNG &rng,sobj &l) { rng.fill(l,rng._uniform ); }
|
||||
template <class sobj> inline void gaussian(GridSerialRNG &rng,sobj &l) { rng.fill(l,rng._gaussian ); }
|
||||
template <class sobj> inline void bernoulli(GridSerialRNG &rng,sobj &l){ rng.fill(l,rng._bernoulli); }
|
||||
|
||||
}
|
||||
#endif
|
||||
|
||||
Reference in New Issue
Block a user