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Merge pull request #426 from rrhodgson/feature/LCDeflation

Batched Local Coherence Tools
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Peter Boyle 2023-03-21 08:58:40 -04:00 committed by GitHub
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@ -54,6 +54,7 @@ NAMESPACE_CHECK(BiCGSTAB);
#include <Grid/algorithms/iterative/SchurRedBlack.h>
#include <Grid/algorithms/iterative/ConjugateGradientMultiShift.h>
#include <Grid/algorithms/iterative/ConjugateGradientMixedPrec.h>
#include <Grid/algorithms/iterative/ConjugateGradientMixedPrecBatched.h>
#include <Grid/algorithms/iterative/BiCGSTABMixedPrec.h>
#include <Grid/algorithms/iterative/BlockConjugateGradient.h>
#include <Grid/algorithms/iterative/ConjugateGradientReliableUpdate.h>

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@ -0,0 +1,213 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateGradientMixedPrecBatched.h
Copyright (C) 2015
Author: Raoul Hodgson <raoul.hodgson@ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_GRADIENT_MIXED_PREC_BATCHED_H
#define GRID_CONJUGATE_GRADIENT_MIXED_PREC_BATCHED_H
NAMESPACE_BEGIN(Grid);
//Mixed precision restarted defect correction CG
template<class FieldD,class FieldF,
typename std::enable_if< getPrecision<FieldD>::value == 2, int>::type = 0,
typename std::enable_if< getPrecision<FieldF>::value == 1, int>::type = 0>
class MixedPrecisionConjugateGradientBatched : public LinearFunction<FieldD> {
public:
using LinearFunction<FieldD>::operator();
RealD Tolerance;
RealD InnerTolerance; //Initial tolerance for inner CG. Defaults to Tolerance but can be changed
Integer MaxInnerIterations;
Integer MaxOuterIterations;
Integer MaxPatchupIterations;
GridBase* SinglePrecGrid; //Grid for single-precision fields
RealD OuterLoopNormMult; //Stop the outer loop and move to a final double prec solve when the residual is OuterLoopNormMult * Tolerance
LinearOperatorBase<FieldF> &Linop_f;
LinearOperatorBase<FieldD> &Linop_d;
//Option to speed up *inner single precision* solves using a LinearFunction that produces a guess
LinearFunction<FieldF> *guesser;
bool updateResidual;
MixedPrecisionConjugateGradientBatched(RealD tol,
Integer maxinnerit,
Integer maxouterit,
Integer maxpatchit,
GridBase* _sp_grid,
LinearOperatorBase<FieldF> &_Linop_f,
LinearOperatorBase<FieldD> &_Linop_d,
bool _updateResidual=true) :
Linop_f(_Linop_f), Linop_d(_Linop_d),
Tolerance(tol), InnerTolerance(tol), MaxInnerIterations(maxinnerit), MaxOuterIterations(maxouterit), MaxPatchupIterations(maxpatchit), SinglePrecGrid(_sp_grid),
OuterLoopNormMult(100.), guesser(NULL), updateResidual(_updateResidual) { };
void useGuesser(LinearFunction<FieldF> &g){
guesser = &g;
}
void operator() (const FieldD &src_d_in, FieldD &sol_d){
std::vector<FieldD> srcs_d_in{src_d_in};
std::vector<FieldD> sols_d{sol_d};
(*this)(srcs_d_in,sols_d);
sol_d = sols_d[0];
}
void operator() (const std::vector<FieldD> &src_d_in, std::vector<FieldD> &sol_d){
assert(src_d_in.size() == sol_d.size());
int NBatch = src_d_in.size();
std::cout << GridLogMessage << "NBatch = " << NBatch << std::endl;
Integer TotalOuterIterations = 0; //Number of restarts
std::vector<Integer> TotalInnerIterations(NBatch,0); //Number of inner CG iterations
std::vector<Integer> TotalFinalStepIterations(NBatch,0); //Number of CG iterations in final patch-up step
GridStopWatch TotalTimer;
TotalTimer.Start();
GridStopWatch InnerCGtimer;
GridStopWatch PrecChangeTimer;
int cb = src_d_in[0].Checkerboard();
std::vector<RealD> src_norm;
std::vector<RealD> norm;
std::vector<RealD> stop;
GridBase* DoublePrecGrid = src_d_in[0].Grid();
FieldD tmp_d(DoublePrecGrid);
tmp_d.Checkerboard() = cb;
FieldD tmp2_d(DoublePrecGrid);
tmp2_d.Checkerboard() = cb;
std::vector<FieldD> src_d;
std::vector<FieldF> src_f;
std::vector<FieldF> sol_f;
for (int i=0; i<NBatch; i++) {
sol_d[i].Checkerboard() = cb;
src_norm.push_back(norm2(src_d_in[i]));
norm.push_back(0.);
stop.push_back(src_norm[i] * Tolerance*Tolerance);
src_d.push_back(src_d_in[i]); //source for next inner iteration, computed from residual during operation
src_f.push_back(SinglePrecGrid);
src_f[i].Checkerboard() = cb;
sol_f.push_back(SinglePrecGrid);
sol_f[i].Checkerboard() = cb;
}
RealD inner_tol = InnerTolerance;
ConjugateGradient<FieldF> CG_f(inner_tol, MaxInnerIterations);
CG_f.ErrorOnNoConverge = false;
Integer &outer_iter = TotalOuterIterations; //so it will be equal to the final iteration count
for(outer_iter = 0; outer_iter < MaxOuterIterations; outer_iter++){
std::cout << GridLogMessage << std::endl;
std::cout << GridLogMessage << "Outer iteration " << outer_iter << std::endl;
bool allConverged = true;
for (int i=0; i<NBatch; i++) {
//Compute double precision rsd and also new RHS vector.
Linop_d.HermOp(sol_d[i], tmp_d);
norm[i] = axpy_norm(src_d[i], -1., tmp_d, src_d_in[i]); //src_d is residual vector
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradientBatched: Outer iteration " << outer_iter <<" solve " << i << " residual "<< norm[i] << " target "<< stop[i] <<std::endl;
PrecChangeTimer.Start();
precisionChange(src_f[i], src_d[i]);
PrecChangeTimer.Stop();
sol_f[i] = Zero();
if(norm[i] > OuterLoopNormMult * stop[i]) {
allConverged = false;
}
}
if (allConverged) break;
if (updateResidual) {
RealD normMax = *std::max_element(std::begin(norm), std::end(norm));
RealD stopMax = *std::max_element(std::begin(stop), std::end(stop));
while( normMax * inner_tol * inner_tol < stopMax) inner_tol *= 2; // inner_tol = sqrt(stop/norm) ??
CG_f.Tolerance = inner_tol;
}
//Optionally improve inner solver guess (eg using known eigenvectors)
if(guesser != NULL) {
(*guesser)(src_f, sol_f);
}
for (int i=0; i<NBatch; i++) {
//Inner CG
InnerCGtimer.Start();
CG_f(Linop_f, src_f[i], sol_f[i]);
InnerCGtimer.Stop();
TotalInnerIterations[i] += CG_f.IterationsToComplete;
//Convert sol back to double and add to double prec solution
PrecChangeTimer.Start();
precisionChange(tmp_d, sol_f[i]);
PrecChangeTimer.Stop();
axpy(sol_d[i], 1.0, tmp_d, sol_d[i]);
}
}
//Final trial CG
std::cout << GridLogMessage << std::endl;
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradientBatched: Starting final patch-up double-precision solve"<<std::endl;
for (int i=0; i<NBatch; i++) {
ConjugateGradient<FieldD> CG_d(Tolerance, MaxPatchupIterations);
CG_d(Linop_d, src_d_in[i], sol_d[i]);
TotalFinalStepIterations[i] += CG_d.IterationsToComplete;
}
TotalTimer.Stop();
std::cout << GridLogMessage << std::endl;
for (int i=0; i<NBatch; i++) {
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradientBatched: solve " << i << " Inner CG iterations " << TotalInnerIterations[i] << " Restarts " << TotalOuterIterations << " Final CG iterations " << TotalFinalStepIterations[i] << std::endl;
}
std::cout << GridLogMessage << std::endl;
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradientBatched: Total time " << TotalTimer.Elapsed() << " Precision change " << PrecChangeTimer.Elapsed() << " Inner CG total " << InnerCGtimer.Elapsed() << std::endl;
}
};
NAMESPACE_END(Grid);
#endif

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@ -288,7 +288,36 @@ inline void blockProject(Lattice<iVector<CComplex,nbasis > > &coarseData,
blockZAXPY(fineDataRed,ip,Basis[v],fineDataRed);
}
}
template<class vobj,class CComplex,int nbasis,class VLattice>
inline void batchBlockProject(std::vector<Lattice<iVector<CComplex,nbasis>>> &coarseData,
const std::vector<Lattice<vobj>> &fineData,
const VLattice &Basis)
{
int NBatch = fineData.size();
assert(coarseData.size() == NBatch);
GridBase * fine = fineData[0].Grid();
GridBase * coarse= coarseData[0].Grid();
Lattice<iScalar<CComplex>> ip(coarse);
std::vector<Lattice<vobj>> fineDataCopy = fineData;
autoView(ip_, ip, AcceleratorWrite);
for(int v=0;v<nbasis;v++) {
for (int k=0; k<NBatch; k++) {
autoView( coarseData_ , coarseData[k], AcceleratorWrite);
blockInnerProductD(ip,Basis[v],fineDataCopy[k]); // ip = <basis|fine>
accelerator_for( sc, coarse->oSites(), vobj::Nsimd(), {
convertType(coarseData_[sc](v),ip_[sc]);
});
// improve numerical stability of projection
// |fine> = |fine> - <basis|fine> |basis>
ip=-ip;
blockZAXPY(fineDataCopy[k],ip,Basis[v],fineDataCopy[k]);
}
}
}
template<class vobj,class vobj2,class CComplex>
inline void blockZAXPY(Lattice<vobj> &fineZ,
@ -590,6 +619,26 @@ inline void blockPromote(const Lattice<iVector<CComplex,nbasis > > &coarseData,
}
#endif
template<class vobj,class CComplex,int nbasis,class VLattice>
inline void batchBlockPromote(const std::vector<Lattice<iVector<CComplex,nbasis>>> &coarseData,
std::vector<Lattice<vobj>> &fineData,
const VLattice &Basis)
{
int NBatch = coarseData.size();
assert(fineData.size() == NBatch);
GridBase * fine = fineData[0].Grid();
GridBase * coarse = coarseData[0].Grid();
for (int k=0; k<NBatch; k++)
fineData[k]=Zero();
for (int i=0;i<nbasis;i++) {
for (int k=0; k<NBatch; k++) {
Lattice<iScalar<CComplex>> ip = PeekIndex<0>(coarseData[k],i);
blockZAXPY(fineData[k],ip,Basis[i],fineData[k]);
}
}
}
// Useful for precision conversion, or indeed anything where an operator= does a conversion on scalars.
// Simd layouts need not match since we use peek/poke Local
template<class vobj,class vvobj>