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synced 2025-06-12 20:27:06 +01:00
Merge branch 'develop' into feature/dwf-multirhs
This commit is contained in:
paboyle
@ -87,15 +87,22 @@ void ThinQRfact (Eigen::MatrixXcd &m_rr,
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////////////////////////////////////////////////////////////////////////////////////////////////////
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sliceInnerProductMatrix(m_rr,R,R,Orthog);
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// Cholesky from Eigen
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// There exists a ldlt that is documented as more stable
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////////////////////////////////////////////////////////////////////////////////////////////////////
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Eigen::MatrixXcd L = m_rr.llt().matrixL();
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// Force manifest hermitian to avoid rounding related
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m_rr = 0.5*(m_rr+m_rr.adjoint());
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#if 0
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std::cout << " Calling Cholesky ldlt on m_rr " << m_rr <<std::endl;
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Eigen::MatrixXcd L_ldlt = m_rr.ldlt().matrixL();
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std::cout << " Called Cholesky ldlt on m_rr " << L_ldlt <<std::endl;
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auto D_ldlt = m_rr.ldlt().vectorD();
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std::cout << " Called Cholesky ldlt on m_rr " << D_ldlt <<std::endl;
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#endif
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// std::cout << " Calling Cholesky llt on m_rr " <<std::endl;
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Eigen::MatrixXcd L = m_rr.llt().matrixL();
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// std::cout << " Called Cholesky llt on m_rr " << L <<std::endl;
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C = L.adjoint();
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Cinv = C.inverse();
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// Q = R C^{-1}
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//
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@ -103,7 +110,6 @@ void ThinQRfact (Eigen::MatrixXcd &m_rr,
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//
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// NB maddMatrix conventions are Right multiplication X[j] a[j,i] already
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// FIXME:: make a sliceMulMatrix to avoid zero vector
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sliceMulMatrix(Q,Cinv,R,Orthog);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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@ -199,7 +205,12 @@ void BlockCGrQsolve(LinearOperatorBase<Field> &Linop, const Field &B, Field &X)
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Linop.HermOp(X, AD);
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tmp = B - AD;
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//std::cout << GridLogMessage << " initial tmp " << norm2(tmp)<< std::endl;
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ThinQRfact (m_rr, m_C, m_Cinv, Q, tmp);
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//std::cout << GridLogMessage << " initial Q " << norm2(Q)<< std::endl;
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//std::cout << GridLogMessage << " m_rr " << m_rr<<std::endl;
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//std::cout << GridLogMessage << " m_C " << m_C<<std::endl;
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//std::cout << GridLogMessage << " m_Cinv " << m_Cinv<<std::endl;
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D=Q;
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std::cout << GridLogMessage<<"BlockCGrQ computed initial residual and QR fact " <<std::endl;
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@ -221,13 +232,15 @@ void BlockCGrQsolve(LinearOperatorBase<Field> &Linop, const Field &B, Field &X)
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MatrixTimer.Start();
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Linop.HermOp(D, Z);
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MatrixTimer.Stop();
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//std::cout << GridLogMessage << " norm2 Z " <<norm2(Z)<<std::endl;
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//4. M = [D^dag Z]^{-1}
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sliceInnerTimer.Start();
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sliceInnerProductMatrix(m_DZ,D,Z,Orthog);
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sliceInnerTimer.Stop();
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m_M = m_DZ.inverse();
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//std::cout << GridLogMessage << " m_DZ " <<m_DZ<<std::endl;
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//5. X = X + D MC
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m_tmp = m_M * m_C;
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sliceMaddTimer.Start();
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256
lib/algorithms/iterative/ConjugateGradientReliableUpdate.h
Normal file
256
lib/algorithms/iterative/ConjugateGradientReliableUpdate.h
Normal file
@ -0,0 +1,256 @@
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/*************************************************************************************
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Grid physics library, www.github.com/paboyle/Grid
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Source file: ./lib/algorithms/iterative/ConjugateGradientReliableUpdate.h
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Copyright (C) 2015
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Author: Christopher Kelly <ckelly@phys.columbia.edu>
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This program is free software; you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation; either version 2 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License along
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with this program; if not, write to the Free Software Foundation, Inc.,
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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See the full license in the file "LICENSE" in the top level distribution directory
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*************************************************************************************/
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/* END LEGAL */
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#ifndef GRID_CONJUGATE_GRADIENT_RELIABLE_UPDATE_H
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#define GRID_CONJUGATE_GRADIENT_RELIABLE_UPDATE_H
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namespace Grid {
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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>
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class ConjugateGradientReliableUpdate : public LinearFunction<FieldD> {
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public:
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bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
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// Defaults true.
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RealD Tolerance;
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Integer MaxIterations;
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Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
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Integer ReliableUpdatesPerformed;
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bool DoFinalCleanup; //Final DP cleanup, defaults to true
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Integer IterationsToCleanup; //Final DP cleanup step iterations
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LinearOperatorBase<FieldF> &Linop_f;
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LinearOperatorBase<FieldD> &Linop_d;
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GridBase* SinglePrecGrid;
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RealD Delta; //reliable update parameter
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//Optional ability to switch to a different linear operator once the tolerance reaches a certain point. Useful for single/half -> single/single
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LinearOperatorBase<FieldF> *Linop_fallback;
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RealD fallback_transition_tol;
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ConjugateGradientReliableUpdate(RealD tol, Integer maxit, RealD _delta, GridBase* _sp_grid, LinearOperatorBase<FieldF> &_Linop_f, LinearOperatorBase<FieldD> &_Linop_d, bool err_on_no_conv = true)
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: Tolerance(tol),
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MaxIterations(maxit),
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Delta(_delta),
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Linop_f(_Linop_f),
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Linop_d(_Linop_d),
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SinglePrecGrid(_sp_grid),
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ErrorOnNoConverge(err_on_no_conv),
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DoFinalCleanup(true),
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Linop_fallback(NULL)
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{};
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void setFallbackLinop(LinearOperatorBase<FieldF> &_Linop_fallback, const RealD _fallback_transition_tol){
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Linop_fallback = &_Linop_fallback;
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fallback_transition_tol = _fallback_transition_tol;
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}
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void operator()(const FieldD &src, FieldD &psi) {
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LinearOperatorBase<FieldF> *Linop_f_use = &Linop_f;
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bool using_fallback = false;
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psi.checkerboard = src.checkerboard;
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conformable(psi, src);
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RealD cp, c, a, d, b, ssq, qq, b_pred;
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FieldD p(src);
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FieldD mmp(src);
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FieldD r(src);
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// Initial residual computation & set up
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RealD guess = norm2(psi);
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assert(std::isnan(guess) == 0);
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Linop_d.HermOpAndNorm(psi, mmp, d, b);
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r = src - mmp;
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p = r;
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a = norm2(p);
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cp = a;
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ssq = norm2(src);
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: guess " << guess << std::endl;
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: src " << ssq << std::endl;
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: mp " << d << std::endl;
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: mmp " << b << std::endl;
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: cp,r " << cp << std::endl;
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std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: p " << a << std::endl;
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RealD rsq = Tolerance * Tolerance * ssq;
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// Check if guess is really REALLY good :)
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if (cp <= rsq) {
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate guess was REALLY good\n";
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std::cout << GridLogMessage << "\tComputed residual " << sqrt(cp / ssq)<<std::endl;
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return;
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}
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//Single prec initialization
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FieldF r_f(SinglePrecGrid);
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r_f.checkerboard = r.checkerboard;
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precisionChange(r_f, r);
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FieldF psi_f(r_f);
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psi_f = zero;
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FieldF p_f(r_f);
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FieldF mmp_f(r_f);
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RealD MaxResidSinceLastRelUp = cp; //initial residual
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std::cout << GridLogIterative << std::setprecision(4)
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<< "ConjugateGradient: k=0 residual " << cp << " target " << rsq << std::endl;
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GridStopWatch LinalgTimer;
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GridStopWatch MatrixTimer;
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GridStopWatch SolverTimer;
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SolverTimer.Start();
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int k = 0;
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int l = 0;
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for (k = 1; k <= MaxIterations; k++) {
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c = cp;
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MatrixTimer.Start();
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Linop_f_use->HermOpAndNorm(p_f, mmp_f, d, qq);
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MatrixTimer.Stop();
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LinalgTimer.Start();
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a = c / d;
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b_pred = a * (a * qq - d) / c;
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cp = axpy_norm(r_f, -a, mmp_f, r_f);
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b = cp / c;
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// Fuse these loops ; should be really easy
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psi_f = a * p_f + psi_f;
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//p_f = p_f * b + r_f;
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LinalgTimer.Stop();
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std::cout << GridLogIterative << "ConjugateGradientReliableUpdate: Iteration " << k
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<< " residual " << cp << " target " << rsq << std::endl;
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std::cout << GridLogDebug << "a = "<< a << " b_pred = "<< b_pred << " b = "<< b << std::endl;
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std::cout << GridLogDebug << "qq = "<< qq << " d = "<< d << " c = "<< c << std::endl;
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if(cp > MaxResidSinceLastRelUp){
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std::cout << GridLogIterative << "ConjugateGradientReliableUpdate: updating MaxResidSinceLastRelUp : " << MaxResidSinceLastRelUp << " -> " << cp << std::endl;
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MaxResidSinceLastRelUp = cp;
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}
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// Stopping condition
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if (cp <= rsq) {
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//Although not written in the paper, I assume that I have to add on the final solution
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precisionChange(mmp, psi_f);
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psi = psi + mmp;
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SolverTimer.Stop();
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Linop_d.HermOpAndNorm(psi, mmp, d, qq);
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p = mmp - src;
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RealD srcnorm = sqrt(norm2(src));
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RealD resnorm = sqrt(norm2(p));
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RealD true_residual = resnorm / srcnorm;
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate Converged on iteration " << k << " after " << l << " reliable updates" << std::endl;
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std::cout << GridLogMessage << "\tComputed residual " << sqrt(cp / ssq)<<std::endl;
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std::cout << GridLogMessage << "\tTrue residual " << true_residual<<std::endl;
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std::cout << GridLogMessage << "\tTarget " << Tolerance << std::endl;
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std::cout << GridLogMessage << "Time breakdown "<<std::endl;
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std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
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std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
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std::cout << GridLogMessage << "\tLinalg " << LinalgTimer.Elapsed() <<std::endl;
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IterationsToComplete = k;
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ReliableUpdatesPerformed = l;
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if(DoFinalCleanup){
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//Do a final CG to cleanup
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate performing final cleanup.\n";
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ConjugateGradient<FieldD> CG(Tolerance,MaxIterations);
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CG.ErrorOnNoConverge = ErrorOnNoConverge;
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CG(Linop_d,src,psi);
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IterationsToCleanup = CG.IterationsToComplete;
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}
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else if (ErrorOnNoConverge) assert(true_residual / Tolerance < 10000.0);
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate complete.\n";
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return;
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}
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else if(cp < Delta * MaxResidSinceLastRelUp) { //reliable update
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate "
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<< cp << "(residual) < " << Delta << "(Delta) * " << MaxResidSinceLastRelUp << "(MaxResidSinceLastRelUp) on iteration " << k << " : performing reliable update\n";
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precisionChange(mmp, psi_f);
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psi = psi + mmp;
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Linop_d.HermOpAndNorm(psi, mmp, d, qq);
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r = src - mmp;
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psi_f = zero;
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precisionChange(r_f, r);
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cp = norm2(r);
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MaxResidSinceLastRelUp = cp;
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b = cp/c;
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate new residual " << cp << std::endl;
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l = l+1;
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}
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p_f = p_f * b + r_f; //update search vector after reliable update appears to help convergence
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if(!using_fallback && Linop_fallback != NULL && cp < fallback_transition_tol){
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate switching to fallback linear operator on iteration " << k << " at residual " << cp << std::endl;
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Linop_f_use = Linop_fallback;
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using_fallback = true;
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}
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}
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std::cout << GridLogMessage << "ConjugateGradientReliableUpdate did NOT converge"
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<< std::endl;
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if (ErrorOnNoConverge) assert(0);
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IterationsToComplete = k;
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ReliableUpdatesPerformed = l;
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}
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};
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};
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#endif
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