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38 * Implements the BiasState class.
40 * \author Viveca Lindahl
41 * \author Berk Hess <hess@kth.se>
47 #include "biasstate.h"
58 #include "gromacs/fileio/gmxfio.h"
59 #include "gromacs/fileio/xvgr.h"
60 #include "gromacs/gmxlib/network.h"
61 #include "gromacs/math/utilities.h"
62 #include "gromacs/mdrunutility/multisim.h"
63 #include "gromacs/mdtypes/awh_history.h"
64 #include "gromacs/mdtypes/awh_params.h"
65 #include "gromacs/mdtypes/commrec.h"
66 #include "gromacs/simd/simd.h"
67 #include "gromacs/simd/simd_math.h"
68 #include "gromacs/utility/arrayref.h"
69 #include "gromacs/utility/exceptions.h"
70 #include "gromacs/utility/gmxassert.h"
71 #include "gromacs/utility/smalloc.h"
72 #include "gromacs/utility/stringutil.h"
75 #include "pointstate.h"
80 void BiasState::getPmf(gmx::ArrayRef<float> pmf) const
82 GMX_ASSERT(pmf.size() == points_.size(), "pmf should have the size of the bias grid");
84 /* The PMF is just the negative of the log of the sampled PMF histogram.
85 * Points with zero target weight are ignored, they will mostly contain noise.
87 for (size_t i = 0; i < points_.size(); i++)
89 pmf[i] = points_[i].inTargetRegion() ? -points_[i].logPmfSum() : GMX_FLOAT_MAX;
97 * Sum an array over all simulations on the master rank of each simulation.
99 * \param[in,out] arrayRef The data to sum.
100 * \param[in] multiSimComm Struct for multi-simulation communication.
102 void sumOverSimulations(gmx::ArrayRef<int> arrayRef, const gmx_multisim_t* multiSimComm)
104 gmx_sumi_sim(arrayRef.size(), arrayRef.data(), multiSimComm);
108 * Sum an array over all simulations on the master rank of each simulation.
110 * \param[in,out] arrayRef The data to sum.
111 * \param[in] multiSimComm Struct for multi-simulation communication.
113 void sumOverSimulations(gmx::ArrayRef<double> arrayRef, const gmx_multisim_t* multiSimComm)
115 gmx_sumd_sim(arrayRef.size(), arrayRef.data(), multiSimComm);
119 * Sum an array over all simulations on all ranks of each simulation.
121 * This assumes the data is identical on all ranks within each simulation.
123 * \param[in,out] arrayRef The data to sum.
124 * \param[in] commRecord Struct for intra-simulation communication.
125 * \param[in] multiSimComm Struct for multi-simulation communication.
128 void sumOverSimulations(gmx::ArrayRef<T> arrayRef, const t_commrec* commRecord, const gmx_multisim_t* multiSimComm)
130 if (MASTER(commRecord))
132 sumOverSimulations(arrayRef, multiSimComm);
134 if (commRecord->nnodes > 1)
136 gmx_bcast(arrayRef.size() * sizeof(T), arrayRef.data(), commRecord->mpi_comm_mygroup);
141 * Sum PMF over multiple simulations, when requested.
143 * \param[in,out] pointState The state of the points in the bias.
144 * \param[in] numSharedUpdate The number of biases sharing the histogram.
145 * \param[in] commRecord Struct for intra-simulation communication.
146 * \param[in] multiSimComm Struct for multi-simulation communication.
148 void sumPmf(gmx::ArrayRef<PointState> pointState,
150 const t_commrec* commRecord,
151 const gmx_multisim_t* multiSimComm)
153 if (numSharedUpdate == 1)
157 GMX_ASSERT(multiSimComm != nullptr && numSharedUpdate % multiSimComm->numSimulations_ == 0,
158 "numSharedUpdate should be a multiple of multiSimComm->numSimulations_");
159 GMX_ASSERT(numSharedUpdate == multiSimComm->numSimulations_,
160 "Sharing within a simulation is not implemented (yet)");
162 std::vector<double> buffer(pointState.size());
164 /* Need to temporarily exponentiate the log weights to sum over simulations */
165 for (size_t i = 0; i < buffer.size(); i++)
167 buffer[i] = pointState[i].inTargetRegion() ? std::exp(-pointState[i].logPmfSum()) : 0;
170 sumOverSimulations(gmx::ArrayRef<double>(buffer), commRecord, multiSimComm);
172 /* Take log again to get (non-normalized) PMF */
173 double normFac = 1.0 / numSharedUpdate;
174 for (gmx::index i = 0; i < pointState.ssize(); i++)
176 if (pointState[i].inTargetRegion())
178 pointState[i].setLogPmfSum(-std::log(buffer[i] * normFac));
184 * Find the minimum free energy value.
186 * \param[in] pointState The state of the points.
187 * \returns the minimum free energy value.
189 double freeEnergyMinimumValue(gmx::ArrayRef<const PointState> pointState)
191 double fMin = GMX_FLOAT_MAX;
193 for (auto const& ps : pointState)
195 if (ps.inTargetRegion() && ps.freeEnergy() < fMin)
197 fMin = ps.freeEnergy();
205 * Find and return the log of the probability weight of a point given a coordinate value.
207 * The unnormalized weight is given by
208 * w(point|value) = exp(bias(point) - U(value,point)),
209 * where U is a harmonic umbrella potential.
211 * \param[in] dimParams The bias dimensions parameters
212 * \param[in] points The point state.
213 * \param[in] grid The grid.
214 * \param[in] pointIndex Point to evaluate probability weight for.
215 * \param[in] pointBias Bias for the point (as a log weight).
216 * \param[in] value Coordinate value.
217 * \param[in] neighborLambdaEnergies The energy of the system in neighboring lambdas states. Can be
218 * empty when there are no free energy lambda state dimensions.
219 * \param[in] gridpointIndex The index of the current grid point.
220 * \returns the log of the biased probability weight.
222 double biasedLogWeightFromPoint(const std::vector<DimParams>& dimParams,
223 const std::vector<PointState>& points,
224 const BiasGrid& grid,
227 const awh_dvec value,
228 gmx::ArrayRef<const double> neighborLambdaEnergies,
231 double logWeight = detail::c_largeNegativeExponent;
233 /* Only points in the target region have non-zero weight */
234 if (points[pointIndex].inTargetRegion())
236 logWeight = pointBias;
238 /* Add potential for all parameter dimensions */
239 for (size_t d = 0; d < dimParams.size(); d++)
241 if (dimParams[d].isFepLambdaDimension())
243 /* If this is not a sampling step or if this function is called from
244 * calcConvolvedBias(), when writing energy subblocks, neighborLambdaEnergies will
245 * be empty. No convolution is required along the lambda dimension. */
246 if (!neighborLambdaEnergies.empty())
248 const int pointLambdaIndex = grid.point(pointIndex).coordValue[d];
249 const int gridpointLambdaIndex = grid.point(gridpointIndex).coordValue[d];
250 logWeight -= dimParams[d].fepDimParams().beta
251 * (neighborLambdaEnergies[pointLambdaIndex]
252 - neighborLambdaEnergies[gridpointLambdaIndex]);
257 double dev = getDeviationFromPointAlongGridAxis(grid, d, pointIndex, value[d]);
258 logWeight -= 0.5 * dimParams[d].pullDimParams().betak * dev * dev;
266 * Calculates the marginal distribution (marginal probability) for each value along
267 * a free energy lambda axis.
268 * The marginal distribution of one coordinate dimension value is the sum of the probability
269 * distribution of all values (herein all neighbor values) with the same value in the dimension
271 * \param[in] grid The bias grid.
272 * \param[in] neighbors The points to use for the calculation of the marginal distribution.
273 * \param[in] probWeightNeighbor Probability weights of the neighbors.
274 * \returns The calculated marginal distribution in a 1D array with
275 * as many elements as there are points along the axis of interest.
277 std::vector<double> calculateFELambdaMarginalDistribution(const BiasGrid& grid,
278 gmx::ArrayRef<const int> neighbors,
279 gmx::ArrayRef<const double> probWeightNeighbor)
281 const std::optional<int> lambdaAxisIndex = grid.lambdaAxisIndex();
282 GMX_RELEASE_ASSERT(lambdaAxisIndex,
283 "There must be a free energy lambda axis in order to calculate the free "
284 "energy lambda marginal distribution.");
285 const int numFepLambdaStates = grid.numFepLambdaStates();
286 std::vector<double> lambdaMarginalDistribution(numFepLambdaStates, 0);
288 for (size_t i = 0; i < neighbors.size(); i++)
290 const int neighbor = neighbors[i];
291 const int lambdaState = grid.point(neighbor).coordValue[lambdaAxisIndex.value()];
292 lambdaMarginalDistribution[lambdaState] += probWeightNeighbor[i];
294 return lambdaMarginalDistribution;
299 void BiasState::calcConvolvedPmf(const std::vector<DimParams>& dimParams,
300 const BiasGrid& grid,
301 std::vector<float>* convolvedPmf) const
303 size_t numPoints = grid.numPoints();
305 convolvedPmf->resize(numPoints);
307 /* Get the PMF to convolve. */
308 std::vector<float> pmf(numPoints);
311 for (size_t m = 0; m < numPoints; m++)
313 double freeEnergyWeights = 0;
314 const GridPoint& point = grid.point(m);
315 for (auto& neighbor : point.neighbor)
317 /* Do not convolve the bias along a lambda axis - only use the pmf from the current point */
318 if (!pointsHaveDifferentLambda(grid, m, neighbor))
320 /* The negative PMF is a positive bias. */
321 double biasNeighbor = -pmf[neighbor];
323 /* Add the convolved PMF weights for the neighbors of this point.
324 Note that this function only adds point within the target > 0 region.
325 Sum weights, take the logarithm last to get the free energy. */
326 double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
327 biasNeighbor, point.coordValue, {}, m);
328 freeEnergyWeights += std::exp(logWeight);
332 GMX_RELEASE_ASSERT(freeEnergyWeights > 0,
333 "Attempting to do log(<= 0) in AWH convolved PMF calculation.");
334 (*convolvedPmf)[m] = -std::log(static_cast<float>(freeEnergyWeights));
342 * Updates the target distribution for all points.
344 * The target distribution is always updated for all points
347 * \param[in,out] pointState The state of all points.
348 * \param[in] params The bias parameters.
350 void updateTargetDistribution(gmx::ArrayRef<PointState> pointState, const BiasParams& params)
352 double freeEnergyCutoff = 0;
353 if (params.eTarget == eawhtargetCUTOFF)
355 freeEnergyCutoff = freeEnergyMinimumValue(pointState) + params.freeEnergyCutoffInKT;
358 double sumTarget = 0;
359 for (PointState& ps : pointState)
361 sumTarget += ps.updateTargetWeight(params, freeEnergyCutoff);
363 GMX_RELEASE_ASSERT(sumTarget > 0, "We should have a non-zero distribution");
366 double invSum = 1.0 / sumTarget;
367 for (PointState& ps : pointState)
369 ps.scaleTarget(invSum);
374 * Puts together a string describing a grid point.
376 * \param[in] grid The grid.
377 * \param[in] point BiasGrid point index.
378 * \returns a string for the point.
380 std::string gridPointValueString(const BiasGrid& grid, int point)
382 std::string pointString;
386 for (int d = 0; d < grid.numDimensions(); d++)
388 pointString += gmx::formatString("%g", grid.point(point).coordValue[d]);
389 if (d < grid.numDimensions() - 1)
404 int BiasState::warnForHistogramAnomalies(const BiasGrid& grid, int biasIndex, double t, FILE* fplog, int maxNumWarnings) const
406 GMX_ASSERT(fplog != nullptr, "Warnings can only be issued if there is log file.");
407 const double maxHistogramRatio = 0.5; /* Tolerance for printing a warning about the histogram ratios */
409 /* Sum up the histograms and get their normalization */
410 double sumVisits = 0;
411 double sumWeights = 0;
412 for (auto& pointState : points_)
414 if (pointState.inTargetRegion())
416 sumVisits += pointState.numVisitsTot();
417 sumWeights += pointState.weightSumTot();
420 GMX_RELEASE_ASSERT(sumVisits > 0, "We should have visits");
421 GMX_RELEASE_ASSERT(sumWeights > 0, "We should have weight");
422 double invNormVisits = 1.0 / sumVisits;
423 double invNormWeight = 1.0 / sumWeights;
425 /* Check all points for warnings */
427 size_t numPoints = grid.numPoints();
428 for (size_t m = 0; m < numPoints; m++)
430 /* Skip points close to boundary or non-target region */
431 const GridPoint& gridPoint = grid.point(m);
432 bool skipPoint = false;
433 for (size_t n = 0; (n < gridPoint.neighbor.size()) && !skipPoint; n++)
435 int neighbor = gridPoint.neighbor[n];
436 skipPoint = !points_[neighbor].inTargetRegion();
437 for (int d = 0; (d < grid.numDimensions()) && !skipPoint; d++)
439 const GridPoint& neighborPoint = grid.point(neighbor);
440 skipPoint = neighborPoint.index[d] == 0
441 || neighborPoint.index[d] == grid.axis(d).numPoints() - 1;
445 /* Warn if the coordinate distribution is less than the target distribution with a certain fraction somewhere */
446 const double relativeWeight = points_[m].weightSumTot() * invNormWeight;
447 const double relativeVisits = points_[m].numVisitsTot() * invNormVisits;
448 if (!skipPoint && relativeVisits < relativeWeight * maxHistogramRatio)
450 std::string pointValueString = gridPointValueString(grid, m);
451 std::string warningMessage = gmx::formatString(
453 "at t = %g ps the obtained coordinate distribution at coordinate value %s "
454 "is less than a fraction %g of the reference distribution at that point. "
455 "If you are not certain about your settings you might want to increase your "
456 "pull force constant or "
457 "modify your sampling region.\n",
458 biasIndex + 1, t, pointValueString.c_str(), maxHistogramRatio);
459 gmx::TextLineWrapper wrapper;
460 wrapper.settings().setLineLength(c_linewidth);
461 fprintf(fplog, "%s", wrapper.wrapToString(warningMessage).c_str());
465 if (numWarnings >= maxNumWarnings)
474 double BiasState::calcUmbrellaForceAndPotential(const std::vector<DimParams>& dimParams,
475 const BiasGrid& grid,
477 ArrayRef<const double> neighborLambdaDhdl,
478 gmx::ArrayRef<double> force) const
480 double potential = 0;
481 for (size_t d = 0; d < dimParams.size(); d++)
483 if (dimParams[d].isFepLambdaDimension())
485 if (!neighborLambdaDhdl.empty())
487 const int coordpointLambdaIndex = grid.point(point).coordValue[d];
488 force[d] = neighborLambdaDhdl[coordpointLambdaIndex];
489 /* The potential should not be affected by the lambda dimension. */
495 getDeviationFromPointAlongGridAxis(grid, d, point, coordState_.coordValue()[d]);
496 double k = dimParams[d].pullDimParams().k;
498 /* Force from harmonic potential 0.5*k*dev^2 */
499 force[d] = -k * deviation;
500 potential += 0.5 * k * deviation * deviation;
507 void BiasState::calcConvolvedForce(const std::vector<DimParams>& dimParams,
508 const BiasGrid& grid,
509 gmx::ArrayRef<const double> probWeightNeighbor,
510 ArrayRef<const double> neighborLambdaDhdl,
511 gmx::ArrayRef<double> forceWorkBuffer,
512 gmx::ArrayRef<double> force) const
514 for (size_t d = 0; d < dimParams.size(); d++)
519 /* Only neighboring points have non-negligible contribution. */
520 const std::vector<int>& neighbor = grid.point(coordState_.gridpointIndex()).neighbor;
521 gmx::ArrayRef<double> forceFromNeighbor = forceWorkBuffer;
522 for (size_t n = 0; n < neighbor.size(); n++)
524 double weightNeighbor = probWeightNeighbor[n];
525 int indexNeighbor = neighbor[n];
527 /* Get the umbrella force from this point. The returned potential is ignored here. */
528 calcUmbrellaForceAndPotential(dimParams, grid, indexNeighbor, neighborLambdaDhdl, forceFromNeighbor);
530 /* Add the weighted umbrella force to the convolved force. */
531 for (size_t d = 0; d < dimParams.size(); d++)
533 force[d] += forceFromNeighbor[d] * weightNeighbor;
538 double BiasState::moveUmbrella(const std::vector<DimParams>& dimParams,
539 const BiasGrid& grid,
540 gmx::ArrayRef<const double> probWeightNeighbor,
541 ArrayRef<const double> neighborLambdaDhdl,
542 gmx::ArrayRef<double> biasForce,
546 bool onlySampleUmbrellaGridpoint)
548 /* Generate and set a new coordinate reference value */
549 coordState_.sampleUmbrellaGridpoint(grid, coordState_.gridpointIndex(), probWeightNeighbor,
550 step, seed, indexSeed);
552 if (onlySampleUmbrellaGridpoint)
557 std::vector<double> newForce(dimParams.size());
558 double newPotential = calcUmbrellaForceAndPotential(
559 dimParams, grid, coordState_.umbrellaGridpoint(), neighborLambdaDhdl, newForce);
561 /* A modification of the reference value at time t will lead to a different
562 force over t-dt/2 to t and over t to t+dt/2. For high switching rates
563 this means the force and velocity will change signs roughly as often.
564 To avoid any issues we take the average of the previous and new force
565 at steps when the reference value has been moved. E.g. if the ref. value
566 is set every step to (coord dvalue +/- delta) would give zero force.
568 for (gmx::index d = 0; d < biasForce.ssize(); d++)
570 /* Average of the current and new force */
571 biasForce[d] = 0.5 * (biasForce[d] + newForce[d]);
581 * Sets the histogram rescaling factors needed to control the histogram size.
583 * For sake of robustness, the reference weight histogram can grow at a rate
584 * different from the actual sampling rate. Typically this happens for a limited
585 * initial time, alternatively growth is scaled down by a constant factor for all
586 * times. Since the size of the reference histogram sets the size of the free
587 * energy update this should be reflected also in the PMF. Thus the PMF histogram
588 * needs to be rescaled too.
590 * This function should only be called by the bias update function or wrapped by a function that
591 * knows what scale factors should be applied when, e.g,
592 * getSkippedUpdateHistogramScaleFactors().
594 * \param[in] params The bias parameters.
595 * \param[in] newHistogramSize New reference weight histogram size.
596 * \param[in] oldHistogramSize Previous reference weight histogram size (before adding new samples).
597 * \param[out] weightHistScaling Scaling factor for the reference weight histogram.
598 * \param[out] logPmfSumScaling Log of the scaling factor for the PMF histogram.
600 void setHistogramUpdateScaleFactors(const BiasParams& params,
601 double newHistogramSize,
602 double oldHistogramSize,
603 double* weightHistScaling,
604 double* logPmfSumScaling)
607 /* The two scaling factors below are slightly different (ignoring the log factor) because the
608 reference and the PMF histogram apply weight scaling differently. The weight histogram
609 applies is locally, i.e. each sample is scaled down meaning all samples get equal weight.
610 It is done this way because that is what target type local Boltzmann (for which
611 target = weight histogram) needs. In contrast, the PMF histogram is rescaled globally
612 by repeatedly scaling down the whole histogram. The reasons for doing it this way are:
613 1) empirically this is necessary for converging the PMF; 2) since the extraction of
614 the PMF is theoretically only valid for a constant bias, new samples should get more
615 weight than old ones for which the bias is fluctuating more. */
617 newHistogramSize / (oldHistogramSize + params.updateWeight * params.localWeightScaling);
618 *logPmfSumScaling = std::log(newHistogramSize / (oldHistogramSize + params.updateWeight));
623 void BiasState::getSkippedUpdateHistogramScaleFactors(const BiasParams& params,
624 double* weightHistScaling,
625 double* logPmfSumScaling) const
627 GMX_ASSERT(params.skipUpdates(),
628 "Calling function for skipped updates when skipping updates is not allowed");
630 if (inInitialStage())
632 /* In between global updates the reference histogram size is kept constant so we trivially
633 know what the histogram size was at the time of the skipped update. */
634 double histogramSize = histogramSize_.histogramSize();
635 setHistogramUpdateScaleFactors(params, histogramSize, histogramSize, weightHistScaling,
640 /* In the final stage, the reference histogram grows at the sampling rate which gives trivial scale factors. */
641 *weightHistScaling = 1;
642 *logPmfSumScaling = 0;
646 void BiasState::doSkippedUpdatesForAllPoints(const BiasParams& params)
648 double weightHistScaling;
649 double logPmfsumScaling;
651 getSkippedUpdateHistogramScaleFactors(params, &weightHistScaling, &logPmfsumScaling);
653 for (auto& pointState : points_)
655 bool didUpdate = pointState.performPreviouslySkippedUpdates(
656 params, histogramSize_.numUpdates(), weightHistScaling, logPmfsumScaling);
658 /* Update the bias for this point only if there were skipped updates in the past to avoid calculating the log unneccessarily */
661 pointState.updateBias();
666 void BiasState::doSkippedUpdatesInNeighborhood(const BiasParams& params, const BiasGrid& grid)
668 double weightHistScaling;
669 double logPmfsumScaling;
671 getSkippedUpdateHistogramScaleFactors(params, &weightHistScaling, &logPmfsumScaling);
673 /* For each neighbor point of the center point, refresh its state by adding the results of all past, skipped updates. */
674 const std::vector<int>& neighbors = grid.point(coordState_.gridpointIndex()).neighbor;
675 for (auto& neighbor : neighbors)
677 bool didUpdate = points_[neighbor].performPreviouslySkippedUpdates(
678 params, histogramSize_.numUpdates(), weightHistScaling, logPmfsumScaling);
682 points_[neighbor].updateBias();
691 * Merge update lists from multiple sharing simulations.
693 * \param[in,out] updateList Update list for this simulation (assumed >= npoints long).
694 * \param[in] numPoints Total number of points.
695 * \param[in] commRecord Struct for intra-simulation communication.
696 * \param[in] multiSimComm Struct for multi-simulation communication.
698 void mergeSharedUpdateLists(std::vector<int>* updateList,
700 const t_commrec* commRecord,
701 const gmx_multisim_t* multiSimComm)
703 std::vector<int> numUpdatesOfPoint;
705 /* Flag the update points of this sim.
706 TODO: we can probably avoid allocating this array and just use the input array. */
707 numUpdatesOfPoint.resize(numPoints, 0);
708 for (auto& pointIndex : *updateList)
710 numUpdatesOfPoint[pointIndex] = 1;
713 /* Sum over the sims to get all the flagged points */
714 sumOverSimulations(arrayRefFromArray(numUpdatesOfPoint.data(), numPoints), commRecord, multiSimComm);
716 /* Collect the indices of the flagged points in place. The resulting array will be the merged update list.*/
718 for (int m = 0; m < numPoints; m++)
720 if (numUpdatesOfPoint[m] > 0)
722 updateList->push_back(m);
728 * Generate an update list of points sampled since the last update.
730 * \param[in] grid The AWH bias.
731 * \param[in] points The point state.
732 * \param[in] originUpdatelist The origin of the rectangular region that has been sampled since
733 * last update. \param[in] endUpdatelist The end of the rectangular that has been sampled since
734 * last update. \param[in,out] updateList Local update list to set (assumed >= npoints long).
736 void makeLocalUpdateList(const BiasGrid& grid,
737 const std::vector<PointState>& points,
738 const awh_ivec originUpdatelist,
739 const awh_ivec endUpdatelist,
740 std::vector<int>* updateList)
745 /* Define the update search grid */
746 for (int d = 0; d < grid.numDimensions(); d++)
748 origin[d] = originUpdatelist[d];
749 numPoints[d] = endUpdatelist[d] - originUpdatelist[d] + 1;
751 /* Because the end_updatelist is unwrapped it can be > (npoints - 1) so that numPoints can be > npoints in grid.
752 This helps for calculating the distance/number of points but should be removed and fixed when the way of
753 updating origin/end updatelist is changed (see sampleProbabilityWeights). */
754 numPoints[d] = std::min(grid.axis(d).numPoints(), numPoints[d]);
757 /* Make the update list */
760 bool pointExists = true;
763 pointExists = advancePointInSubgrid(grid, origin, numPoints, &pointIndex);
765 if (pointExists && points[pointIndex].inTargetRegion())
767 updateList->push_back(pointIndex);
774 void BiasState::resetLocalUpdateRange(const BiasGrid& grid)
776 const int gridpointIndex = coordState_.gridpointIndex();
777 for (int d = 0; d < grid.numDimensions(); d++)
779 /* This gives the minimum range consisting only of the current closest point. */
780 originUpdatelist_[d] = grid.point(gridpointIndex).index[d];
781 endUpdatelist_[d] = grid.point(gridpointIndex).index[d];
789 * Add partial histograms (accumulating between updates) to accumulating histograms.
791 * \param[in,out] pointState The state of the points in the bias.
792 * \param[in,out] weightSumCovering The weights for checking covering.
793 * \param[in] numSharedUpdate The number of biases sharing the histrogram.
794 * \param[in] commRecord Struct for intra-simulation communication.
795 * \param[in] multiSimComm Struct for multi-simulation communication.
796 * \param[in] localUpdateList List of points with data.
798 void sumHistograms(gmx::ArrayRef<PointState> pointState,
799 gmx::ArrayRef<double> weightSumCovering,
801 const t_commrec* commRecord,
802 const gmx_multisim_t* multiSimComm,
803 const std::vector<int>& localUpdateList)
805 /* The covering checking histograms are added before summing over simulations, so that the
806 weights from different simulations are kept distinguishable. */
807 for (int globalIndex : localUpdateList)
809 weightSumCovering[globalIndex] += pointState[globalIndex].weightSumIteration();
812 /* Sum histograms over multiple simulations if needed. */
813 if (numSharedUpdate > 1)
815 GMX_ASSERT(numSharedUpdate == multiSimComm->numSimulations_,
816 "Sharing within a simulation is not implemented (yet)");
818 /* Collect the weights and counts in linear arrays to be able to use gmx_sumd_sim. */
819 std::vector<double> weightSum;
820 std::vector<double> coordVisits;
822 weightSum.resize(localUpdateList.size());
823 coordVisits.resize(localUpdateList.size());
825 for (size_t localIndex = 0; localIndex < localUpdateList.size(); localIndex++)
827 const PointState& ps = pointState[localUpdateList[localIndex]];
829 weightSum[localIndex] = ps.weightSumIteration();
830 coordVisits[localIndex] = ps.numVisitsIteration();
833 sumOverSimulations(gmx::ArrayRef<double>(weightSum), commRecord, multiSimComm);
834 sumOverSimulations(gmx::ArrayRef<double>(coordVisits), commRecord, multiSimComm);
836 /* Transfer back the result */
837 for (size_t localIndex = 0; localIndex < localUpdateList.size(); localIndex++)
839 PointState& ps = pointState[localUpdateList[localIndex]];
841 ps.setPartialWeightAndCount(weightSum[localIndex], coordVisits[localIndex]);
845 /* Now add the partial counts and weights to the accumulating histograms.
846 Note: we still need to use the weights for the update so we wait
847 with resetting them until the end of the update. */
848 for (int globalIndex : localUpdateList)
850 pointState[globalIndex].addPartialWeightAndCount();
855 * Label points along an axis as covered or not.
857 * A point is covered if it is surrounded by visited points up to a radius = coverRadius.
859 * \param[in] visited Visited? For each point.
860 * \param[in] checkCovering Check for covering? For each point.
861 * \param[in] numPoints The number of grid points along this dimension.
862 * \param[in] period Period in number of points.
863 * \param[in] coverRadius Cover radius, in points, needed for defining a point as covered.
864 * \param[in,out] covered In this array elements are 1 for covered points and 0 for
865 * non-covered points, this routine assumes that \p covered has at least size \p numPoints.
867 void labelCoveredPoints(const std::vector<bool>& visited,
868 const std::vector<bool>& checkCovering,
872 gmx::ArrayRef<int> covered)
874 GMX_ASSERT(covered.ssize() >= numPoints, "covered should be at least as large as the grid");
876 bool haveFirstNotVisited = false;
877 int firstNotVisited = -1;
878 int notVisitedLow = -1;
879 int notVisitedHigh = -1;
881 for (int n = 0; n < numPoints; n++)
883 if (checkCovering[n] && !visited[n])
885 if (!haveFirstNotVisited)
889 haveFirstNotVisited = true;
895 /* Have now an interval I = [notVisitedLow,notVisitedHigh] of visited points bounded
896 by unvisited points. The unvisted end points affect the coveredness of the
897 visited with a reach equal to the cover radius. */
898 int notCoveredLow = notVisitedLow + coverRadius;
899 int notCoveredHigh = notVisitedHigh - coverRadius;
900 for (int i = notVisitedLow; i <= notVisitedHigh; i++)
902 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
905 /* Find a new interval to set covering for. Make the notVisitedHigh of this interval
906 the notVisitedLow of the next. */
907 notVisitedLow = notVisitedHigh;
912 /* Have labelled all the internal points. Now take care of the boundary regions. */
913 if (!haveFirstNotVisited)
915 /* No non-visited points <=> all points visited => all points covered. */
917 for (int n = 0; n < numPoints; n++)
924 int lastNotVisited = notVisitedLow;
926 /* For periodic boundaries, non-visited points can influence points
927 on the other side of the boundary so we need to wrap around. */
929 /* Lower end. For periodic boundaries the last upper end not visited point becomes the low-end not visited point.
930 For non-periodic boundaries there is no lower end point so a dummy value is used. */
931 int notVisitedHigh = firstNotVisited;
932 int notVisitedLow = period > 0 ? (lastNotVisited - period) : -(coverRadius + 1);
934 int notCoveredLow = notVisitedLow + coverRadius;
935 int notCoveredHigh = notVisitedHigh - coverRadius;
937 for (int i = 0; i <= notVisitedHigh; i++)
939 /* For non-periodic boundaries notCoveredLow = -1 will impose no restriction. */
940 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
943 /* Upper end. Same as for lower end but in the other direction. */
944 notVisitedHigh = period > 0 ? (firstNotVisited + period) : (numPoints + coverRadius);
945 notVisitedLow = lastNotVisited;
947 notCoveredLow = notVisitedLow + coverRadius;
948 notCoveredHigh = notVisitedHigh - coverRadius;
950 for (int i = notVisitedLow; i <= numPoints - 1; i++)
952 /* For non-periodic boundaries notCoveredHigh = numPoints will impose no restriction. */
953 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
960 bool BiasState::isSamplingRegionCovered(const BiasParams& params,
961 const std::vector<DimParams>& dimParams,
962 const BiasGrid& grid,
963 const t_commrec* commRecord,
964 const gmx_multisim_t* multiSimComm) const
966 /* Allocate and initialize arrays: one for checking visits along each dimension,
967 one for keeping track of which points to check and one for the covered points.
968 Possibly these could be kept as AWH variables to avoid these allocations. */
971 std::vector<bool> visited;
972 std::vector<bool> checkCovering;
973 // We use int for the covering array since we might use gmx_sumi_sim.
974 std::vector<int> covered;
977 std::vector<CheckDim> checkDim;
978 checkDim.resize(grid.numDimensions());
980 for (int d = 0; d < grid.numDimensions(); d++)
982 const size_t numPoints = grid.axis(d).numPoints();
983 checkDim[d].visited.resize(numPoints, false);
984 checkDim[d].checkCovering.resize(numPoints, false);
985 checkDim[d].covered.resize(numPoints, 0);
988 /* Set visited points along each dimension and which points should be checked for covering.
989 Specifically, points above the free energy cutoff (if there is one) or points outside
990 of the target region are ignored. */
992 /* Set the free energy cutoff */
993 double maxFreeEnergy = GMX_FLOAT_MAX;
995 if (params.eTarget == eawhtargetCUTOFF)
997 maxFreeEnergy = freeEnergyMinimumValue(points_) + params.freeEnergyCutoffInKT;
1000 /* Set the threshold weight for a point to be considered visited. */
1001 double weightThreshold = 1;
1002 for (int d = 0; d < grid.numDimensions(); d++)
1004 if (grid.axis(d).isFepLambdaAxis())
1006 /* TODO: Verify that a threshold of 1.0 is OK. With a very high sample weight 1.0 can be
1007 * reached quickly even in regions with low probability. Should the sample weight be
1008 * taken into account here? */
1009 weightThreshold *= 1.0;
1013 weightThreshold *= grid.axis(d).spacing()
1014 * std::sqrt(dimParams[d].pullDimParams().betak * 0.5 * M_1_PI);
1018 /* Project the sampling weights onto each dimension */
1019 for (size_t m = 0; m < grid.numPoints(); m++)
1021 const PointState& pointState = points_[m];
1023 for (int d = 0; d < grid.numDimensions(); d++)
1025 int n = grid.point(m).index[d];
1027 /* Is visited if it was already visited or if there is enough weight at the current point */
1028 checkDim[d].visited[n] = checkDim[d].visited[n] || (weightSumCovering_[m] > weightThreshold);
1030 /* Check for covering if there is at least point in this slice that is in the target region and within the cutoff */
1031 checkDim[d].checkCovering[n] =
1032 checkDim[d].checkCovering[n]
1033 || (pointState.inTargetRegion() && pointState.freeEnergy() < maxFreeEnergy);
1037 /* Label each point along each dimension as covered or not. */
1038 for (int d = 0; d < grid.numDimensions(); d++)
1040 labelCoveredPoints(checkDim[d].visited, checkDim[d].checkCovering, grid.axis(d).numPoints(),
1041 grid.axis(d).numPointsInPeriod(), params.coverRadius()[d], checkDim[d].covered);
1044 /* Now check for global covering. Each dimension needs to be covered separately.
1045 A dimension is covered if each point is covered. Multiple simulations collectively
1046 cover the points, i.e. a point is covered if any of the simulations covered it.
1047 However, visited points are not shared, i.e. if a point is covered or not is
1048 determined by the visits of a single simulation. In general the covering criterion is
1049 all points covered => all points are surrounded by visited points up to a radius = coverRadius.
1050 For 1 simulation, all points covered <=> all points visited. For multiple simulations
1051 however, all points visited collectively !=> all points covered, except for coverRadius = 0.
1052 In the limit of large coverRadius, all points covered => all points visited by at least one
1053 simulation (since no point will be covered until all points have been visited by a
1054 single simulation). Basically coverRadius sets how much "connectedness" (or mixing) a point
1055 needs with surrounding points before sharing covering information with other simulations. */
1057 /* Communicate the covered points between sharing simulations if needed. */
1058 if (params.numSharedUpdate > 1)
1060 /* For multiple dimensions this may not be the best way to do it. */
1061 for (int d = 0; d < grid.numDimensions(); d++)
1064 gmx::arrayRefFromArray(checkDim[d].covered.data(), grid.axis(d).numPoints()),
1065 commRecord, multiSimComm);
1069 /* Now check if for each dimension all points are covered. Break if not true. */
1070 bool allPointsCovered = true;
1071 for (int d = 0; d < grid.numDimensions() && allPointsCovered; d++)
1073 for (int n = 0; n < grid.axis(d).numPoints() && allPointsCovered; n++)
1075 allPointsCovered = (checkDim[d].covered[n] != 0);
1079 return allPointsCovered;
1083 * Normalizes the free energy and PMF sum.
1085 * \param[in] pointState The state of the points.
1087 static void normalizeFreeEnergyAndPmfSum(std::vector<PointState>* pointState)
1089 double minF = freeEnergyMinimumValue(*pointState);
1091 for (PointState& ps : *pointState)
1093 ps.normalizeFreeEnergyAndPmfSum(minF);
1097 void BiasState::updateFreeEnergyAndAddSamplesToHistogram(const std::vector<DimParams>& dimParams,
1098 const BiasGrid& grid,
1099 const BiasParams& params,
1100 const t_commrec* commRecord,
1101 const gmx_multisim_t* multiSimComm,
1105 std::vector<int>* updateList)
1107 /* Note hat updateList is only used in this scope and is always
1108 * re-initialized. We do not use a local vector, because that would
1109 * cause reallocation every time this funtion is called and the vector
1110 * can be the size of the whole grid.
1113 /* Make a list of all local points, i.e. those that could have been touched since
1114 the last update. These are the points needed for summing histograms below
1115 (non-local points only add zeros). For local updates, this will also be the
1116 final update list. */
1117 makeLocalUpdateList(grid, points_, originUpdatelist_, endUpdatelist_, updateList);
1118 if (params.numSharedUpdate > 1)
1120 mergeSharedUpdateLists(updateList, points_.size(), commRecord, multiSimComm);
1123 /* Reset the range for the next update */
1124 resetLocalUpdateRange(grid);
1126 /* Add samples to histograms for all local points and sync simulations if needed */
1127 sumHistograms(points_, weightSumCovering_, params.numSharedUpdate, commRecord, multiSimComm, *updateList);
1129 sumPmf(points_, params.numSharedUpdate, commRecord, multiSimComm);
1131 /* Renormalize the free energy if values are too large. */
1132 bool needToNormalizeFreeEnergy = false;
1133 for (int& globalIndex : *updateList)
1135 /* We want to keep the absolute value of the free energies to be less
1136 c_largePositiveExponent to be able to safely pass these values to exp(). The check below
1137 ensures this as long as the free energy values grow less than 0.5*c_largePositiveExponent
1138 in a return time to this neighborhood. For reasonable update sizes it's unlikely that
1139 this requirement would be broken. */
1140 if (std::abs(points_[globalIndex].freeEnergy()) > 0.5 * detail::c_largePositiveExponent)
1142 needToNormalizeFreeEnergy = true;
1147 /* Update target distribution? */
1148 bool needToUpdateTargetDistribution =
1149 (params.eTarget != eawhtargetCONSTANT && params.isUpdateTargetStep(step));
1151 /* In the initial stage, the histogram grows dynamically as a function of the number of coverings. */
1152 bool detectedCovering = false;
1153 if (inInitialStage())
1156 (params.isCheckCoveringStep(step)
1157 && isSamplingRegionCovered(params, dimParams, grid, commRecord, multiSimComm));
1160 /* The weighthistogram size after this update. */
1161 double newHistogramSize = histogramSize_.newHistogramSize(params, t, detectedCovering, points_,
1162 weightSumCovering_, fplog);
1164 /* Make the update list. Usually we try to only update local points,
1165 * but if the update has non-trivial or non-deterministic effects
1166 * on non-local points a global update is needed. This is the case when:
1167 * 1) a covering occurred in the initial stage, leading to non-trivial
1168 * histogram rescaling factors; or
1169 * 2) the target distribution will be updated, since we don't make any
1170 * assumption on its form; or
1171 * 3) the AWH parameters are such that we never attempt to skip non-local
1173 * 4) the free energy values have grown so large that a renormalization
1176 if (needToUpdateTargetDistribution || detectedCovering || !params.skipUpdates() || needToNormalizeFreeEnergy)
1178 /* Global update, just add all points. */
1179 updateList->clear();
1180 for (size_t m = 0; m < points_.size(); m++)
1182 if (points_[m].inTargetRegion())
1184 updateList->push_back(m);
1189 /* Set histogram scale factors. */
1190 double weightHistScalingSkipped = 0;
1191 double logPmfsumScalingSkipped = 0;
1192 if (params.skipUpdates())
1194 getSkippedUpdateHistogramScaleFactors(params, &weightHistScalingSkipped, &logPmfsumScalingSkipped);
1196 double weightHistScalingNew;
1197 double logPmfsumScalingNew;
1198 setHistogramUpdateScaleFactors(params, newHistogramSize, histogramSize_.histogramSize(),
1199 &weightHistScalingNew, &logPmfsumScalingNew);
1201 /* Update free energy and reference weight histogram for points in the update list. */
1202 for (int pointIndex : *updateList)
1204 PointState* pointStateToUpdate = &points_[pointIndex];
1206 /* Do updates from previous update steps that were skipped because this point was at that time non-local. */
1207 if (params.skipUpdates())
1209 pointStateToUpdate->performPreviouslySkippedUpdates(params, histogramSize_.numUpdates(),
1210 weightHistScalingSkipped,
1211 logPmfsumScalingSkipped);
1214 /* Now do an update with new sampling data. */
1215 pointStateToUpdate->updateWithNewSampling(params, histogramSize_.numUpdates(),
1216 weightHistScalingNew, logPmfsumScalingNew);
1219 /* Only update the histogram size after we are done with the local point updates */
1220 histogramSize_.setHistogramSize(newHistogramSize, weightHistScalingNew);
1222 if (needToNormalizeFreeEnergy)
1224 normalizeFreeEnergyAndPmfSum(&points_);
1227 if (needToUpdateTargetDistribution)
1229 /* The target distribution is always updated for all points at once. */
1230 updateTargetDistribution(points_, params);
1233 /* Update the bias. The bias is updated separately and last since it simply a function of
1234 the free energy and the target distribution and we want to avoid doing extra work. */
1235 for (int pointIndex : *updateList)
1237 points_[pointIndex].updateBias();
1240 /* Increase the update counter. */
1241 histogramSize_.incrementNumUpdates();
1244 double BiasState::updateProbabilityWeightsAndConvolvedBias(const std::vector<DimParams>& dimParams,
1245 const BiasGrid& grid,
1246 gmx::ArrayRef<const double> neighborLambdaEnergies,
1247 std::vector<double, AlignedAllocator<double>>* weight) const
1249 /* Only neighbors of the current coordinate value will have a non-negligible chance of getting sampled */
1250 const std::vector<int>& neighbors = grid.point(coordState_.gridpointIndex()).neighbor;
1252 #if GMX_SIMD_HAVE_DOUBLE
1253 typedef SimdDouble PackType;
1254 constexpr int packSize = GMX_SIMD_DOUBLE_WIDTH;
1256 typedef double PackType;
1257 constexpr int packSize = 1;
1259 /* Round the size of the weight array up to packSize */
1260 const int weightSize = ((neighbors.size() + packSize - 1) / packSize) * packSize;
1261 weight->resize(weightSize);
1263 double* gmx_restrict weightData = weight->data();
1264 PackType weightSumPack(0.0);
1265 for (size_t i = 0; i < neighbors.size(); i += packSize)
1267 for (size_t n = i; n < i + packSize; n++)
1269 if (n < neighbors.size())
1271 const int neighbor = neighbors[n];
1272 (*weight)[n] = biasedLogWeightFromPoint(
1273 dimParams, points_, grid, neighbor, points_[neighbor].bias(),
1274 coordState_.coordValue(), neighborLambdaEnergies, coordState_.gridpointIndex());
1278 /* Pad with values that don't affect the result */
1279 (*weight)[n] = detail::c_largeNegativeExponent;
1282 PackType weightPack = load<PackType>(weightData + i);
1283 weightPack = gmx::exp(weightPack);
1284 weightSumPack = weightSumPack + weightPack;
1285 store(weightData + i, weightPack);
1287 /* Sum of probability weights */
1288 double weightSum = reduce(weightSumPack);
1289 GMX_RELEASE_ASSERT(weightSum > 0,
1290 "zero probability weight when updating AWH probability weights.");
1292 /* Normalize probabilities to sum to 1 */
1293 double invWeightSum = 1 / weightSum;
1295 /* When there is a free energy lambda state axis remove the convolved contributions along that
1296 * axis from the total bias. This must be done after calculating invWeightSum (since weightSum
1297 * will be modified), but before normalizing the weights (below). */
1298 if (grid.hasLambdaAxis())
1300 /* If there is only one axis the bias will not be convolved in any dimension. */
1301 if (grid.axis().size() == 1)
1303 weightSum = gmx::exp(points_[coordState_.gridpointIndex()].bias());
1307 for (size_t i = 0; i < neighbors.size(); i++)
1309 const int neighbor = neighbors[i];
1310 if (pointsHaveDifferentLambda(grid, coordState_.gridpointIndex(), neighbor))
1312 weightSum -= weightData[i];
1318 for (double& w : *weight)
1323 /* Return the convolved bias */
1324 return std::log(weightSum);
1327 double BiasState::calcConvolvedBias(const std::vector<DimParams>& dimParams,
1328 const BiasGrid& grid,
1329 const awh_dvec& coordValue) const
1331 int point = grid.nearestIndex(coordValue);
1332 const GridPoint& gridPoint = grid.point(point);
1334 /* Sum the probability weights from the neighborhood of the given point */
1335 double weightSum = 0;
1336 for (int neighbor : gridPoint.neighbor)
1338 /* No convolution is required along the lambda dimension. */
1339 if (pointsHaveDifferentLambda(grid, point, neighbor))
1343 double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
1344 points_[neighbor].bias(), coordValue, {}, point);
1345 weightSum += std::exp(logWeight);
1348 /* Returns -GMX_FLOAT_MAX if no neighboring points were in the target region. */
1349 return (weightSum > 0) ? std::log(weightSum) : -GMX_FLOAT_MAX;
1352 void BiasState::sampleProbabilityWeights(const BiasGrid& grid, gmx::ArrayRef<const double> probWeightNeighbor)
1354 const std::vector<int>& neighbor = grid.point(coordState_.gridpointIndex()).neighbor;
1356 /* Save weights for next update */
1357 for (size_t n = 0; n < neighbor.size(); n++)
1359 points_[neighbor[n]].increaseWeightSumIteration(probWeightNeighbor[n]);
1362 /* Update the local update range. Two corner points define this rectangular
1363 * domain. We need to choose two new corner points such that the new domain
1364 * contains both the old update range and the current neighborhood.
1365 * In the simplest case when an update is performed every sample,
1366 * the update range would simply equal the current neighborhood.
1368 int neighborStart = neighbor[0];
1369 int neighborLast = neighbor[neighbor.size() - 1];
1370 for (int d = 0; d < grid.numDimensions(); d++)
1372 int origin = grid.point(neighborStart).index[d];
1373 int last = grid.point(neighborLast).index[d];
1377 /* Unwrap if wrapped around the boundary (only happens for periodic
1378 * boundaries). This has been already for the stored index interval.
1380 /* TODO: what we want to do is to find the smallest the update
1381 * interval that contains all points that need to be updated.
1382 * This amounts to combining two intervals, the current
1383 * [origin, end] update interval and the new touched neighborhood
1384 * into a new interval that contains all points from both the old
1387 * For periodic boundaries it becomes slightly more complicated
1388 * than for closed boundaries because then it needs not be
1389 * true that origin < end (so one can't simply relate the origin/end
1390 * in the min()/max() below). The strategy here is to choose the
1391 * origin closest to a reference point (index 0) and then unwrap
1392 * the end index if needed and choose the largest end index.
1393 * This ensures that both intervals are in the new interval
1394 * but it's not necessarily the smallest.
1395 * Currently we solve this by going through each possibility
1396 * and checking them.
1398 last += grid.axis(d).numPointsInPeriod();
1401 originUpdatelist_[d] = std::min(originUpdatelist_[d], origin);
1402 endUpdatelist_[d] = std::max(endUpdatelist_[d], last);
1406 void BiasState::sampleCoordAndPmf(const std::vector<DimParams>& dimParams,
1407 const BiasGrid& grid,
1408 gmx::ArrayRef<const double> probWeightNeighbor,
1409 double convolvedBias)
1411 /* Sampling-based deconvolution extracting the PMF.
1412 * Update the PMF histogram with the current coordinate value.
1414 * Because of the finite width of the harmonic potential, the free energy
1415 * defined for each coordinate point does not exactly equal that of the
1416 * actual coordinate, the PMF. However, the PMF can be estimated by applying
1417 * the relation exp(-PMF) = exp(-bias_convolved)*P_biased/Z, i.e. by keeping a
1418 * reweighted histogram of the coordinate value. Strictly, this relies on
1419 * the unknown normalization constant Z being either constant or known. Here,
1420 * neither is true except in the long simulation time limit. Empirically however,
1421 * it works (mainly because how the PMF histogram is rescaled).
1424 const int gridPointIndex = coordState_.gridpointIndex();
1425 const std::optional<int> lambdaAxisIndex = grid.lambdaAxisIndex();
1427 /* Update the PMF of points along a lambda axis with their bias. */
1428 if (lambdaAxisIndex)
1430 const std::vector<int>& neighbors = grid.point(gridPointIndex).neighbor;
1432 std::vector<double> lambdaMarginalDistribution =
1433 calculateFELambdaMarginalDistribution(grid, neighbors, probWeightNeighbor);
1435 awh_dvec coordValueAlongLambda = { coordState_.coordValue()[0], coordState_.coordValue()[1],
1436 coordState_.coordValue()[2], coordState_.coordValue()[3] };
1437 for (size_t i = 0; i < neighbors.size(); i++)
1439 const int neighbor = neighbors[i];
1441 if (pointsAlongLambdaAxis(grid, gridPointIndex, neighbor))
1443 const double neighborLambda = grid.point(neighbor).coordValue[lambdaAxisIndex.value()];
1444 if (neighbor == gridPointIndex)
1446 bias = convolvedBias;
1450 coordValueAlongLambda[lambdaAxisIndex.value()] = neighborLambda;
1451 bias = calcConvolvedBias(dimParams, grid, coordValueAlongLambda);
1454 const double probWeight = lambdaMarginalDistribution[neighborLambda];
1455 const double weightedBias = bias - std::log(std::max(probWeight, GMX_DOUBLE_MIN)); // avoid log(0)
1457 if (neighbor == gridPointIndex && grid.covers(coordState_.coordValue()))
1459 points_[neighbor].samplePmf(weightedBias);
1463 points_[neighbor].updatePmfUnvisited(weightedBias);
1470 /* Only save coordinate data that is in range (the given index is always
1471 * in range even if the coordinate value is not).
1473 if (grid.covers(coordState_.coordValue()))
1475 /* Save PMF sum and keep a histogram of the sampled coordinate values */
1476 points_[gridPointIndex].samplePmf(convolvedBias);
1480 /* Save probability weights for the update */
1481 sampleProbabilityWeights(grid, probWeightNeighbor);
1484 void BiasState::initHistoryFromState(AwhBiasHistory* biasHistory) const
1486 biasHistory->pointState.resize(points_.size());
1489 void BiasState::updateHistory(AwhBiasHistory* biasHistory, const BiasGrid& grid) const
1491 GMX_RELEASE_ASSERT(biasHistory->pointState.size() == points_.size(),
1492 "The AWH history setup does not match the AWH state.");
1494 AwhBiasStateHistory* stateHistory = &biasHistory->state;
1495 stateHistory->umbrellaGridpoint = coordState_.umbrellaGridpoint();
1497 for (size_t m = 0; m < biasHistory->pointState.size(); m++)
1499 AwhPointStateHistory* psh = &biasHistory->pointState[m];
1501 points_[m].storeState(psh);
1503 psh->weightsum_covering = weightSumCovering_[m];
1506 histogramSize_.storeState(stateHistory);
1508 stateHistory->origin_index_updatelist = multiDimGridIndexToLinear(grid, originUpdatelist_);
1509 stateHistory->end_index_updatelist = multiDimGridIndexToLinear(grid, endUpdatelist_);
1512 void BiasState::restoreFromHistory(const AwhBiasHistory& biasHistory, const BiasGrid& grid)
1514 const AwhBiasStateHistory& stateHistory = biasHistory.state;
1516 coordState_.restoreFromHistory(stateHistory);
1518 if (biasHistory.pointState.size() != points_.size())
1521 InvalidInputError("Bias grid size in checkpoint and simulation do not match. "
1522 "Likely you provided a checkpoint from a different simulation."));
1524 for (size_t m = 0; m < points_.size(); m++)
1526 points_[m].setFromHistory(biasHistory.pointState[m]);
1529 for (size_t m = 0; m < weightSumCovering_.size(); m++)
1531 weightSumCovering_[m] = biasHistory.pointState[m].weightsum_covering;
1534 histogramSize_.restoreFromHistory(stateHistory);
1536 linearGridindexToMultiDim(grid, stateHistory.origin_index_updatelist, originUpdatelist_);
1537 linearGridindexToMultiDim(grid, stateHistory.end_index_updatelist, endUpdatelist_);
1540 void BiasState::broadcast(const t_commrec* commRecord)
1542 gmx_bcast(sizeof(coordState_), &coordState_, commRecord->mpi_comm_mygroup);
1544 gmx_bcast(points_.size() * sizeof(PointState), points_.data(), commRecord->mpi_comm_mygroup);
1546 gmx_bcast(weightSumCovering_.size() * sizeof(double), weightSumCovering_.data(),
1547 commRecord->mpi_comm_mygroup);
1549 gmx_bcast(sizeof(histogramSize_), &histogramSize_, commRecord->mpi_comm_mygroup);
1552 void BiasState::setFreeEnergyToConvolvedPmf(const std::vector<DimParams>& dimParams, const BiasGrid& grid)
1554 std::vector<float> convolvedPmf;
1556 calcConvolvedPmf(dimParams, grid, &convolvedPmf);
1558 for (size_t m = 0; m < points_.size(); m++)
1560 points_[m].setFreeEnergy(convolvedPmf[m]);
1565 * Count trailing data rows containing only zeros.
1567 * \param[in] data 2D data array.
1568 * \param[in] numRows Number of rows in array.
1569 * \param[in] numColumns Number of cols in array.
1570 * \returns the number of trailing zero rows.
1572 static int countTrailingZeroRows(const double* const* data, int numRows, int numColumns)
1574 int numZeroRows = 0;
1575 for (int m = numRows - 1; m >= 0; m--)
1577 bool rowIsZero = true;
1578 for (int d = 0; d < numColumns; d++)
1580 if (data[d][m] != 0)
1589 /* At a row with non-zero data */
1594 /* Still at a zero data row, keep checking rows higher up. */
1603 * Initializes the PMF and target with data read from an input table.
1605 * \param[in] dimParams The dimension parameters.
1606 * \param[in] grid The grid.
1607 * \param[in] filename The filename to read PMF and target from.
1608 * \param[in] numBias Number of biases.
1609 * \param[in] biasIndex The index of the bias.
1610 * \param[in,out] pointState The state of the points in this bias.
1612 static void readUserPmfAndTargetDistribution(const std::vector<DimParams>& dimParams,
1613 const BiasGrid& grid,
1614 const std::string& filename,
1617 std::vector<PointState>* pointState)
1619 /* Read the PMF and target distribution.
1620 From the PMF, the convolved PMF, or the reference value free energy, can be calculated
1621 base on the force constant. The free energy and target together determine the bias.
1623 std::string filenameModified(filename);
1626 size_t n = filenameModified.rfind('.');
1627 GMX_RELEASE_ASSERT(n != std::string::npos,
1628 "The filename should contain an extension starting with .");
1629 filenameModified.insert(n, formatString("%d", biasIndex));
1632 std::string correctFormatMessage = formatString(
1633 "%s is expected in the following format. "
1634 "The first ndim column(s) should contain the coordinate values for each point, "
1635 "each column containing values of one dimension (in ascending order). "
1636 "For a multidimensional coordinate, points should be listed "
1637 "in the order obtained by traversing lower dimensions first. "
1638 "E.g. for two-dimensional grid of size nxn: "
1639 "(1, 1), (1, 2),..., (1, n), (2, 1), (2, 2), ..., , (n, n - 1), (n, n). "
1640 "Column ndim + 1 should contain the PMF value for each coordinate value. "
1641 "The target distribution values should be in column ndim + 2 or column ndim + 5. "
1642 "Make sure the input file ends with a new line but has no trailing new lines.",
1644 gmx::TextLineWrapper wrapper;
1645 wrapper.settings().setLineLength(c_linewidth);
1646 correctFormatMessage = wrapper.wrapToString(correctFormatMessage);
1650 int numRows = read_xvg(filenameModified.c_str(), &data, &numColumns);
1652 /* Check basic data properties here. BiasGrid takes care of more complicated things. */
1656 std::string mesg = gmx::formatString("%s is empty!.\n\n%s", filename.c_str(),
1657 correctFormatMessage.c_str());
1658 GMX_THROW(InvalidInputError(mesg));
1661 /* Less than 2 points is not useful for PMF or target. */
1664 std::string mesg = gmx::formatString(
1665 "%s contains too few data points (%d)."
1666 "The minimum number of points is 2.",
1667 filename.c_str(), numRows);
1668 GMX_THROW(InvalidInputError(mesg));
1671 /* Make sure there are enough columns of data.
1673 Two formats are allowed. Either with columns {coords, PMF, target} or
1674 {coords, PMF, x, y, z, target, ...}. The latter format is allowed since that
1675 is how AWH output is written (x, y, z being other AWH variables). For this format,
1676 trailing columns are ignored.
1678 int columnIndexTarget;
1679 int numColumnsMin = dimParams.size() + 2;
1680 int columnIndexPmf = dimParams.size();
1681 if (numColumns == numColumnsMin)
1683 columnIndexTarget = columnIndexPmf + 1;
1687 columnIndexTarget = columnIndexPmf + 4;
1690 if (numColumns < numColumnsMin)
1692 std::string mesg = gmx::formatString(
1693 "The number of columns in %s should be at least %d."
1695 filename.c_str(), numColumnsMin, correctFormatMessage.c_str());
1696 GMX_THROW(InvalidInputError(mesg));
1699 /* read_xvg can give trailing zero data rows for trailing new lines in the input. We allow 1 zero row,
1700 since this could be real data. But multiple trailing zero rows cannot correspond to valid data. */
1701 int numZeroRows = countTrailingZeroRows(data, numRows, numColumns);
1702 if (numZeroRows > 1)
1704 std::string mesg = gmx::formatString(
1705 "Found %d trailing zero data rows in %s. Please remove trailing empty lines and "
1707 numZeroRows, filename.c_str());
1708 GMX_THROW(InvalidInputError(mesg));
1711 /* Convert from user units to internal units before sending the data of to grid. */
1712 for (size_t d = 0; d < dimParams.size(); d++)
1714 double scalingFactor = dimParams[d].scaleUserInputToInternal(1);
1715 if (scalingFactor == 1)
1719 for (size_t m = 0; m < pointState->size(); m++)
1721 data[d][m] *= scalingFactor;
1725 /* Get a data point for each AWH grid point so that they all get data. */
1726 std::vector<int> gridIndexToDataIndex(grid.numPoints());
1727 mapGridToDataGrid(&gridIndexToDataIndex, data, numRows, filename, grid, correctFormatMessage);
1729 /* Extract the data for each grid point.
1730 * We check if the target distribution is zero for all points.
1732 bool targetDistributionIsZero = true;
1733 for (size_t m = 0; m < pointState->size(); m++)
1735 (*pointState)[m].setLogPmfSum(-data[columnIndexPmf][gridIndexToDataIndex[m]]);
1736 double target = data[columnIndexTarget][gridIndexToDataIndex[m]];
1738 /* Check if the values are allowed. */
1741 std::string mesg = gmx::formatString(
1742 "Target distribution weight at point %zu (%g) in %s is negative.", m, target,
1744 GMX_THROW(InvalidInputError(mesg));
1748 targetDistributionIsZero = false;
1750 (*pointState)[m].setTargetConstantWeight(target);
1753 if (targetDistributionIsZero)
1756 gmx::formatString("The target weights given in column %d in %s are all 0",
1757 columnIndexTarget, filename.c_str());
1758 GMX_THROW(InvalidInputError(mesg));
1761 /* Free the arrays. */
1762 for (int m = 0; m < numColumns; m++)
1769 void BiasState::normalizePmf(int numSharingSims)
1771 /* The normalization of the PMF estimate matters because it determines how big effect the next sample has.
1772 Approximately (for large enough force constant) we should have:
1773 sum_x(exp(-pmf(x)) = nsamples*sum_xref(exp(-f(xref)).
1776 /* Calculate the normalization factor, i.e. divide by the pmf sum, multiply by the number of samples and the f sum */
1777 double expSumPmf = 0;
1779 for (const PointState& pointState : points_)
1781 if (pointState.inTargetRegion())
1783 expSumPmf += std::exp(pointState.logPmfSum());
1784 expSumF += std::exp(-pointState.freeEnergy());
1787 double numSamples = histogramSize_.histogramSize() / numSharingSims;
1790 double logRenorm = std::log(numSamples * expSumF / expSumPmf);
1791 for (PointState& pointState : points_)
1793 if (pointState.inTargetRegion())
1795 pointState.setLogPmfSum(pointState.logPmfSum() + logRenorm);
1800 void BiasState::initGridPointState(const AwhBiasParams& awhBiasParams,
1801 const std::vector<DimParams>& dimParams,
1802 const BiasGrid& grid,
1803 const BiasParams& params,
1804 const std::string& filename,
1807 /* Modify PMF, free energy and the constant target distribution factor
1808 * to user input values if there is data given.
1810 if (awhBiasParams.bUserData)
1812 readUserPmfAndTargetDistribution(dimParams, grid, filename, numBias, params.biasIndex, &points_);
1813 setFreeEnergyToConvolvedPmf(dimParams, grid);
1816 /* The local Boltzmann distribution is special because the target distribution is updated as a function of the reference weighthistogram. */
1817 GMX_RELEASE_ASSERT(params.eTarget != eawhtargetLOCALBOLTZMANN || points_[0].weightSumRef() != 0,
1818 "AWH reference weight histogram not initialized properly with local "
1819 "Boltzmann target distribution.");
1821 updateTargetDistribution(points_, params);
1823 for (PointState& pointState : points_)
1825 if (pointState.inTargetRegion())
1827 pointState.updateBias();
1831 /* Note that for zero target this is a value that represents -infinity but should not be used for biasing. */
1832 pointState.setTargetToZero();
1836 /* Set the initial reference weighthistogram. */
1837 const double histogramSize = histogramSize_.histogramSize();
1838 for (auto& pointState : points_)
1840 pointState.setInitialReferenceWeightHistogram(histogramSize);
1843 /* Make sure the pmf is normalized consistently with the histogram size.
1844 Note: the target distribution and free energy need to be set here. */
1845 normalizePmf(params.numSharedUpdate);
1848 BiasState::BiasState(const AwhBiasParams& awhBiasParams,
1849 double histogramSizeInitial,
1850 const std::vector<DimParams>& dimParams,
1851 const BiasGrid& grid) :
1852 coordState_(awhBiasParams, dimParams, grid),
1853 points_(grid.numPoints()),
1854 weightSumCovering_(grid.numPoints()),
1855 histogramSize_(awhBiasParams, histogramSizeInitial)
1857 /* The minimum and maximum multidimensional point indices that are affected by the next update */
1858 for (size_t d = 0; d < dimParams.size(); d++)
1860 int index = grid.point(coordState_.gridpointIndex()).index[d];
1861 originUpdatelist_[d] = index;
1862 endUpdatelist_[d] = index;